This research introduced the optimized Deep Stacked Autoencoder (DSA) for performing Intrusion Detection (ID) in the IoT. Firstly, IoT simulation is carried out and then, the information is routed by using the Chronological War Strategy Optimization (CWSO). Here, the CWSO is newly designed by incorporating the chronological concept with the WSO. After the routing, the ID is completed at the Base station (BS) by executing the following steps. Initially, data is obtained from a database, after that, feature normalization is done using min-max normalization. Meanwhile, Canberra distance is applied to execute the feature selection process. Finally, ID is performed using DSA, which is trained using the Competitive Swarm Henry War Strategy Optimization algorithm (CSHWO). The experimental result confirms that the invented scheme accomplished the superior outcome by the energy, f-score, precision, and recall values of 0.379, 0.913, 0.918 and 0.912, respectively.
{"title":"Hybrid optimization based deep stacked autoencoder for routing and intrusion detection","authors":"M. Boopathi","doi":"10.3233/web-230109a","DOIUrl":"https://doi.org/10.3233/web-230109a","url":null,"abstract":"This research introduced the optimized Deep Stacked Autoencoder (DSA) for performing Intrusion Detection (ID) in the IoT. Firstly, IoT simulation is carried out and then, the information is routed by using the Chronological War Strategy Optimization (CWSO). Here, the CWSO is newly designed by incorporating the chronological concept with the WSO. After the routing, the ID is completed at the Base station (BS) by executing the following steps. Initially, data is obtained from a database, after that, feature normalization is done using min-max normalization. Meanwhile, Canberra distance is applied to execute the feature selection process. Finally, ID is performed using DSA, which is trained using the Competitive Swarm Henry War Strategy Optimization algorithm (CSHWO). The experimental result confirms that the invented scheme accomplished the superior outcome by the energy, f-score, precision, and recall values of 0.379, 0.913, 0.918 and 0.912, respectively.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805714","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}
Malicious traffic segregation and attack detection caused major financial loss and became one of the most serious security hazards. Moreover, cyber security attack is the major issue, which impacts network security. The network attack methods are constantly being upgraded by the technology development and it remains a major issue for detection and protection against network attacks. For this, it is required to present an effective strategy for detecting and maintaining network security. The work provides timely and accurate congestion attack detection and identification. In the Internet of Things (IoT) cloud system malicious traffic segregation and attack detection based on a hybrid optimization-enabled deep learning (DL) network is developed in this research. At first, the input log files are gathered from the simulation of IoT sensors and the superior route is selected by the proposed Fractional Hunger Jellyfish Search Optimization (FHGJO) algorithm. The FHGJO is the integration of Hunger Game Jelly Fish Optimization (HGJO) and Fractional Calculus (FC). Furthermore, the HGJO is the combination of Hunger Game Search Optimization (HGS) with Jellyfish Optimization (JSO). Then, the segregation is done based on the fitness measures and for preprocessing; the input data is fed using quantile normalization. The feature selection process is employed using the weighted Euclidian distance (WED). With the SpinalNet, the malicious segregation is categorized as malicious and non-malicious and the proposed FHJGO is used to tune the SpinalNet. Furthermore, the proposed FHGJO-trained Deep Quantum Neural Network (DQNN) is utilized to detect the attack and classifies it into a Denial-of-Service (DOS) attack, Distributed Denial of Service (DDoS) attack, and buffer overflow attack. Moreover, the proposed model is evaluated using the NSL-KDD dataset and BoT-IoT dataset. The proposed method ensures network security with 0.931 accuracy, 0.923 sensitivity, and 0.936 specificity.
