Pub Date : 2022-12-23DOI: 10.17762/ijcnis.v14i3.5569
P. Supraja, Anastasia Salameh, R. VaradarajuH., M. Anand, Unggul Priyadi
Wireless networks, particularly Wireless Mesh Networks (WMNs), are undergoing a significant change as a result of wireless technology advancements and the Internet's rapid expansion. Mesh routers, which have limited mobility and serve as the foundation of WMN, are made up of mesh clients and form the core of WMNs. Mesh clients can with mesh routers to create a client mesh network. Mesh clients can be either stationary or mobile. To properly utilise the network resources of WMNs, a topology must be designed that provides the best client coverage and network connectivity. Finding the ideal answer to the WMN mesh router placement dilemma will resolve this issue MRP-WMN. Since the MRP-WMN is known to be NP-hard, approximation methods are frequently used to solve it. This is another reason we are carrying out this task. Using the Multi-Verse Optimizer algorithm, we provide a quick technique for resolving the MRP-WMN (MVO). It is also proposed to create a new objective function for the MRP-WMN that accounts for the connected client ratio and connected router ratio, two crucial performance indicators. The connected client ratio rises by an average of 16.1%, 12.5%, and 6.9% according to experiment data, when the MVO method is employed to solve the MRP-WMN problem, the path loss falls by 1.3, 0.9, and 0.6 dB when compared to the Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), correspondingly.
{"title":"An Optimal Routing Protocol Using a Multiverse Optimizer Algorithm for Wireless Mesh Network","authors":"P. Supraja, Anastasia Salameh, R. VaradarajuH., M. Anand, Unggul Priyadi","doi":"10.17762/ijcnis.v14i3.5569","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5569","url":null,"abstract":"Wireless networks, particularly Wireless Mesh Networks (WMNs), are undergoing a significant change as a result of wireless technology advancements and the Internet's rapid expansion. Mesh routers, which have limited mobility and serve as the foundation of WMN, are made up of mesh clients and form the core of WMNs. Mesh clients can with mesh routers to create a client mesh network. Mesh clients can be either stationary or mobile. To properly utilise the network resources of WMNs, a topology must be designed that provides the best client coverage and network connectivity. Finding the ideal answer to the WMN mesh router placement dilemma will resolve this issue MRP-WMN. Since the MRP-WMN is known to be NP-hard, approximation methods are frequently used to solve it. This is another reason we are carrying out this task. Using the Multi-Verse Optimizer algorithm, we provide a quick technique for resolving the MRP-WMN (MVO). It is also proposed to create a new objective function for the MRP-WMN that accounts for the connected client ratio and connected router ratio, two crucial performance indicators. The connected client ratio rises by an average of 16.1%, 12.5%, and 6.9% according to experiment data, when the MVO method is employed to solve the MRP-WMN problem, the path loss falls by 1.3, 0.9, and 0.6 dB when compared to the Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), correspondingly.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123390193","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}
Pub Date : 2022-12-23DOI: 10.17762/ijcnis.v14i3.5570
R. M. Batyha, Dr. S. Janani, Dr. S. G. Hymlin Rose, Yanina Gallardo Lolandes, Gerardo Rodríguez Ortíz, S. Navaz
Cognitive Radio (CR) effectively involved in the management of spectrum to perform improved data transmission. CR system actively engaged in the data sensing, learning and dynamic adjustment of radio spectrum parameters with management of unused spectrum in the signal. The spectrum sensing is indispensable in the CR for the management of Primary Users (PUs) and Secondary users (SUs) without any interference. Spectrum sensing is considered as the effective adaptive signal processing model to evaluate the computational complexity model for the signal transmission through Matched filtering, Waveform and Cyclostationary based Energy sensing model. Cyclostationary based model is effective for the energy based sensing model based on unique characteristics with estimation of available channel in the spectrum to extract the received signal in the PU signal. Cyclostationary based model uses the spectrum availability without any periodic property to extract the noise features. This paper developed a Adaptive Cross Score Cyclostationary (ACSCS) to evaluate the spectrum sensing in the CR network. The developed ACSCS model uses the computational complexity with estimation of Signal-to-Interference-and-Noise Ratio (SINR) elimination of cost function. ACSCS model uses the Adaptive Least square Spectral Self-Coherence Restoral (SCORE) with the Adaptive Cross Score (ACS) to overcome the issues in CR. With the derived ACSCS algorithm minimizes the computational complexity based on cost function compared with the ACS algorithm. To minimize the computational complexity pipeline triangular array based Gram-Schmidt Orthogonalization (GSO) structure for the optimization of network. The simulation performance analysis with the ACSCS scheme uses the Rician Multipath Fading channel to estimate detection probability to sense the Receiver Operating Characteristics, detection probability and probability of false alarm using Maximum Likelihood (ML) detector. The ACSC model uses the Square-law combining (SLC) with the moment generation function in the multipath fading channel for the channel sensing with reduced computational complexity. The simulation analysis expressed that ACSC scheme achieves the maximal detection probability value of 1. The analysis expressed that proposed ACSC scheme achieves the improved channel estimation in the 4G communication environment.
