Pub Date : 2022-09-30DOI: 10.17762/ijcnis.v14i2.5510
N. N. Alleema, R. Raman, Fidel Castro-Cayllahua, V. Rathod, J. Cotrina-Aliaga, S. Ajagekar, R. Kanse
This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance.
{"title":"Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization","authors":"N. N. Alleema, R. Raman, Fidel Castro-Cayllahua, V. Rathod, J. Cotrina-Aliaga, S. Ajagekar, R. Kanse","doi":"10.17762/ijcnis.v14i2.5510","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5510","url":null,"abstract":"This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"23 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":"125198966","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.v14i2.5513
Dadang Hermawan, Ni Made Dewi Kansa Putri, Lucky Kartanto
Data shared between hospitals and patients using mobile and wearable Internet of Medical Things (IoMT) devices raises privacy concerns due to the methods used in training. the development of the Internet of Medical Things (IoMT) and related technologies and the most current advances in these areas The Internet of Medical Things and other recent technological advancements have transformed the traditional healthcare system into a smart one. improvement in computing power and the spread of information have transformed the healthcare system into a high-tech, data-driven operation. On the other hand, mobile and wearable IoMT devices present privacy concerns regarding the data transmitted between hospitals and end users because of the way in which artificial intelligence is trained (AI-centralized). In terms of machine learning (AI-centralized). Devices connected to the IoMT network transmit highly confidential information that could be intercepted by adversaries. Due to the portability of electronic health record data for clinical research made possible by medical cyber-physical systems, the rate at which new scientific discoveries can be made has increased. While AI helps improve medical informatics, the current methods of centralised data training and insecure data storage management risk exposing private medical information to unapproved foreign organisations. New avenues for protecting users' privacy in IoMT without requiring access to their data have been opened by the federated learning (FL) distributive AI paradigm. FL safeguards user privacy by concealing all but gradients during training. DeepFed is a novel Federated Deep Learning approach presented in this research for the purpose of detecting cyber threats to intelligent healthcare CPSs.
{"title":"Cyber Physical System Based Smart Healthcare System with Federated Deep Learning Architectures with Data Analytics","authors":"Dadang Hermawan, Ni Made Dewi Kansa Putri, Lucky Kartanto","doi":"10.17762/ijcnis.v14i2.5513","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5513","url":null,"abstract":"Data shared between hospitals and patients using mobile and wearable Internet of Medical Things (IoMT) devices raises privacy concerns due to the methods used in training. the development of the Internet of Medical Things (IoMT) and related technologies and the most current advances in these areas The Internet of Medical Things and other recent technological advancements have transformed the traditional healthcare system into a smart one. improvement in computing power and the spread of information have transformed the healthcare system into a high-tech, data-driven operation. On the other hand, mobile and wearable IoMT devices present privacy concerns regarding the data transmitted between hospitals and end users because of the way in which artificial intelligence is trained (AI-centralized). In terms of machine learning (AI-centralized). Devices connected to the IoMT network transmit highly confidential information that could be intercepted by adversaries. Due to the portability of electronic health record data for clinical research made possible by medical cyber-physical systems, the rate at which new scientific discoveries can be made has increased. While AI helps improve medical informatics, the current methods of centralised data training and insecure data storage management risk exposing private medical information to unapproved foreign organisations. New avenues for protecting users' privacy in IoMT without requiring access to their data have been opened by the federated learning (FL) distributive AI paradigm. FL safeguards user privacy by concealing all but gradients during training. DeepFed is a novel Federated Deep Learning approach presented in this research for the purpose of detecting cyber threats to intelligent healthcare CPSs.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"309 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":"134041334","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 : 2022-09-10DOI: 10.17762/ijcnis.v14i2.5499
Neeraj Gupta, S. Janani, R. Dilip, Ravi Hosur, Abhay Chaturvedi, Ankur Gupta
In our everyday life records, human activity identification utilizing MotionNode sensors is becoming more and more prominent. A difficult issue in ubiquitous computing and HCI is providing reliable data on human actions and behaviors. In this study, we put forward a practical methodology for incorporating statistical data into Sequential Minimization Optimization-based random forests. In order to extract useful features, we first prepared a 1-Dimensional Hadamard transform wavelet and a 1-Dimensional Local Binary Pattern-dependent extraction technique. Over two benchmark datasets, the University of Southern California-Human Activities Dataset, and the IM-Sporting Behaviors datasets, we employed sequential minimum optimization together with Random Forest to classify activities. Experimental findings demonstrate that our suggested model may successfully be utilized to identify strong human actions for matters related to efficiency and accuracy, and may challenge with existing cutting-edge approaches.