{"title":"Fractional hunger jellyfish search optimization based deep quantum neural network for malicious traffic segregation and attack detection","authors":"Sunil Sonawane, Reshma R. Gulwani, Pooja Sharma","doi":"10.3233/web-230214","DOIUrl":"https://doi.org/10.3233/web-230214","url":null,"abstract":"Malicious traffic segregation and attack detection caused major financial loss and became one of the most serious security hazards. Moreover, cyber security attack is the major issue, which impacts network security. The network attack methods are constantly being upgraded by the technology development and it remains a major issue for detection and protection against network attacks. For this, it is required to present an effective strategy for detecting and maintaining network security. The work provides timely and accurate congestion attack detection and identification. In the Internet of Things (IoT) cloud system malicious traffic segregation and attack detection based on a hybrid optimization-enabled deep learning (DL) network is developed in this research. At first, the input log files are gathered from the simulation of IoT sensors and the superior route is selected by the proposed Fractional Hunger Jellyfish Search Optimization (FHGJO) algorithm. The FHGJO is the integration of Hunger Game Jelly Fish Optimization (HGJO) and Fractional Calculus (FC). Furthermore, the HGJO is the combination of Hunger Game Search Optimization (HGS) with Jellyfish Optimization (JSO). Then, the segregation is done based on the fitness measures and for preprocessing; the input data is fed using quantile normalization. The feature selection process is employed using the weighted Euclidian distance (WED). With the SpinalNet, the malicious segregation is categorized as malicious and non-malicious and the proposed FHJGO is used to tune the SpinalNet. Furthermore, the proposed FHGJO-trained Deep Quantum Neural Network (DQNN) is utilized to detect the attack and classifies it into a Denial-of-Service (DOS) attack, Distributed Denial of Service (DDoS) attack, and buffer overflow attack. Moreover, the proposed model is evaluated using the NSL-KDD dataset and BoT-IoT dataset. The proposed method ensures network security with 0.931 accuracy, 0.923 sensitivity, and 0.936 specificity.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662561","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}
The integrated system has generated numerous features for the users, like as identifying heart disease by its symptoms, forwarding the information to the doctors regarding the phase of the probability of disease as well as aiding to fix it. When an emergency situation exists, the system forwards the emergency alert to the respective doctor. Moreover, the automatic system is needed to diagnose heart disease but, the larger data is not sufficient to train the model. Thus, the Internet of Things (IoT) is employed to manage the huge amount of data. Therefore, a novel prediction of heart diseases is implemented with the aid of IoT-based deep learning approaches. Here, the collected data is collected from the three standard databases and then perform preprocessed over the gathered data. Here, the IoT assisted deep learning model is performed to predict heart related diseases accurately. Further, the acquired features of heart diseases are selected using the developed Hybrid Chameleon Electric Fish Swarm Optimization (HCEFSO) via Chameleon Swarm Algorithm (CSA) and Electric Fish Optimization (EFO). Then, the optimally selected features are fed to the training process, where the Trans-Bi-directional Long Short-Term Memory with Gated Recurrent Unit (Trans-Bi-LSTM-GRU) is adopted for predicting heart diseases. Here, the weights are updated with the developed HCEFSO while validating the training phase. The trained Trans-Bi-LSTM-GRU network is used in the testing phase for predicting heart diseases.
{"title":"Efficient IoT-based heart disease prediction framework with Weight Updated Trans-Bidirectional Long Short Term Memory-Gated Recurrent Unit","authors":"K. Sasirekha, D. Asha, P. Sivaganga, R. Harini","doi":"10.3233/web-230063","DOIUrl":"https://doi.org/10.3233/web-230063","url":null,"abstract":"The integrated system has generated numerous features for the users, like as identifying heart disease by its symptoms, forwarding the information to the doctors regarding the phase of the probability of disease as well as aiding to fix it. When an emergency situation exists, the system forwards the emergency alert to the respective doctor. Moreover, the automatic system is needed to diagnose heart disease but, the larger data is not sufficient to train the model. Thus, the Internet of Things (IoT) is employed to manage the huge amount of data. Therefore, a novel prediction of heart diseases is implemented with the aid of IoT-based deep learning approaches. Here, the collected data is collected from the three standard databases and then perform preprocessed over the gathered data. Here, the IoT assisted deep learning model is performed to predict heart related diseases accurately. Further, the acquired features of heart diseases are selected using the developed Hybrid Chameleon Electric Fish Swarm Optimization (HCEFSO) via Chameleon Swarm Algorithm (CSA) and Electric Fish Optimization (EFO). Then, the optimally selected features are fed to the training process, where the Trans-Bi-directional Long Short-Term Memory with Gated Recurrent Unit (Trans-Bi-LSTM-GRU) is adopted for predicting heart diseases. Here, the weights are updated with the developed HCEFSO while validating the training phase. The trained Trans-Bi-LSTM-GRU network is used in the testing phase for predicting heart diseases.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970463","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}
The recent development of communication technologies made it possible for people to share opinions on various social media platforms. The opinion of the people is converted into small-sized textual data. Aspect Based Sentiment Analysis (ABSA) is a process used by businesses and other organizations to assess these textual data in order to comprehend people’s opinions about the services or products offered by them. The majority of earlier Sentiment Analysis (SA) research uses lexicons, word frequencies, or black box techniques to obtain the sentiment in the text. It should be highlighted that these methods disregard the relationships and interdependence between words in terms of semantics. Hence, an efficient ABSA framework to determine the sentiment from the textual reviews of the customers is developed in this work. Initially, the raw text review data is collected from the standard benchmark datasets. The gathered text reviews undergo text pre-processing to neglect the unwanted words and characters from the input text document. The pre-processed data is directly provided to the feature extraction phase in which the seq2seq network and transformer network are employed. Further, the optimal features from the two resultant features are chosen by utilizing the proposed Modified Bird Swarm-Ladybug Beetle Optimization (MBS-LBO). After obtaining optimal features, these features are fused together and given to the final detection model. Consequently, the Optimized Cascaded Long Short Term Memory (OCas-LSTM) is proposed for predicting the sentiments from the given review by the users. Here, the parameters are tuned optimally by the MBS-LBO algorithm, and also it is utilized for enhancing the performance rate. The experimental evaluation is made to reveal the excellent performance of the developed SA model by contrasting it with conventional models.