认知无线电(CR)有效地参与了频谱管理，以执行改进的数据传输。CR系统积极参与无线电频谱参数的数据感知、学习和动态调整，对信号中未使用的频谱进行管理。频谱感知是CR中必不可少的功能，可以实现对Primary user (Primary user)和Secondary user (Secondary user)的无干扰管理。将频谱感知作为一种有效的自适应信号处理模型，通过匹配滤波、波形和基于环平稳的能量感知模型来评估信号传输的计算复杂度模型。基于周期平稳的能量感知模型是基于频谱中可用信道估计的独特特性提取PU信号中的接收信号的有效方法。基于循环平稳的模型利用频谱可用性来提取噪声特征，而不考虑周期特性。本文提出了一种自适应交叉评分循环平稳(ACSCS)方法来评估CR网络中的频谱感知。所建立的ACSCS模型利用了成本函数的信噪比(SINR)估计的计算复杂度。ACSCS模型采用自适应最小二乘谱自相干恢复(SCORE)和自适应交叉评分(ACS)算法克服了自适应最小二乘谱自相干恢复(SCORE)算法的不足，与ACS算法相比，其基于代价函数的计算量最小化。为了最小化计算复杂度，基于管道三角形阵列的Gram-Schmidt正交化(GSO)结构进行网络优化。对ACSCS方案进行了仿真性能分析，采用了fourier多径衰落信道估计检测概率来感知接收机工作特性，采用最大似然(ML)检测器检测概率和虚警概率。ACSC模型在多径衰落信道中使用平方律组合(SLC)和矩量生成函数进行信道感知，降低了计算复杂度。仿真分析表明，ACSC方案最大检测概率值为1。分析表明，提出的ACSC方案在4G通信环境下实现了改进的信道估计。
{"title":"Cyclostationary Algorithm for Signal Analysis in Cognitive 4G Networks with Spectral Sensing and Resource Allocation","authors":"R. M. Batyha, Dr. S. Janani, Dr. S. G. Hymlin Rose, Yanina Gallardo Lolandes, Gerardo Rodríguez Ortíz, S. Navaz","doi":"10.17762/ijcnis.v14i3.5570","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5570","url":null,"abstract":"Cognitive Radio (CR) effectively involved in the management of spectrum to perform improved data transmission. CR system actively engaged in the data sensing, learning and dynamic adjustment of radio spectrum parameters with management of unused spectrum in the signal. The spectrum sensing is indispensable in the CR for the management of Primary Users (PUs) and Secondary users (SUs) without any interference. Spectrum sensing is considered as the effective adaptive signal processing model to evaluate the computational complexity model for the signal transmission through Matched filtering, Waveform and Cyclostationary based Energy sensing model. Cyclostationary based model is effective for the energy based sensing model based on unique characteristics with estimation of available channel in the spectrum to extract the received signal in the PU signal. Cyclostationary based model uses the spectrum availability without any periodic property to extract the noise features. This paper developed a Adaptive Cross Score Cyclostationary (ACSCS) to evaluate the spectrum sensing in the CR network. The developed ACSCS model uses the computational complexity with estimation of Signal-to-Interference-and-Noise Ratio (SINR) elimination of cost function. ACSCS model uses the Adaptive Least square Spectral Self-Coherence Restoral (SCORE) with the Adaptive Cross Score (ACS) to overcome the issues in CR. With the derived ACSCS algorithm minimizes the computational complexity based on cost function compared with the ACS algorithm. To minimize the computational complexity pipeline triangular array based Gram-Schmidt Orthogonalization (GSO) structure for the optimization of network. The simulation performance analysis with the ACSCS scheme uses the Rician Multipath Fading channel to estimate detection probability to sense the Receiver Operating Characteristics, detection probability and probability of false alarm using Maximum Likelihood (ML) detector. The ACSC model uses the Square-law combining (SLC) with the moment generation function in the multipath fading channel for the channel sensing with reduced computational complexity. The simulation analysis expressed that ACSC scheme achieves the maximal detection probability value of 1. The analysis expressed that proposed ACSC scheme achieves the improved channel estimation in the 4G communication environment.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114864451","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}
Pub Date : 2022-12-23DOI: 10.17762/ijcnis.v14i3.5577
K. Priyadarsini, S. Chandana, Severo Simón Calderón Samaniego, M. G. Chaudhary, V. Vekariya, A. Chaturvedi
With the increase in number of devices enabled the Internet of Things (IoT) communication with the centralized cloud computing model. With the implementation of the cloud computing model leads to increased Quality of Service (QoS). The cloud computing model provides the edge computing technologies for the real-time application to achieve reliability and security. Edge computing is considered the extension of the cloud computing technology involved in transfer of the sensitive information in the cloud edge to increase the network security. The real-time data transmission realizes the interaction with the high frequency to derive improved network security. However, with edge computing server security is considered as sensitive privacy information maintenance. The information generated from the IoT devices are separated based on stored edge servers based on the service