{"title":"Wearable Sensors for Evaluation Over Smart Home Using Sequential Minimization Optimization-based Random Forest","authors":"Neeraj Gupta, S. Janani, R. Dilip, Ravi Hosur, Abhay Chaturvedi, Ankur Gupta","doi":"10.17762/ijcnis.v14i2.5499","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5499","url":null,"abstract":"In our everyday life records, human activity identification utilizing MotionNode sensors is becoming more and more prominent. A difficult issue in ubiquitous computing and HCI is providing reliable data on human actions and behaviors. In this study, we put forward a practical methodology for incorporating statistical data into Sequential Minimization Optimization-based random forests. In order to extract useful features, we first prepared a 1-Dimensional Hadamard transform wavelet and a 1-Dimensional Local Binary Pattern-dependent extraction technique. Over two benchmark datasets, the University of Southern California-Human Activities Dataset, and the IM-Sporting Behaviors datasets, we employed sequential minimum optimization together with Random Forest to classify activities. Experimental findings demonstrate that our suggested model may successfully be utilized to identify strong human actions for matters related to efficiency and accuracy, and may challenge with existing cutting-edge approaches.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129506711","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 : 2022-09-10DOI: 10.17762/ijcnis.v14i2.5494
Hitesh Keserwani, Himanshu Rastogi, Ardhariksa Zukhruf Kurniullah, Sushil Kumar Janardan, R. Raman, V. Rathod, Ankur Gupta
Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model.
{"title":"Security Enhancement by Identifying Attacks Using Machine Learning for 5G Network","authors":"Hitesh Keserwani, Himanshu Rastogi, Ardhariksa Zukhruf Kurniullah, Sushil Kumar Janardan, R. Raman, V. Rathod, Ankur Gupta","doi":"10.17762/ijcnis.v14i2.5494","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5494","url":null,"abstract":"Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133690787","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 architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms.
{"title":"Construction of Data Driven Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network for IoT Healthcare Applications","authors":"M. Sowmya, Sunil Sharma, Akash Kumar Bhagat, Pooja Verma, Sunny Verma, Durgesh Wadhwa","doi":"10.17762/ijcnis.v14i2.5495","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5495","url":null,"abstract":"The architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126084801","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 : 2022-09-10DOI: 10.17762/ijcnis.v14i2.5496
Diksha Verma, Sweta Kumari Barnwal, Amit Barve, M. J. Kannan, Rajesh Gupta, R. Swaminathan
In today's competitive business environment, exponential increase of multimodal content results in a massive amount of shapeless data. Big data that is unstructured has no specific format or organisation and can take any form, including text, audio, photos, and video. Many assumptions and algorithms are generally required to recognize different emotions as per literature survey, and the main focus for emotion recognition is based on single modality, such as voice, facial expression and bio signals. This paper proposed the novel technique in multimodal sentiment sensing with emotion recognition using artificial intelligence technique. Here the audio and visual data has been collected based on social media review and classified using hidden Markov model based extreme learning machine (HMM_ExLM). The features are trained using this method. Simultaneously, these speech emotional traits are suitably maximised. The strategy of splitting areas is employed in the research for expression photographs and various weights are provided to each area to extract information. Speech as well as facial expression data are then merged using decision level fusion and speech properties of each expression in region of face are utilized to categorize. Findings of experiments show that combining features of speech and expression boosts effect greatly when compared to using either speech or expression alone. In terms of accuracy, recall, precision, and optimization level, a parametric comparison was made.