近年来通信技术的发展使人们可以在各种社交媒体平台上分享意见。人们的意见被转换成小尺寸的文本数据。基于方面的情感分析（ABSA）是企业和其他组织用来评估这些文本数据的过程，以了解人们对其提供的服务或产品的看法。早期的情感分析（SA）研究大多使用词典、词频或黑盒技术来获取文本中的情感。需要强调的是，这些方法忽略了词与词之间的语义关系和相互依存性。因此，本研究开发了一种高效的 ABSA 框架，用于从客户的文本评论中确定情感。首先，从标准基准数据集中收集原始文本评论数据。收集到的文本评论会经过文本预处理，以忽略输入文本文档中不需要的单词和字符。预处理后的数据直接提供给特征提取阶段，在这一阶段中使用了 seq2seq 网络和转换器网络。然后，利用建议的修正鸟群-瓢虫优化法（MBS-LBO）从两个结果特征中选择最佳特征。获得最佳特征后，将这些特征融合在一起，并赋予最终检测模型。因此，我们提出了优化级联长短期记忆（OCas-LSTM）来预测用户给定评论中的情绪。在此，通过 MBS-LBO 算法对参数进行优化调整，并利用它来提高性能。通过与传统模型的对比，实验评估揭示了所开发的 SA 模型的卓越性能。
{"title":"Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework","authors":"Mekala Ramasamy, Mohanraj Elangovan","doi":"10.3233/web-230096","DOIUrl":"https://doi.org/10.3233/web-230096","url":null,"abstract":"The recent development of communication technologies made it possible for people to share opinions on various social media platforms. The opinion of the people is converted into small-sized textual data. Aspect Based Sentiment Analysis (ABSA) is a process used by businesses and other organizations to assess these textual data in order to comprehend people’s opinions about the services or products offered by them. The majority of earlier Sentiment Analysis (SA) research uses lexicons, word frequencies, or black box techniques to obtain the sentiment in the text. It should be highlighted that these methods disregard the relationships and interdependence between words in terms of semantics. Hence, an efficient ABSA framework to determine the sentiment from the textual reviews of the customers is developed in this work. Initially, the raw text review data is collected from the standard benchmark datasets. The gathered text reviews undergo text pre-processing to neglect the unwanted words and characters from the input text document. The pre-processed data is directly provided to the feature extraction phase in which the seq2seq network and transformer network are employed. Further, the optimal features from the two resultant features are chosen by utilizing the proposed Modified Bird Swarm-Ladybug Beetle Optimization (MBS-LBO). After obtaining optimal features, these features are fused together and given to the final detection model. Consequently, the Optimized Cascaded Long Short Term Memory (OCas-LSTM) is proposed for predicting the sentiments from the given review by the users. Here, the parameters are tuned optimally by the MBS-LBO algorithm, and also it is utilized for enhancing the performance rate. The experimental evaluation is made to reveal the excellent performance of the developed SA model by contrasting it with conventional models.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973450","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}
Creative ideas are introduced to the market by business owners, and inventiveness creates new demands that cause existing markets to be disrupted and new ones to be created, which are then destroyed by even more innovative goods or services. In this rsesearch work, an empirical study is undertaken to gather information about business model innovation and creativity as well as Entrepreneurship development in Greek SMEs. Using the stratum sample size determination formula, a valid sample of 257 people influenced the study. SEM and the F-test were used in the research’s data analysis. The findings of the study demonstrate that entrepreneurship has a significant connection between business model innovation & creativity and digital capabilities. The test results also indicate that digital capabilities have a favorable impact on the business model innovation & creativity. They also found that the creativity and innovation of business models have a favorable impact on entrepreneurs’ business survival. However, the creativity and innovation of business models have no favorable impact on entrepreneurs’ business performance and reputation.