location. Edge computing data is separated based in edge servers for the guaranteed data integrity for the data loss and storage. Blockchain technologies are subjected to different security problem for the data integrity through integrated blockchain technologies. This paper developed a Voted Blockchain Elliptical Curve Cryptography (VBECC) model for the millimetre wave application. The examination of the blockchain model is evaluated based on the edge computing architecture. The VBECC model develop an architectural model based Blockchain technology with the voting scheme for the millimetre application. The estimated voting scheme computes the edge computing technologies for the estimation of features through ECC model. The VBECC model computes the security model for the data transmission in the edge computing-based millimetre application. The experimental analysis stated that VBECC model uses the data security model ~8% increased performance than the conventional technique.
{"title":"Intelligent Mobile Edge Computing Integrated with Blockchain Security Analysis for Millimetre-Wave Communication","authors":"K. Priyadarsini, S. Chandana, Severo Simón Calderón Samaniego, M. G. Chaudhary, V. Vekariya, A. Chaturvedi","doi":"10.17762/ijcnis.v14i3.5577","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5577","url":null,"abstract":" With the increase in number of devices enabled the Internet of Things (IoT) communication with the centralized cloud computing model. With the implementation of the cloud computing model leads to increased Quality of Service (QoS). The cloud computing model provides the edge computing technologies for the real-time application to achieve reliability and security. Edge computing is considered the extension of the cloud computing technology involved in transfer of the sensitive information in the cloud edge to increase the network security. The real-time data transmission realizes the interaction with the high frequency to derive improved network security. However, with edge computing server security is considered as sensitive privacy information maintenance. The information generated from the IoT devices are separated based on stored edge servers based on the service location. Edge computing data is separated based in edge servers for the guaranteed data integrity for the data loss and storage. Blockchain technologies are subjected to different security problem for the data integrity through integrated blockchain technologies. This paper developed a Voted Blockchain Elliptical Curve Cryptography (VBECC) model for the millimetre wave application. The examination of the blockchain model is evaluated based on the edge computing architecture. The VBECC model develop an architectural model based Blockchain technology with the voting scheme for the millimetre application. The estimated voting scheme computes the edge computing technologies for the estimation of features through ECC model. The VBECC model computes the security model for the data transmission in the edge computing-based millimetre application. The experimental analysis stated that VBECC model uses the data security model ~8% increased performance than the conventional technique.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121145317","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}
Pub Date : 2022-12-23DOI: 10.17762/ijcnis.v14i3.5576
S. Mansouri, S. Chabchoub
Emotion recognition is the automatic detection of a person’s emotional state through his or her non-physiological or physiological signals. The EEG-related technique was an effectual system, which is typically employed for recognizing feelings in real time. Artificial Intelligence (AI) can be a developing research field which had rapid growth particularly to constitute a bridge between technology and its implementation in solving real-time issues particularly those relevant to the healthcare domain. This study develops a new deep learning-based emotion detection based on EEG signal processing, named DLED-EEGSP technique. The presented DLED-EEGSP technique identifies the distinct kinds of emotions based on the sensors and EEG signals. To perform this, the presented DLED-EEGSP technique exploits multi-head attention based long short-term memory (MHA-LSTM) method for emotion recognition. The MHALSTM model recognizes the emotion states based on the higher order cross feature samples. The experimental result analysis of the DLED-EEGSP technique is investigated on a series of data. A wide-ranging simulation results reported the supremacy of the DLED-EEGSP technique over other existing models.
