Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.009
Ş. Yıldırım, Caglar Sevim, M. Kalkat
Due to travelling on railway systems; there are many gaps and problems in cross areas. Therefore; it is necessary and very important to establish intelligent crossing systems in such areas. On the other hand, it is not possible for trains to stop or brake immediately against an obstacle due to their high speed and inertia. For this reason, it is necessary to work on the safety/warning of the other main factors and necessities (pedestrians and vehicles) in level crossings. This experimental investigation is carried out by using an experimental real-time train and crossing systems. The main vibration parameters are analysed by using neural networks. First, the dynamics of the train-rail system related to level crossings are examined, and the vibrations created by the train on rails are measured at different speeds. Then three types of proposed neural networks predictors, Levenberg-Marquardt backpropagation (LMBP), scaled conjugate gradient backpropagation (SCGB) and BFGS quasi-Newton backpropagation (BFGS) are used to predict the vibration of the train-rail system. From the results, it is seen that the proposed LMBP is more suitable for analysing and predicting the vibration of the train-rail system. It is clear that the speeds of the trains approaching the level crossing can be estimated from the vibration of the trains on the rails.
{"title":"Vibration analyses of railway systems using proposed neural predictors","authors":"Ş. Yıldırım, Caglar Sevim, M. Kalkat","doi":"10.14311/nnw.2023.33.009","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.009","url":null,"abstract":"Due to travelling on railway systems; there are many gaps and problems in cross areas. Therefore; it is necessary and very important to establish intelligent crossing systems in such areas. On the other hand, it is not possible for trains to stop or brake immediately against an obstacle due to their high speed and inertia. For this reason, it is necessary to work on the safety/warning of the other main factors and necessities (pedestrians and vehicles) in level crossings. This experimental investigation is carried out by using an experimental real-time train and crossing systems. The main vibration parameters are analysed by using neural networks. First, the dynamics of the train-rail system related to level crossings are examined, and the vibrations created by the train on rails are measured at different speeds. Then three types of proposed neural networks predictors, Levenberg-Marquardt backpropagation (LMBP), scaled conjugate gradient backpropagation (SCGB) and BFGS quasi-Newton backpropagation (BFGS) are used to predict the vibration of the train-rail system. From the results, it is seen that the proposed LMBP is more suitable for analysing and predicting the vibration of the train-rail system. It is clear that the speeds of the trains approaching the level crossing can be estimated from the vibration of the trains on the rails.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.011
Syeda Hafsa Ahmed, Mehwish Raza, M. Kazmi, Syeda Shajeeha Mehdi, Inshal Rehman, S. A. Qazi
Modern development in deep learning and computer vision techniques, intelligent transportation system (ITS) has emerged as a useful tool for building a traffic infrastructure in smart cities. Previously, several computer vision techniques have been proposed for vehicle recognition, which were limited in handling undisciplined, dense and laneless traffic conditions. Moreover, these frameworks did not incorporate many of the local vehicle configurations common in South Asian countries such as Pakistan, Bangladesh, and India. Considering the limitations of previous frameworks, this paper presents efficient vehicle detection and counting model for undisciplined conditions including dense and laneless traffic, occulusion cases and diverse range of local vehicles. A dataset of more than 2400 images of vehicles has been collected comprising of six new categories of local vehicles, and considering undisciplined traffic conditions to ensure robustness in vehicle detection and counting system. Transfer learning based technique has been used, using faster R-CNN model with Inception V2 as underlying architecture. The experimental results show a precision of 86.14% in terms of mAP. The work finds its application in South Asian contexts as more smart cities are formed in this region. The proposed framework will enable traffic monitoring with higher reliability, accuracy and granularity, contributing in having next-generation ITS.