企业主将创意引入市场，创造性产生新的需求，从而扰乱现有市场，创造新的市场，然后被更具创新性的商品或服务所摧毁。在这项研究工作中，我们开展了一项实证研究，以收集有关希腊中小企业商业模式创新和创造力以及企业家精神发展的信息。利用分层抽样规模确定公式，257 人的有效样本对研究产生了影响。研究数据分析采用了 SEM 和 F 检验。研究结果表明，创业在商业模式创新与创造力和数字化能力之间有着重要的联系。检验结果还表明，数字化能力对商业模式创新与创造力具有有利影响。他们还发现，商业模式的创意和创新对创业者的企业生存有有利影响。但是，商业模式的创造性和创新性对企业家的商业绩效和声誉没有有利影响。
{"title":"Business model innovation and creativity impact on entrepreneurship development: An empirical study","authors":"Vazakas Anastasios","doi":"10.3233/web-230006","DOIUrl":"https://doi.org/10.3233/web-230006","url":null,"abstract":"Creative ideas are introduced to the market by business owners, and inventiveness creates new demands that cause existing markets to be disrupted and new ones to be created, which are then destroyed by even more innovative goods or services. In this rsesearch work, an empirical study is undertaken to gather information about business model innovation and creativity as well as Entrepreneurship development in Greek SMEs. Using the stratum sample size determination formula, a valid sample of 257 people influenced the study. SEM and the F-test were used in the research’s data analysis. The findings of the study demonstrate that entrepreneurship has a significant connection between business model innovation & creativity and digital capabilities. The test results also indicate that digital capabilities have a favorable impact on the business model innovation & creativity. They also found that the creativity and innovation of business models have a favorable impact on entrepreneurs’ business survival. However, the creativity and innovation of business models have no favorable impact on entrepreneurs’ business performance and reputation.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973800","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}
Tamara Abdulmunim, Xiaohui Tao, Ji Zhang, Jianming Yong, Xujuan Zhou
Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.
{"title":"Movie recommendation and classification system using block chain","authors":"Tamara Abdulmunim, Xiaohui Tao, Ji Zhang, Jianming Yong, Xujuan Zhou","doi":"10.3233/web-230346","DOIUrl":"https://doi.org/10.3233/web-230346","url":null,"abstract":"Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140990681","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}
Heart disease is a critical issue that affects people, causes serious sickness, and is the main cause of mortality worldwide. Early diagnosis of disease plays a significant role in heart disease prediction and is attained by various automation techniques. The availability of automation techniques initiates the necessity for medical data and the storage of medical data becomes a research problem due to its high sensitivity. The emergence of IoT networks formed a promising solution for data storage through the cloud server and preventing the data from various threats is a challenging problem. A secure heart disease prediction system is developed by the utility of the ESVO-based Swish Bessel CNN classifier (Emperor Spheniscidae Vampire Optimization-based Swish Bessel Convolutional Neural Network), and the important significance of the research depends on the ESVO optimization that helps in gaining a deeper insight of the classifier as well as helps in preventing the threatening of data. The security of the cloud server is enhanced by the EDH-ECC (Entropy Diffie Hellman – Elliptic Curve Cryptography) which promotes the information exchange even in unsecured channels. Similarly, the authentication and authorization of the cloud server are carried out using the EAN-13 and salt-based digital signature that initiates strong credentials and enhance data security. Finally, the heart disease is diagnosed using the ESVO-based Swish Bessel CNN classifier. Assessing the accuracy, sensitivity, specificity, and F1-measure, which provided values of 94.877 %, 95.464 %, 93.293 %, and 95.14 % shows the effectiveness of the research.