{"title":"Multimodal Sentiment Sensing and Emotion Recognition Based on Cognitive Computing Using Hidden Markov Model with Extreme Learning Machine","authors":"Diksha Verma, Sweta Kumari Barnwal, Amit Barve, M. J. Kannan, Rajesh Gupta, R. Swaminathan","doi":"10.17762/ijcnis.v14i2.5496","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5496","url":null,"abstract":"In today's competitive business environment, exponential increase of multimodal content results in a massive amount of shapeless data. Big data that is unstructured has no specific format or organisation and can take any form, including text, audio, photos, and video. Many assumptions and algorithms are generally required to recognize different emotions as per literature survey, and the main focus for emotion recognition is based on single modality, such as voice, facial expression and bio signals. This paper proposed the novel technique in multimodal sentiment sensing with emotion recognition using artificial intelligence technique. Here the audio and visual data has been collected based on social media review and classified using hidden Markov model based extreme learning machine (HMM_ExLM). The features are trained using this method. Simultaneously, these speech emotional traits are suitably maximised. The strategy of splitting areas is employed in the research for expression photographs and various weights are provided to each area to extract information. Speech as well as facial expression data are then merged using decision level fusion and speech properties of each expression in region of face are utilized to categorize. Findings of experiments show that combining features of speech and expression boosts effect greatly when compared to using either speech or expression alone. In terms of accuracy, recall, precision, and optimization level, a parametric comparison was made.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125383321","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 : 2022-09-10DOI: 10.17762/ijcnis.v14i2.5498
I. Yuwono, Eviani Damastuti Utomo
A brain-computer interface (BCI) would afford real-time communication, pointedly refining the standard of lifespan, brain-to-internet (B2I) connection, and interaction between the external digital devices and the brain. This assistive technology invents information and transmission advancement patterns, like directly linking the brain and multimedia gadgets to the cyber world. This system will convert brain data to signals which is understandable by multimedia gadgets without physical intervention and exchanges human-related languages with external atmosphere control protocols. These progressive difficulties would limit security severely. Hence, the rate of ransomware, attacks, malware, and other types of vulnerabilities will be rising radically. On the other hand, the necessity to enhance conventional processes for investigating cyberenvironment security facets. This article presents a Neurological Disorders Detection based on Computer Brain Interface Using Centralized Blockchain with Intrusion System (NDDCBI-CBIS). The projected NDDCBI-CBIS technique focuses on the identification of neurological disorders and epileptic seizure detection. To attain this, the presented NDDCBI-CBIS technique pre-processes the biomedical signals. Next, to detect epileptic seizures, long short-term memory (LSTM) model is applied. The experimental evaluation of the NDDCBI-CBIS approach can be tested by making use of the medical dataset and the outcomes inferred from the enhanced outcomes of the NDDCBI-CBIS technique.