{"title":"Towards the next generation intelligent transportation system: A vehicle detection and counting framework for undisciplined traffic conditions","authors":"Syeda Hafsa Ahmed, Mehwish Raza, M. Kazmi, Syeda Shajeeha Mehdi, Inshal Rehman, S. A. Qazi","doi":"10.14311/nnw.2023.33.011","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.011","url":null,"abstract":"Modern development in deep learning and computer vision techniques, intelligent transportation system (ITS) has emerged as a useful tool for building a traffic infrastructure in smart cities. Previously, several computer vision techniques have been proposed for vehicle recognition, which were limited in handling undisciplined, dense and laneless traffic conditions. Moreover, these frameworks did not incorporate many of the local vehicle configurations common in South Asian countries such as Pakistan, Bangladesh, and India. Considering the limitations of previous frameworks, this paper presents efficient vehicle detection and counting model for undisciplined conditions including dense and laneless traffic, occulusion cases and diverse range of local vehicles. A dataset of more than 2400 images of vehicles has been collected comprising of six new categories of local vehicles, and considering undisciplined traffic conditions to ensure robustness in vehicle detection and counting system. Transfer learning based technique has been used, using faster R-CNN model with Inception V2 as underlying architecture. The experimental results show a precision of 86.14% in terms of mAP. The work finds its application in South Asian contexts as more smart cities are formed in this region. The proposed framework will enable traffic monitoring with higher reliability, accuracy and granularity, contributing in having next-generation ITS.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.018
Muhammad Shahzad Haroon, Husnain Mansoor Ali
In this paper, a defence mechanism is proposed against adversarial attacks. The defence is based on an ensemble classifier that is adversarially trained. This is accomplished by generating adversarial attacks from four different attack methods, i.e., Jacobian-based saliency map attack (JSMA), projected gradient descent (PGD), momentum iterative method (MIM), and fast gradient signed method (FGSM). The adversarial examples are used to identify the robust machine-learning algorithms which eventually participate in the ensemble. The adversarial attacks are divided into seen and unseen attacks. To validate our work, the experiments are conducted using NSLKDD, UNSW-NB15 and CICIDS17 datasets. Grid search for the ensemble is used to optimise results. The parameter used for performance evaluations is accuracy, F1 score and AUC score. It is shown that an adversarially trained ensemble classifier produces better results.
{"title":"Ensemble adversarial training based defense against adversarial attacks for machine learning-based intrusion detection system","authors":"Muhammad Shahzad Haroon, Husnain Mansoor Ali","doi":"10.14311/nnw.2023.33.018","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.018","url":null,"abstract":"In this paper, a defence mechanism is proposed against adversarial attacks. The defence is based on an ensemble classifier that is adversarially trained. This is accomplished by generating adversarial attacks from four different attack methods, i.e., Jacobian-based saliency map attack (JSMA), projected gradient descent (PGD), momentum iterative method (MIM), and fast gradient signed method (FGSM). The adversarial examples are used to identify the robust machine-learning algorithms which eventually participate in the ensemble. The adversarial attacks are divided into seen and unseen attacks. To validate our work, the experiments are conducted using NSLKDD, UNSW-NB15 and CICIDS17 datasets. Grid search for the ensemble is used to optimise results. The parameter used for performance evaluations is accuracy, F1 score and AUC score. It is shown that an adversarially trained ensemble classifier produces better results.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135613028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.013
K. Baskar, P. Vijayalakshmi, K. Muthumanickam, A. Arthi
Wireless sensor network (WSN) is a kind of network specifically suitable for place where infrastructure and resources are playing a vital role. Moreover, nodes in a WSN are autonomous in nature. WSNs can be able to solve various real-time problems and issues like smart healthcare, smart office, smart energy, smart home, etc. As energy becomes one of the scarce supplies for this kind of network, attacks against authentication help to validate the legitimacy of sensor nodes become foremost important. Such attacks exhaust the power of nodes that are currently connected to a WSN, thereby reducing their lifetime. In this article, a zonal node authentication technique as well as optimal data access scheduling that renders data deliverance with improved quality of service and network lifetime is proposed. The results obtained from simulation for diverse WSN topologies accentuate the claim of our method over the existing solutions and demonstrate to be efficient in discovering legitimate sensor nodes with the optimal workload. Besides improved network lifetime, efficiency, and throughput, the proposed method also reinforces the security measures of the WSN by integrating node authentication.