{"title":"Emotion Detection Based on EEG Signal Processing by Body Sensor 5G Networks Using Deep Learning Architectures","authors":"S. Mansouri, S. Chabchoub","doi":"10.17762/ijcnis.v14i3.5576","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5576","url":null,"abstract":"Emotion recognition is the automatic detection of a person’s emotional state through his or her non-physiological or physiological signals. The EEG-related technique was an effectual system, which is typically employed for recognizing feelings in real time. Artificial Intelligence (AI) can be a developing research field which had rapid growth particularly to constitute a bridge between technology and its implementation in solving real-time issues particularly those relevant to the healthcare domain. This study develops a new deep learning-based emotion detection based on EEG signal processing, named DLED-EEGSP technique. The presented DLED-EEGSP technique identifies the distinct kinds of emotions based on the sensors and EEG signals. To perform this, the presented DLED-EEGSP technique exploits multi-head attention based long short-term memory (MHA-LSTM) method for emotion recognition. The MHALSTM model recognizes the emotion states based on the higher order cross feature samples. The experimental result analysis of the DLED-EEGSP technique is investigated on a series of data. A wide-ranging simulation results reported the supremacy of the DLED-EEGSP technique over other existing models.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128643948","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}
Pub Date : 2022-10-14DOI: 10.17762/ijcnis.v14i2.5456
L. Devi, C. Subbarao, Boppana Swathi Lakshmi, T. Sushma
A multiple slot fractal antenna design has been determined communication efficiency and its multi-function activities. High-speed small communication devices have been required for future smart chip applications, so that researchers have been employed new and creative antenna design. Antennas are key part in communication systems, those are used to improve communication parameters like gain, efficiency, and bandwidth. Consistently, modern antennas design with high bandwidth and gain balancing is very difficult, therefore an adaptive antenna array chip design is required. In this research work a coaxial fed antenna with fractal geometry design has been implemented for Wi-Fi and Radio altimeter application. The fractal geometry has been taken with multiple numbers of slots in the radiating structure for uncertain applications. The coaxial feeding location has been selected based on the good impedance matching condition (50 Ohms). The overall dimension mentioned for antenna are approximately 50X50X1.6 mm on FR4 substrate and performance characteristic analysis is performed with change in substrate material presented in this work. Dual-band resonant frequency is being emitted by the antenna with resonance at 3.1 and 4.3 GHz for FR4 substrate material and change in the resonant bands is obtained with change in substrate. The proposed Antenna is prototyped on Anritsu VNA tool and presented the comparative analysis like VSWR 12%, reflection coefficient 9.4%,3D-Gain 6.2% and surface current 9.3% had been improved.