{"title":"Neurological Disorders Detection Based on Computer Brain Interface Using Centralized Blockchain with Intrusion System","authors":"I. Yuwono, Eviani Damastuti Utomo","doi":"10.17762/ijcnis.v14i2.5498","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5498","url":null,"abstract":"A brain-computer interface (BCI) would afford real-time communication, pointedly refining the standard of lifespan, brain-to-internet (B2I) connection, and interaction between the external digital devices and the brain. This assistive technology invents information and transmission advancement patterns, like directly linking the brain and multimedia gadgets to the cyber world. This system will convert brain data to signals which is understandable by multimedia gadgets without physical intervention and exchanges human-related languages with external atmosphere control protocols. These progressive difficulties would limit security severely. Hence, the rate of ransomware, attacks, malware, and other types of vulnerabilities will be rising radically. On the other hand, the necessity to enhance conventional processes for investigating cyberenvironment security facets. This article presents a Neurological Disorders Detection based on Computer Brain Interface Using Centralized Blockchain with Intrusion System (NDDCBI-CBIS). The projected NDDCBI-CBIS technique focuses on the identification of neurological disorders and epileptic seizure detection. To attain this, the presented NDDCBI-CBIS technique pre-processes the biomedical signals. Next, to detect epileptic seizures, long short-term memory (LSTM) model is applied. The experimental evaluation of the NDDCBI-CBIS approach can be tested by making use of the medical dataset and the outcomes inferred from the enhanced outcomes of the NDDCBI-CBIS technique.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121754014","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 : 2022-08-31DOI: 10.17762/ijcnis.v14i2.5480
C. Mohan, K. S. Kumar, K. Kavya
The design of RF-MEMS Switch is useful for future artificial intelligence applications. Radio detection and range estimation has been employed with RF MEMS technology. Attenuators, limiters, phase shifters, T/R switches, and adjustable matching networks are components of RF MEMS. The proposed RF MEMS technology has been introduced in T/R modules, lenses, reflect arrays, sub arrays and switching beam formers. The uncertain RF MEMS switches have been faced many issues like switching and voltage alterations. This study aims in the direction of design, simulation, model along with RF MEMS switching analysis including consistent curving or meandering. The proposed RF MEMS Switch is a flexure form of the Meanders that attain minimal power in nominal voltage. Moreover, this research work highlights the materials assortment in case of beam along with signal-based dielectric. The performance analysis is demonstrated for various materials that have been utilized in the design purpose. Further, better isolation is accomplished at the range of -31dB necessary regarding 8.06V pull-in voltage through a spring constant valued at 3.588N/m, switching capacitance analysis has been found to be 103 fF at ON state and 7.03pF at OFF state and the proposed switch is optimized to work at 38GHz. The designed RF MEMS switch is giving 30% voltage improvement; switching frequency is improved by 21.32% had been attained, which are outperformance the methodology and compete with present technology.
{"title":"Performance Exploration of Uncertain RF MEMS Switch Design with Uniform Meanders","authors":"C. Mohan, K. S. Kumar, K. Kavya","doi":"10.17762/ijcnis.v14i2.5480","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5480","url":null,"abstract":"The design of RF-MEMS Switch is useful for future artificial intelligence applications. Radio detection and range estimation has been employed with RF MEMS technology. Attenuators, limiters, phase shifters, T/R switches, and adjustable matching networks are components of RF MEMS. The proposed RF MEMS technology has been introduced in T/R modules, lenses, reflect arrays, sub arrays and switching beam formers. The uncertain RF MEMS switches have been faced many issues like switching and voltage alterations. This study aims in the direction of design, simulation, model along with RF MEMS switching analysis including consistent curving or meandering. The proposed RF MEMS Switch is a flexure form of the Meanders that attain minimal power in nominal voltage. Moreover, this research work highlights the materials assortment in case of beam along with signal-based dielectric. The performance analysis is demonstrated for various materials that have been utilized in the design purpose. Further, better isolation is accomplished at the range of -31dB necessary regarding 8.06V pull-in voltage through a spring constant valued at 3.588N/m, switching capacitance analysis has been found to be 103 fF at ON state and 7.03pF at OFF state and the proposed switch is optimized to work at 38GHz. The designed RF MEMS switch is giving 30% voltage improvement; switching frequency is improved by 21.32% had been attained, which are outperformance the methodology and compete with present technology.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124283859","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 : 2022-08-31DOI: 10.17762/ijcnis.v14i2.5481
K. U. Kiran, K. Ramesh, S. Rao
Location evaluation applications are one of the most imperative services in GPS position applications. The Global Positioning Systems (GPS) is a versatile and legacy technology has been providing a reliable and accurate position of objects on Earth. The uncertain GPS position is considered an initialization parameter for many inherent systems in today’s world. This initialization position estimate has a wide variety of applications such as Coast line maps, understanding the geo-dynamical phenomena such as volcanic eruptions, earthquakes and subsequent originating source mechanisms, Mean Sea level estimation for contours of land surfaces, Oceanic en-route as well as in mobile and Vehicular technologies etc. The validation and reliability of the results of all those applications is dependent on the accuracy of the position estimate given by GPS. In this work an attempt is made to retrieve accurate and reliable position parameters from GPS by correcting the measurement errors for all the visible satellites at every epoch. The maximum and minimum pseudo ranges in L2 signal observed are 2437404.2 meters and -76295.22 meters.