{"title":"A novel authentication and access scheduling scheme to improve the performance of WSN","authors":"K. Baskar, P. Vijayalakshmi, K. Muthumanickam, A. Arthi","doi":"10.14311/nnw.2023.33.013","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.013","url":null,"abstract":"Wireless sensor network (WSN) is a kind of network specifically suitable for place where infrastructure and resources are playing a vital role. Moreover, nodes in a WSN are autonomous in nature. WSNs can be able to solve various real-time problems and issues like smart healthcare, smart office, smart energy, smart home, etc. As energy becomes one of the scarce supplies for this kind of network, attacks against authentication help to validate the legitimacy of sensor nodes become foremost important. Such attacks exhaust the power of nodes that are currently connected to a WSN, thereby reducing their lifetime. In this article, a zonal node authentication technique as well as optimal data access scheduling that renders data deliverance with improved quality of service and network lifetime is proposed. The results obtained from simulation for diverse WSN topologies accentuate the claim of our method over the existing solutions and demonstrate to be efficient in discovering legitimate sensor nodes with the optimal workload. Besides improved network lifetime, efficiency, and throughput, the proposed method also reinforces the security measures of the WSN by integrating node authentication.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135650379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.001
Hao Guo, Xunlin Tao, Xingcun Li
With the development of high-density and intensive aquaculture production and the increasing frequency of water quality changes in aquaculture water bodies, the number of pollution sources in aquaculture ponds is also increasing. As the water quality of aquaculture ponds is a crucial factor affecting the production and quality of pond aquaculture products, water quality assessment and management are more important than in the past. Water quality analysis is a crucial way to evaluate the water quality of fish farming water bodies. Traditional water quality analysis is usually obtained by practitioners through experience and visual observation. There is an observability deviation caused by subjectivity. Deep transfer learning-based water quality monitoring system is easier to deploy and can avoid unnecessary duplication of efforts to save costs for aquaculture industry. This paper uses the transfer learning model of artificial intelligence to analyze the water color image automatically. 5203 water quality images are collected to create a water quality image dataset, which contains five classes based on water color. Based on the dataset, a deep transfer learning-based classification model is proposed to identify water quality images. The experimental results show that the deep learning model based on transfer learning achieves 99% accuracy and has excellent performance.
{"title":"Water quality image classification for aquaculture using deep transfer learning","authors":"Hao Guo, Xunlin Tao, Xingcun Li","doi":"10.14311/nnw.2023.33.001","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.001","url":null,"abstract":"With the development of high-density and intensive aquaculture production and the increasing frequency of water quality changes in aquaculture water bodies, the number of pollution sources in aquaculture ponds is also increasing. As the water quality of aquaculture ponds is a crucial factor affecting the production and quality of pond aquaculture products, water quality assessment and management are more important than in the past. Water quality analysis is a crucial way to evaluate the water quality of fish farming water bodies. Traditional water quality analysis is usually obtained by practitioners through experience and visual observation. There is an observability deviation caused by subjectivity. Deep transfer learning-based water quality monitoring system is easier to deploy and can avoid unnecessary duplication of efforts to save costs for aquaculture industry. This paper uses the transfer learning model of artificial intelligence to analyze the water color image automatically. 5203 water quality images are collected to create a water quality image dataset, which contains five classes based on water color. Based on the dataset, a deep transfer learning-based classification model is proposed to identify water quality images. The experimental results show that the deep learning model based on transfer learning achieves 99% accuracy and has excellent performance.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.014
S. Poonkuzhal, M. Shobana, J. Jeyalakshmi
The rise of internet connectivity across the globe increases the count of IoT (internet of things)/IIoT (industrial internet of things) devices exponentially. The objects/devices which are connected to the internet are always prone to malicious attacks at various levels, such as physical, network, fog, and applications, which exist in the IoT architecture. Many researchers have addressed this issue and designed their own solutions based on machine and deep learning techniques. It is undeniable that deep learning outperforms machine learning (ML), but it necessitates a massive amount of datasets with appropriate labels. In this work, the deep transfer learning (TL) technique has been adapted for gated recurrent unit (GRU). Each model is trained using a dataset that belongs to one source IoT device (source domain), and this trained model is used to classify the malicious traffic in another dataset that belongs to some other IoT device (target domain). This approach is used for binary classification. These transfer learning models have been evaluated using an IoT/IIoT telemetry dataset called ToN IoT which comprises the sensor data generated from the seven different types of IoT devices. The highest accuracy achieved by IoT garage door was upto 99.76% as a source domain by fixing IoT thermostat as target domain. These models were also evaluated using some more metrics such as precision, recall, F1-measure, training time and testing time. By implementing transfer learning based GRU model, the accuracy of the model is improved from 69.20% to 99.76%. Moreover, to prove the efficiency of the proposed model, it is compared with state of art deep learning model and its results were analyzed in a detailed manner.