{"title":"Secure heart disease prediction model using ESVO-based Swish Bessel CNN classifier","authors":"S. Pawar, Damala Dayakar Rao","doi":"10.3233/web-220118","DOIUrl":"https://doi.org/10.3233/web-220118","url":null,"abstract":"Heart disease is a critical issue that affects people, causes serious sickness, and is the main cause of mortality worldwide. Early diagnosis of disease plays a significant role in heart disease prediction and is attained by various automation techniques. The availability of automation techniques initiates the necessity for medical data and the storage of medical data becomes a research problem due to its high sensitivity. The emergence of IoT networks formed a promising solution for data storage through the cloud server and preventing the data from various threats is a challenging problem. A secure heart disease prediction system is developed by the utility of the ESVO-based Swish Bessel CNN classifier (Emperor Spheniscidae Vampire Optimization-based Swish Bessel Convolutional Neural Network), and the important significance of the research depends on the ESVO optimization that helps in gaining a deeper insight of the classifier as well as helps in preventing the threatening of data. The security of the cloud server is enhanced by the EDH-ECC (Entropy Diffie Hellman – Elliptic Curve Cryptography) which promotes the information exchange even in unsecured channels. Similarly, the authentication and authorization of the cloud server are carried out using the EAN-13 and salt-based digital signature that initiates strong credentials and enhance data security. Finally, the heart disease is diagnosed using the ESVO-based Swish Bessel CNN classifier. Assessing the accuracy, sensitivity, specificity, and F1-measure, which provided values of 94.877 %, 95.464 %, 93.293 %, and 95.14 % shows the effectiveness of the research.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090376","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}
Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.
{"title":"Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization","authors":"Kingston Albert Dhas Y, S. Jerine","doi":"10.3233/web-230272","DOIUrl":"https://doi.org/10.3233/web-230272","url":null,"abstract":"Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963694","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}
Software Defined Network (SDN) facilitates a centralized control management of devices in network, which solves many issues in the old network. However, as the modern era generates a vast amount of data, the controller in an SDN could become overloaded. Numerous investigators have offered their opinions on how to address the issue of controller overloading in order to resolve it. Mostly the traditional models consider two or three parameters to evenly distribute the load in SDN, which is not sufficient for precise load balancing strategy. Hence, an effective load balancing model is in need that considers different parameters. Considering this aspect, this paper presents a new load balancing model in SDN is introduced by following three major phases: (a) work load prediction, (b) optimal load balancing, and (c) switch migration. Initially, work load prediction is done via improved Deep Maxout Network. COA and BWO are conceptually combined in the proposed hybrid optimization technique known as Coati Updated Black Widow (CUBW). Then, the optimal load balancing is done via hybrid optimization named Coati Updated Black Widow (CUBW) Optimization Algorithm. The optimal load balancing is done by considering migration time, migration cost, distance and load balancing parameters like server load, response time and turnaround time. Finally, switch migration is carried out by considering the constraints like migration time, migration cost, and distance. The migration time of the proposed method achieves lower value, which is 27.3%, 40.8%, 24.40%, 41.8%, 42.8%, 42.2%, 40.0%, and 41.6% higher than the previous models like BMO, BES, AOA, TDO, CSO, GLSOM, HDD-PLB, BWO and COA respectively. Finally, the performance of proposed work is validated over the conventional methods in terms of different analysis.