{"title":"Adaptive And Reliable GPS Uncertain Position Estimation an Insightful Oceanography and Geography Applications","authors":"K. U. Kiran, K. Ramesh, S. Rao","doi":"10.17762/ijcnis.v14i2.5481","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5481","url":null,"abstract":"Location evaluation applications are one of the most imperative services in GPS position applications. The Global Positioning Systems (GPS) is a versatile and legacy technology has been providing a reliable and accurate position of objects on Earth. The uncertain GPS position is considered an initialization parameter for many inherent systems in today’s world. This initialization position estimate has a wide variety of applications such as Coast line maps, understanding the geo-dynamical phenomena such as volcanic eruptions, earthquakes and subsequent originating source mechanisms, Mean Sea level estimation for contours of land surfaces, Oceanic en-route as well as in mobile and Vehicular technologies etc. The validation and reliability of the results of all those applications is dependent on the accuracy of the position estimate given by GPS. In this work an attempt is made to retrieve accurate and reliable position parameters from GPS by correcting the measurement errors for all the visible satellites at every epoch. The maximum and minimum pseudo ranges in L2 signal observed are 2437404.2 meters and -76295.22 meters.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134116649","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}
Attention deficit hyperactivity disorder (ADHD) is a frequent Neuro-generative mental disorder. It can persist in adulthood and be expressed as a cognitive complaint. Behavioural analysis of ADHD consumes more time. This is a multi-informant complex procedure due to the overlaps in symptomatology which is the cause for delay in diagnosis and treatment. Dur to these behavioural consequences and various causes, no single test is utilized till now for diagnosing this disorder. Hence, a diagnosing model of ADHD based on Continuous Ability Assessment Test (CAAT) can enhance and balance behavioural assessment. The objective behind this study is to use a deep learning based model with CAAT for predicting ADHD. The proposed Auto Encoder Based Hidden Markov Model (AE-HMM) produces low-dimensional features of brain structures, and a novel Pearson Correlation Coefficient (PCC) is employed for normalizing these features in order to minimize batch effects over populations and datasets. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like CogniLearn and 3-D Convolutional Neural Networks (3DCNN). It is found that the proposed AE-HMM method achieves 93.68% of accuracy, 90.66% of sensitivity, 87.72% of specificity, 87.78% of F1-score and 74.22% of kappa score.
{"title":"Cognitive Based Attention Deficit Hyperactivity Disorder Detection with Ability Assessment Using Auto Encoder Based Hidden Markov Model","authors":"Mahesh T R, Tanmay Goswami, Srinivasan Sriramulu, Neeraj Sharma, Alka Kumari, Ganesh Khekare","doi":"10.17762/ijcnis.v14i2.5464","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i2.5464","url":null,"abstract":"Attention deficit hyperactivity disorder (ADHD) is a frequent Neuro-generative mental disorder. It can persist in adulthood and be expressed as a cognitive complaint. Behavioural analysis of ADHD consumes more time. This is a multi-informant complex procedure due to the overlaps in symptomatology which is the cause for delay in diagnosis and treatment. Dur to these behavioural consequences and various causes, no single test is utilized till now for diagnosing this disorder. Hence, a diagnosing model of ADHD based on Continuous Ability Assessment Test (CAAT) can enhance and balance behavioural assessment. The objective behind this study is to use a deep learning based model with CAAT for predicting ADHD. The proposed Auto Encoder Based Hidden Markov Model (AE-HMM) produces low-dimensional features of brain structures, and a novel Pearson Correlation Coefficient (PCC) is employed for normalizing these features in order to minimize batch effects over populations and datasets. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like CogniLearn and 3-D Convolutional Neural Networks (3DCNN). It is found that the proposed AE-HMM method achieves 93.68% of accuracy, 90.66% of sensitivity, 87.72% of specificity, 87.78% of F1-score and 74.22% of kappa score.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123209654","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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