{"title":"Multiple Slot Fractal Structured Antenna for Wi-Fi and Radio Altimeter for uncertain Applications","authors":"L. Devi, C. Subbarao, Boppana Swathi Lakshmi, T. Sushma","doi":"10.17762/ijcnis.v14i2.5456","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5456","url":null,"abstract":"A multiple slot fractal antenna design has been determined communication efficiency and its multi-function activities. High-speed small communication devices have been required for future smart chip applications, so that researchers have been employed new and creative antenna design. Antennas are key part in communication systems, those are used to improve communication parameters like gain, efficiency, and bandwidth. Consistently, modern antennas design with high bandwidth and gain balancing is very difficult, therefore an adaptive antenna array chip design is required. In this research work a coaxial fed antenna with fractal geometry design has been implemented for Wi-Fi and Radio altimeter application. The fractal geometry has been taken with multiple numbers of slots in the radiating structure for uncertain applications. The coaxial feeding location has been selected based on the good impedance matching condition (50 Ohms). The overall dimension mentioned for antenna are approximately 50X50X1.6 mm on FR4 substrate and performance characteristic analysis is performed with change in substrate material presented in this work. Dual-band resonant frequency is being emitted by the antenna with resonance at 3.1 and 4.3 GHz for FR4 substrate material and change in the resonant bands is obtained with change in substrate. The proposed Antenna is prototyped on Anritsu VNA tool and presented the comparative analysis like VSWR 12%, reflection coefficient 9.4%,3D-Gain 6.2% and surface current 9.3% had been improved.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127239013","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}
Pub Date : 2022-09-30DOI: 10.17762/ijcnis.v14i2.5507
Gopal Arora, Munish Sabharwal, P. Kapila, Divya Paikaray, V. Vekariya, T. Narmadha
For the healthcare framework, automatic recognition of patients’ emotions is considered to be a good facilitator. Feedback about the status of patients and satisfaction levels can be provided automatically to the stakeholders of the healthcare industry. Multimodal sentiment analysis of human is considered as the attractive and hot topic of research in artificial intelligence (AI) and is the much finer classification issue which differs from other classification issues. In cognitive science, as emotional processing procedure has inspired more, the abilities of both binary and multi-classification tasks are enhanced by splitting complex issues to simpler ones which can be handled more easily. This article proposes an automated audio-visual emotional recognition model for a healthcare industry. The model uses Deep Residual Adaptive Neural Network (DeepResANNet) for feature extraction where the scores are computed based on the differences between feature and class values of adjacent instances. Based on the output of feature extraction, positive and negative sub-nets are trained separately by the fusion module thereby improving accuracy. The proposed method is extensively evaluated using eNTERFACE’05, BAUM-2 and MOSI databases by comparing with three standard methods in terms of various parameters. As a result, DeepResANNet method achieves 97.9% of accuracy, 51.5% of RMSE, 42.5% of RAE and 44.9%of MAE in 78.9sec for eNTERFACE’05 dataset. For BAUM-2 dataset, this model achieves 94.5% of accuracy, 46.9% of RMSE, 42.9%of RAE and 30.2% MAE in 78.9 sec. By utilizing MOSI dataset, this model achieves 82.9% of accuracy, 51.2% of RMSE, 40.1% of RAE and 37.6% of MAE in 69.2sec. By analysing all these three databases, eNTERFACE’05 is best in terms of accuracy achieving 97.9%. BAUM-2 is best in terms of error rate as it achieved 30.2 % of MAE and 46.9% of RMSE. Finally MOSI is best in terms of RAE and minimal response time by achieving 40.1% of RAE in 69.2 sec.