软件定义网络（SDN）有利于集中控制管理网络中的设备，解决了旧网络中的许多问题。然而，由于现代社会产生了大量数据，SDN 中的控制器可能会超载。如何解决控制器过载问题，众多研究者提出了自己的看法。传统模型大多考虑两个或三个参数来平均分配 SDN 中的负载，但这不足以实现精确的负载平衡策略。因此，需要一种考虑不同参数的有效负载平衡模型。考虑到这一点，本文通过以下三个主要阶段介绍了一种新的 SDN 负载平衡模型：（a）工作负载预测；（b）优化负载平衡；（c）交换机迁移。最初，工作负载预测是通过改进的深度 Maxout 网络完成的。COA 和 BWO 在概念上被结合到所提出的混合优化技术中，即 Coati Updated Black Widow (CUBW)。然后，通过名为 Coati Updated Black Widow (CUBW) 优化算法的混合优化技术实现最佳负载平衡。最佳负载平衡是通过考虑迁移时间、迁移成本、距离以及服务器负载、响应时间和周转时间等负载平衡参数来实现的。最后，通过考虑迁移时间、迁移成本和迁移距离等约束条件，进行交换机迁移。与 BMO、BES、AOA、TDO、CSO、GLSOM、HDD-PLB、BWO 和 COA 等先前的模型相比，提议方法的迁移时间达到了较低的值，分别为 27.3%、40.8%、24.40%、41.8%、42.8%、42.2%、40.0% 和 41.6%。最后，从不同的分析角度验证了所提方法优于传统方法的性能。
{"title":"Combined optimization strategy: CUBW for load balancing in software defined network","authors":"Sonam Sharma, Dambarudhar Seth, Manoj Kapil","doi":"10.3233/web-230263","DOIUrl":"https://doi.org/10.3233/web-230263","url":null,"abstract":"Software Defined Network (SDN) facilitates a centralized control management of devices in network, which solves many issues in the old network. However, as the modern era generates a vast amount of data, the controller in an SDN could become overloaded. Numerous investigators have offered their opinions on how to address the issue of controller overloading in order to resolve it. Mostly the traditional models consider two or three parameters to evenly distribute the load in SDN, which is not sufficient for precise load balancing strategy. Hence, an effective load balancing model is in need that considers different parameters. Considering this aspect, this paper presents a new load balancing model in SDN is introduced by following three major phases: (a) work load prediction, (b) optimal load balancing, and (c) switch migration. Initially, work load prediction is done via improved Deep Maxout Network. COA and BWO are conceptually combined in the proposed hybrid optimization technique known as Coati Updated Black Widow (CUBW). Then, the optimal load balancing is done via hybrid optimization named Coati Updated Black Widow (CUBW) Optimization Algorithm. The optimal load balancing is done by considering migration time, migration cost, distance and load balancing parameters like server load, response time and turnaround time. Finally, switch migration is carried out by considering the constraints like migration time, migration cost, and distance. The migration time of the proposed method achieves lower value, which is 27.3%, 40.8%, 24.40%, 41.8%, 42.8%, 42.2%, 40.0%, and 41.6% higher than the previous models like BMO, BES, AOA, TDO, CSO, GLSOM, HDD-PLB, BWO and COA respectively. Finally, the performance of proposed work is validated over the conventional methods in terms of different analysis.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383291","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}
In this competitive world, companies should sustain good relationships with their consumers. CRM (customer relationship management) program can improve the company’s customer satisfaction; to satisfy customer need different processes and technique are established to make the CRM more effective. This research is proposed to determine the relationship between customer loyalty and retention. Also, this research examines the impact of Customer Relationship Management (CRM) on Customer Satisfaction. The target population of this study is customers of the tourism industry in India ( n = 300). Then, regression analysis is carried out in order to discover the link between the variables. This study result shows that service quality and employee behavior of customer need and satisfaction with the effect of different significant of positive relation of both the variables. To make the customer satisfied and to retain their company the CRM should be strong and reliable with the consumers. CRM plays a vital role in increasing market share, high productivity, improving in-depth customer knowledge, and customer satisfaction to increase consumer loyalty to the company to have a clear view of who is their customer, what are the need of their customer and how can satisfy their needs and wants their customers.
{"title":"The Customer Loyalty vs. Customer Retention: The Impact of Customer Relationship Management on Customer Satisfaction","authors":"Ram Kumar Dwivedi, Shailee Lohmor Choudhary, R. Dixit, Zainab Sahiba, Satyaprakash Naik","doi":"10.3233/web-230098","DOIUrl":"https://doi.org/10.3233/web-230098","url":null,"abstract":"In this competitive world, companies should sustain good relationships with their consumers. CRM (customer relationship management) program can improve the company’s customer satisfaction; to satisfy customer need different processes and technique are established to make the CRM more effective. This research is proposed to determine the relationship between customer loyalty and retention. Also, this research examines the impact of Customer Relationship Management (CRM) on Customer Satisfaction. The target population of this study is customers of the tourism industry in India ( n = 300). Then, regression analysis is carried out in order to discover the link between the variables. This study result shows that service quality and employee behavior of customer need and satisfaction with the effect of different significant of positive relation of both the variables. To make the customer satisfied and to retain their company the CRM should be strong and reliable with the consumers. CRM plays a vital role in increasing market share, high productivity, improving in-depth customer knowledge, and customer satisfaction to increase consumer loyalty to the company to have a clear view of who is their customer, what are the need of their customer and how can satisfy their needs and wants their customers.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384936","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}
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