{"title":"A deep transfer learning approach for IoT/IIoT cyber attack detection using telemetry data","authors":"S. Poonkuzhal, M. Shobana, J. Jeyalakshmi","doi":"10.14311/nnw.2023.33.014","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.014","url":null,"abstract":"The rise of internet connectivity across the globe increases the count of IoT (internet of things)/IIoT (industrial internet of things) devices exponentially. The objects/devices which are connected to the internet are always prone to malicious attacks at various levels, such as physical, network, fog, and applications, which exist in the IoT architecture. Many researchers have addressed this issue and designed their own solutions based on machine and deep learning techniques. It is undeniable that deep learning outperforms machine learning (ML), but it necessitates a massive amount of datasets with appropriate labels. In this work, the deep transfer learning (TL) technique has been adapted for gated recurrent unit (GRU). Each model is trained using a dataset that belongs to one source IoT device (source domain), and this trained model is used to classify the malicious traffic in another dataset that belongs to some other IoT device (target domain). This approach is used for binary classification. These transfer learning models have been evaluated using an IoT/IIoT telemetry dataset called ToN IoT which comprises the sensor data generated from the seven different types of IoT devices. The highest accuracy achieved by IoT garage door was upto 99.76% as a source domain by fixing IoT thermostat as target domain. These models were also evaluated using some more metrics such as precision, recall, F1-measure, training time and testing time. By implementing transfer learning based GRU model, the accuracy of the model is improved from 69.20% to 99.76%. Moreover, to prove the efficiency of the proposed model, it is compared with state of art deep learning model and its results were analyzed in a detailed manner.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135650037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.016
K. Daqrouq, A. Hazazi, A. Alkhateeb, R.A. Alharbey
The measurement of heart rate (HR) has numerous applications in various fields, such as the internet of things, security, sports, and telemedicine. There are many methods for measuring pulse rates, and this research is based on a novel technique of measuring the heartbeat using image recognition technology. The innovations in the field of visual objects have made the detection process easy and quick, with high efficiency. Four step-based algorithms, including a computer, an external high-definition camera, and an open-source computer vision library, have been presented for measuring heart rate. The first step was the face detection (FD) algorithm, and the second was the area attention algorithm to determine the region of interest (ROI). The ROI signal analysis algorithm was used in the third step, using a fast Fourier transform (FFT) for frequency detection. The pulse measurement phase was the final step, and it was based on the strength of the color concentration in proportion to the time extracted from video clips. With the help of our recorded database of 50 participants based on different ages and skin colors, the process was carried out. The results of this study contributed to the development of an HR detection technique based on image recognition using the Python programming language. This is a very comfortable and effective method for measuring the human heart rate. This research article discussed various factors and obstacles that affect heart rate measurement. The results found that our system is highly competent in measuring heart rate.