{"title":"Deep Residual Adaptive Neural Network Based Feature Extraction for Cognitive Computing with Multimodal Sentiment Sensing and Emotion Recognition Process","authors":"Gopal Arora, Munish Sabharwal, P. Kapila, Divya Paikaray, V. Vekariya, T. Narmadha","doi":"10.17762/ijcnis.v14i2.5507","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5507","url":null,"abstract":"For the healthcare framework, automatic recognition of patients’ emotions is considered to be a good facilitator. Feedback about the status of patients and satisfaction levels can be provided automatically to the stakeholders of the healthcare industry. Multimodal sentiment analysis of human is considered as the attractive and hot topic of research in artificial intelligence (AI) and is the much finer classification issue which differs from other classification issues. In cognitive science, as emotional processing procedure has inspired more, the abilities of both binary and multi-classification tasks are enhanced by splitting complex issues to simpler ones which can be handled more easily. This article proposes an automated audio-visual emotional recognition model for a healthcare industry. The model uses Deep Residual Adaptive Neural Network (DeepResANNet) for feature extraction where the scores are computed based on the differences between feature and class values of adjacent instances. Based on the output of feature extraction, positive and negative sub-nets are trained separately by the fusion module thereby improving accuracy. The proposed method is extensively evaluated using eNTERFACE’05, BAUM-2 and MOSI databases by comparing with three standard methods in terms of various parameters. As a result, DeepResANNet method achieves 97.9% of accuracy, 51.5% of RMSE, 42.5% of RAE and 44.9%of MAE in 78.9sec for eNTERFACE’05 dataset. For BAUM-2 dataset, this model achieves 94.5% of accuracy, 46.9% of RMSE, 42.9%of RAE and 30.2% MAE in 78.9 sec. By utilizing MOSI dataset, this model achieves 82.9% of accuracy, 51.2% of RMSE, 40.1% of RAE and 37.6% of MAE in 69.2sec. By analysing all these three databases, eNTERFACE’05 is best in terms of accuracy achieving 97.9%. BAUM-2 is best in terms of error rate as it achieved 30.2 % of MAE and 46.9% of RMSE. Finally MOSI is best in terms of RAE and minimal response time by achieving 40.1% of RAE in 69.2 sec.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122449801","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}
Pub Date : 2022-09-30DOI: 10.17762/ijcnis.v14i2.5520
Muhammad Yus Firdaus, M. Kamil
Currently private and government agencies use remote sensing images (RSI) for various applications from military applications to agriculture growth. The images can be multispectral, panchromatic, ultra-spectral, or hyperspectral of terra bytes. RSI classification is considered one important application for remote sensing. Climate change detection especially affects numerous aspects of day-to-day lives, for instance, forestry management, weather forecasting, transportation, agriculture, road condition monitoring, and the detection of the natural atmosphere. Conversely, certain research works had a focus on classification of actual weather phenomenon images, generally depending on visual observations from humans. The conventional artificial visual difference between weather phenomena will take more time and error-prone. This paper develops a new reinforcement learning based climate change analysis on satellite multispectral image processing (RLCCA-SMSIP) technique. In order to properly determine climate change, the RLCCA-SMSIP technique employs residual network (ResNet-101) model for feature extraction. Next, deep reinforcement learning (DRL) approach is utilized for climate classification. Finally, parameter selection of the RLCCA-SMSIP technique involves sine cosine algorithm (SCA) for DRL model. For assuring the enhanced outcomes of the presented RLCCA-SMSIP model, comprehensive comparison results are assessed. The obtained values denote the supremacy of the RLCCA-SMSIP model on climate classification.
{"title":"Climate Change Analysis Based on Satellite Multispectral Image Processing in Feature Selection Using Reinforcement Learning","authors":"Muhammad Yus Firdaus, M. Kamil","doi":"10.17762/ijcnis.v14i2.5520","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5520","url":null,"abstract":"Currently private and government agencies use remote sensing images (RSI) for various applications from military applications to agriculture growth. The images can be multispectral, panchromatic, ultra-spectral, or hyperspectral of terra bytes. RSI classification is considered one important application for remote sensing. Climate change detection especially affects numerous aspects of day-to-day lives, for instance, forestry management, weather forecasting, transportation, agriculture, road condition monitoring, and the detection of the natural atmosphere. Conversely, certain research works had a focus on classification of actual weather phenomenon images, generally depending on visual observations from humans. The conventional artificial visual difference between weather phenomena will take more time and error-prone. This paper develops a new reinforcement learning based climate change analysis on satellite multispectral image processing (RLCCA-SMSIP) technique. In order to properly determine climate change, the RLCCA-SMSIP technique employs residual network (ResNet-101) model for feature extraction. Next, deep reinforcement learning (DRL) approach is utilized for climate classification. Finally, parameter selection of the RLCCA-SMSIP technique involves sine cosine algorithm (SCA) for DRL model. For assuring the enhanced outcomes of the presented RLCCA-SMSIP model, comprehensive comparison results are assessed. The obtained values denote the supremacy of the RLCCA-SMSIP model on climate classification.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122221308","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}
Pub Date : 2022-09-30DOI: 10.17762/ijcnis.v14i2.5521
D. H. Reddy, N. Sirisha
In the current modern world, the way of life style is being completely changed due to the emerging technologies which are reflected in treating the patients too. As there is a tremendous growth in population, the existing e-Healthcare methods are not efficient enough to deal with numerous medical data. There is a delay in caring of patient health as communication networks are poor in quality and moreover smart medical resources are lacking and hence severe causes are experienced in the health of patient. However, authentication is considered as a major challenge ensuring that the illegal participants are not permitted to access the medical data present in cloud. To provide security, the authentication factors required are smart card, password and biometrics. Several approaches based on these are authentication factors are presented for e-Health clouds so far. But mostly serious security defects are experienced with these protocols and even the computation and communication overheads are high. Thus, keeping in mind all these challenges, a novel Multifactor Key management-based authentication by Tunnel IPv6 (MKMA- TIPv6) protocol is introduced for e-Health cloud which prevents main attacks like user anonymity, guessing offline password, impersonation, and stealing smart cards. From the analysis, it is proved that this protocol is effective than the existing ones such as Pair Hand (PH), Linear Combination Authentication Protocol (LCAP), Robust Elliptic Curve Cryptography-based Three factor Authentication (RECCTA) in terms storage cost, Encryption time, Decryption time, computation cost, energy consumption and speed. Hence, the proposed MKMA- TIPv6 achieves 35bits of storage cost, 60sec of encryption time, 50sec decryption time, 45sec computational cost, 50% of energy consumption and 80% speed.