{"title":"Heart rate measurement using image recognition technology","authors":"K. Daqrouq, A. Hazazi, A. Alkhateeb, R.A. Alharbey","doi":"10.14311/nnw.2023.33.016","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.016","url":null,"abstract":"The measurement of heart rate (HR) has numerous applications in various fields, such as the internet of things, security, sports, and telemedicine. There are many methods for measuring pulse rates, and this research is based on a novel technique of measuring the heartbeat using image recognition technology. The innovations in the field of visual objects have made the detection process easy and quick, with high efficiency. Four step-based algorithms, including a computer, an external high-definition camera, and an open-source computer vision library, have been presented for measuring heart rate. The first step was the face detection (FD) algorithm, and the second was the area attention algorithm to determine the region of interest (ROI). The ROI signal analysis algorithm was used in the third step, using a fast Fourier transform (FFT) for frequency detection. The pulse measurement phase was the final step, and it was based on the strength of the color concentration in proportion to the time extracted from video clips. With the help of our recorded database of 50 participants based on different ages and skin colors, the process was carried out. The results of this study contributed to the development of an HR detection technique based on image recognition using the Python programming language. This is a very comfortable and effective method for measuring the human heart rate. This research article discussed various factors and obstacles that affect heart rate measurement. The results found that our system is highly competent in measuring heart rate.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135650391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.002
Adam Hlubuček
The paper presents the method of integration, which is supposed to be applied to the structure of the railway infrastructure topological description system expressed at the level of detail designated as microL2 in order to transform it into the structure expressed at the level of detail designated as macroN0,L0 . The microL2 level is the level of detail at which individual tracks in the structural sense and turnout branches are recognized, while the macroN0,L0 level is the level of individual operational points and line sections. The proposed integration algorithm takes into account both the parameter values of the individual elements appearing at the reference level of detail microL2 and their topological interconnectedness. Based on these aspects, these elements are integrated into the elements of the derived level of detail macroN0,L0 that can be described by the transformed parameter values. The relations between the respective elements are also transformed accordingly. While describing the transformation algorithm, the terminology and principles of the UIC RailTopoModel are used.
{"title":"Integration of railway infrastructure topological description elements from the microL2 to the macroN0,L0 level of detail","authors":"Adam Hlubuček","doi":"10.14311/nnw.2023.33.002","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.002","url":null,"abstract":"The paper presents the method of integration, which is supposed to be applied to the structure of the railway infrastructure topological description system expressed at the level of detail designated as microL2 in order to transform it into the structure expressed at the level of detail designated as macroN0,L0 . The microL2 level is the level of detail at which individual tracks in the structural sense and turnout branches are recognized, while the macroN0,L0 level is the level of individual operational points and line sections. The proposed integration algorithm takes into account both the parameter values of the individual elements appearing at the reference level of detail microL2 and their topological interconnectedness. Based on these aspects, these elements are integrated into the elements of the derived level of detail macroN0,L0 that can be described by the transformed parameter values. The relations between the respective elements are also transformed accordingly. While describing the transformation algorithm, the terminology and principles of the UIC RailTopoModel are used.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.008
K. Krishna, Ramakrishna Thirumuru
Wireless sensor networks (WSNs) have recently acquired prominence in a variety of applications such as remote monitoring and tracking. Since it is virtually hard to recharge the nodes in their remote deployment, also, the transmission of data from nodes to the base station requires a significant amount of energy. Thus, our research proposes a routing protocol, namely hybrid falcon-improved ACO Nature-Inspired Optimization using a deep learning model to reduce energy consumption while increases the network lifetime. In the developed model, initially, the falcon optimization technique is utilized to locate the best possible cluster heads in the quickest possible time. Furthermore, to improve the quality of service in routing optimization a new improved ACO has been proposed in which linear flexible operator and the premier operator are used to increasing the iteration speed. Finally, the optimum route is obtained through DBNN based on predicted energy. As a result, our proposed model gives a lifetime as 121 s and energy consumption as 0.041 J at 500 rounds when compared to the baseline approaches. Therefore, our proposed approaches provides better routing and improves the QoS as well as the energy consumption which increases the longevity of mobile nodes.