{"title":"Multifactor Authentication Key Management System based Security Model Using Effective Handover Tunnel with IPV6","authors":"D. H. Reddy, N. Sirisha","doi":"10.17762/ijcnis.v14i2.5521","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5521","url":null,"abstract":"In the current modern world, the way of life style is being completely changed due to the emerging technologies which are reflected in treating the patients too. As there is a tremendous growth in population, the existing e-Healthcare methods are not efficient enough to deal with numerous medical data. There is a delay in caring of patient health as communication networks are poor in quality and moreover smart medical resources are lacking and hence severe causes are experienced in the health of patient. However, authentication is considered as a major challenge ensuring that the illegal participants are not permitted to access the medical data present in cloud. To provide security, the authentication factors required are smart card, password and biometrics. Several approaches based on these are authentication factors are presented for e-Health clouds so far. But mostly serious security defects are experienced with these protocols and even the computation and communication overheads are high. Thus, keeping in mind all these challenges, a novel Multifactor Key management-based authentication by Tunnel IPv6 (MKMA- TIPv6) protocol is introduced for e-Health cloud which prevents main attacks like user anonymity, guessing offline password, impersonation, and stealing smart cards. From the analysis, it is proved that this protocol is effective than the existing ones such as Pair Hand (PH), Linear Combination Authentication Protocol (LCAP), Robust Elliptic Curve Cryptography-based Three factor Authentication (RECCTA) in terms storage cost, Encryption time, Decryption time, computation cost, energy consumption and speed. Hence, the proposed MKMA- TIPv6 achieves 35bits of storage cost, 60sec of encryption time, 50sec decryption time, 45sec computational cost, 50% of energy consumption and 80% speed.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122475904","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}
Pub Date : 2022-09-30DOI: 10.17762/ijcnis.v14i2.5514
C. F. Pasani, E. Rohaeti
As an efficient distributed renewable energy utilization model, a microgrid is predictable to realize the higher incorporation of the industrial cyber-physical system (CPS) that has gained significant interest in the academia and industry fields. Electric grid is now facing exceptional variations in generation and load as rising number of distributed energy resources (DERs), typically interfaced via power electronics converter, have been positioned, which possess multifaceted technical problems. In the context of electric grid, Blockchain (BC) was primarily developed for peer-to-peer energy trading through cryptocurrency. This paper presents a deep learning based predictive model for automated control analysis (DLBPM-ACS) in BC assisted industrial CPS environment. The presented DLBPM-ACS technique aims to forecast the short-term energy requirement for reducing the delivery cost of electrical energy for consumers. In addition, the presented DLBPM-ACS technique employs BC for effective energy utilization monitoring and trading control. Moreover, the presented DLBPM-ACS technique employs deep belief network (DBN) model for energy prediction process. Furthermore, the artificial ecosystem optimizer (AEO) algorithm is applied for optimal tuning of the hyperparameters related to the DBN approach. A wide range of simulations was conducted and the outcomes demonstrate the better outcomes of the DLBPM-ACS technique.
{"title":"Industrial Cyber Blockchain Physical System for Microgrid in Data Based Predictive Analysis for Automatic Control Analysis","authors":"C. F. Pasani, E. Rohaeti","doi":"10.17762/ijcnis.v14i2.5514","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5514","url":null,"abstract":"As an efficient distributed renewable energy utilization model, a microgrid is predictable to realize the higher incorporation of the industrial cyber-physical system (CPS) that has gained significant interest in the academia and industry fields. Electric grid is now facing exceptional variations in generation and load as rising number of distributed energy resources (DERs), typically interfaced via power electronics converter, have been positioned, which possess multifaceted technical problems. In the context of electric grid, Blockchain (BC) was primarily developed for peer-to-peer energy trading through cryptocurrency. This paper presents a deep learning based predictive model for automated control analysis (DLBPM-ACS) in BC assisted industrial CPS environment. The presented DLBPM-ACS technique aims to forecast the short-term energy requirement for reducing the delivery cost of electrical energy for consumers. In addition, the presented DLBPM-ACS technique employs BC for effective energy utilization monitoring and trading control. Moreover, the presented DLBPM-ACS technique employs deep belief network (DBN) model for energy prediction process. Furthermore, the artificial ecosystem optimizer (AEO) algorithm is applied for optimal tuning of the hyperparameters related to the DBN approach. A wide range of simulations was conducted and the outcomes demonstrate the better outcomes of the DLBPM-ACS technique.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131309315","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}
Pub Date : 2022-09-30DOI: 10.17762/ijcnis.v14i2.5516
Mudassar Husain Naikwadi, K. Patil
Spectrum Inference in contrast to Spectrum Sensing is an active technique for dynamically inferring radio spectrum state in Cognitive Radio Networks. Efficient spectrum inference demands real world multi-dimensional spectral data with distinct features. Spectrum bands exhibit varying noise floors; an effective band wise noise thresholding guarantees an accurate occupancy data. In this work, we have done an extensive real world spectrum occupancy data measurement in frequency range 0.7 GHz to 3 GHz for tele density wise varying locations at Pune, Solapur and Kalaburagi with time diversity ranging from 2 to 7 days. We have applied maximum noise (Max Noise), m-dB and probability of false alarm (PFA) noise thresholding for spectrum occupancy calculations in all bands and across all locations. Overall occupancy across these locations is 37.89 %, 18.90 % and 13.69 % respectively. We have studied signal to noise ratio (SNR), channel vacancy length durations (CVLD) and service congestion rates (SCR) as characteristic features of measured multi-dimensional spectrum data. The results reveal strong time, spectral and spatial correlations of these features across all locations. These features can be used for a multi-dimensional spectrum inference in cognitive radio based on machine learning.
{"title":"A Multi-dimensional Real World Spectrum Occupancy Data Measurement and Analysis for Spectrum Inference in Cognitive Radio Network","authors":"Mudassar Husain Naikwadi, K. Patil","doi":"10.17762/ijcnis.v14i2.5516","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5516","url":null,"abstract":"Spectrum Inference in contrast to Spectrum Sensing is an active technique for dynamically inferring radio spectrum state in Cognitive Radio Networks. Efficient spectrum inference demands real world multi-dimensional spectral data with distinct features. Spectrum bands exhibit varying noise floors; an effective band wise noise thresholding guarantees an accurate occupancy data. In this work, we have done an extensive real world spectrum occupancy data measurement in frequency range 0.7 GHz to 3 GHz for tele density wise varying locations at Pune, Solapur and Kalaburagi with time diversity ranging from 2 to 7 days. We have applied maximum noise (Max Noise), m-dB and probability of false alarm (PFA) noise thresholding for spectrum occupancy calculations in all bands and across all locations. Overall occupancy across these locations is 37.89 %, 18.90 % and 13.69 % respectively. We have studied signal to noise ratio (SNR), channel vacancy length durations (CVLD) and service congestion rates (SCR) as characteristic features of measured multi-dimensional spectrum data. The results reveal strong time, spectral and spatial correlations of these features across all locations. These features can be used for a multi-dimensional spectrum inference in cognitive radio based on machine learning.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132780311","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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