{"title":"Enhanced QOS energy-efficient routing algorithm using deep belief neural network in hybrid falcon-improved ACO nature-inspired optimization in wireless sensor networks","authors":"K. Krishna, Ramakrishna Thirumuru","doi":"10.14311/nnw.2023.33.008","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.008","url":null,"abstract":"Wireless sensor networks (WSNs) have recently acquired prominence in a variety of applications such as remote monitoring and tracking. Since it is virtually hard to recharge the nodes in their remote deployment, also, the transmission of data from nodes to the base station requires a significant amount of energy. Thus, our research proposes a routing protocol, namely hybrid falcon-improved ACO Nature-Inspired Optimization using a deep learning model to reduce energy consumption while increases the network lifetime. In the developed model, initially, the falcon optimization technique is utilized to locate the best possible cluster heads in the quickest possible time. Furthermore, to improve the quality of service in routing optimization a new improved ACO has been proposed in which linear flexible operator and the premier operator are used to increasing the iteration speed. Finally, the optimum route is obtained through DBNN based on predicted energy. As a result, our proposed model gives a lifetime as 121 s and energy consumption as 0.041 J at 500 rounds when compared to the baseline approaches. Therefore, our proposed approaches provides better routing and improves the QoS as well as the energy consumption which increases the longevity of mobile nodes.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.012
Isam Abu-Qasmieh, I. Masad, Hiam Alquran, Khaled Z. Alawneh
Synthetic MRI FLAIR images of an abdominal 3D multimodality phantom and in vivo human knee have been generated from real CT images using predefined mapping functions of CT mean and standard deviation with the corresponding proton density PD, T1 and T2 that were previously generated from spin-echo sequence. First, the validity of generating synthetic MR images from different sequences were tested and the same PD, T1 and T2 maps that were generated from the real CT image have been used in the simulation of MRI inversion-recovery (IR) sequence. The similarity results between the real and synthetic IR sequence images, using different inversion times TI, showed a very good agreement. After confirming the feasibility of generating synthetic IR images from the PD, T1 and T2-maps, that were originally obtained from spin-echo sequence using the phantom, the simulation of a knee image has been generated from the corresponding knee CT real image using the steady-state transverse magnetization formula of the inversion-recovery sequence. The simulated FLAIR IR sequence MR image are generated using proper TI for nulling the signal from the synovial fluid, where the image complement is used as a mask for segmenting the corresponding tissue region in the real CT image.
{"title":"Generation of synthetic FLAIR MRI image from real CT image for accurate synovial fluid segmentation in human knee image","authors":"Isam Abu-Qasmieh, I. Masad, Hiam Alquran, Khaled Z. Alawneh","doi":"10.14311/nnw.2023.33.012","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.012","url":null,"abstract":"Synthetic MRI FLAIR images of an abdominal 3D multimodality phantom and in vivo human knee have been generated from real CT images using predefined mapping functions of CT mean and standard deviation with the corresponding proton density PD, T1 and T2 that were previously generated from spin-echo sequence. First, the validity of generating synthetic MR images from different sequences were tested and the same PD, T1 and T2 maps that were generated from the real CT image have been used in the simulation of MRI inversion-recovery (IR) sequence. The similarity results between the real and synthetic IR sequence images, using different inversion times TI, showed a very good agreement. After confirming the feasibility of generating synthetic IR images from the PD, T1 and T2-maps, that were originally obtained from spin-echo sequence using the phantom, the simulation of a knee image has been generated from the corresponding knee CT real image using the steady-state transverse magnetization formula of the inversion-recovery sequence. The simulated FLAIR IR sequence MR image are generated using proper TI for nulling the signal from the synovial fluid, where the image complement is used as a mask for segmenting the corresponding tissue region in the real CT image.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67126151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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