Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introdu...Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.展开更多
Empirical and deterministic models have not proven to be effective in path loss predictions because of the problems of computational complexities, low accuracies, and inability to generalize. To solve these problems r...Empirical and deterministic models have not proven to be effective in path loss predictions because of the problems of computational complexities, low accuracies, and inability to generalize. To solve these problems relating to path loss predictions, this article presents an optimal path loss propagation model developed at 3.4 GHz with the use of fuzzy logic. We introduced Fuzzy logic to accurately represent all forms of uncertainties in the data spectrum as the signal propagates from the transceiver to the receiver, thereby producing accurate results. Experimental data were collected across Cyprus at 3.4 GHz and compared with three existing path loss models. The fuzzy-logic path loss prediction model was then developed and compared with the experimental data and with each of the theoretical empirical models, the newly developed model predicted signal loss with the greatest accuracy as it gives the lowest root-mean-square error. The newly developed model is very efficient for signal propagation and path loss prediction.展开更多
Road transport is been used for moving people and all kinds of goods throughout the world. However, it is one mode of transportation that is prone to accidents and it faces a plethora of never-ending challenges, such ...Road transport is been used for moving people and all kinds of goods throughout the world. However, it is one mode of transportation that is prone to accidents and it faces a plethora of never-ending challenges, such as the frequent loss of lives and valuables when accident occurs. The best course of action to handle these issues is to set up an autonomous incident detection system using wireless communication, 5G technologies and the Internet of Things. IoT is a seamless technology that increases the connectivity between humans and machines. It is web-based, and improves communication between vehicle to vehicle, vehicle to infrastructures, transfer of data and information to predict incident occurrences through various networks and frameworks such as eCall, OneM2M and integration of mobile broadband. Additionally, internet of things is being adopted for public safety;for instance, it can speed up first responders’ response times to situations by displaying the best routes to a scene of an accident. The rapid development of 5G is happening in parallel with developments of internet of things (IoT), artificial intelligence (AI), and smart platforms for novel applications such as mission-critical communications. 5G is a new generation technology that operates on the Ultra High Spectrum Band UHSB. It is an innovation that uses the pedestrians-vehicle-road-cloud, and the communication between vehicle locations and temperature of high-quality connection. It is essential for intelligent transport systems because it allows for information sharing, prediction of incidences as safety is the primary concern of road transport. This review examines accident detection through 5G technology, integrated mobile broadband, and multiple inputs multiple outputs (MIMO) wireless system. Finally, we conclude by examining recent technology, challenges, present and future research trends.展开更多
Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological condition.It disrupts signals between the bra...Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological condition.It disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and balance.Traditional methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this problem.This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy.This paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat over-fitting.We use gene expression data for MS research in the GEO GSE17048 dataset.The dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the dataset.Meanwhile,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.展开更多
In the current scenario,information technology is taking maximum benefits from the“Intelligent Systems and Internet of Things(IoT)”.In order to get quality articles,we have circulated the call for papers to the rese...In the current scenario,information technology is taking maximum benefits from the“Intelligent Systems and Internet of Things(IoT)”.In order to get quality articles,we have circulated the call for papers to the researchers and academician and received numbers of quality papers.However,due to scope of special issue and review reports,we were able to add papers in this issue.The vip editors are thankful to the authors for supporting a long wait and finally it is before you.The aim of the special issue was to explore ample knowledge and submit some good pieces of papers for Big Data Mining and Analytics.We have received many papers for this special issue and based on the comments of the reviewers and quality of the research papers,we have identified 7 articles for this special issue.This special issue is addressing the original research on the theory,“Intelligent Systems and Internet of Things”,with the aim to contribute better work to the researcher and academic fraternity.We are very much thankful to the authors for their support and for keeping their faith in our vip editorial process.It takes more than a year to complete this issue,we are sure that their work will be well recognized by the researchers and readers who are working in engineering and informatics domain.展开更多
The Internet of Things(IoT)is one of the most prominent technologies emerged in recent years.It has a broad spectrum of applications in diverse disciplines.The IoT is evolving as a significant area of future technolog...The Internet of Things(IoT)is one of the most prominent technologies emerged in recent years.It has a broad spectrum of applications in diverse disciplines.The IoT is evolving as a significant area of future technology and gaining much attention from various walks of life.IoT has revolutionized various application domains,such as home automation,industrial automation,medical aids,mobile healthcare,elderly assistance,intelligent energy management and smart grids,automotive,traffic management,and many others.These applications will make use of the potentially enormous amount and variety of data generated by such objects to provide new services to citizens,companies,and public administrations.展开更多
文摘Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.
文摘Empirical and deterministic models have not proven to be effective in path loss predictions because of the problems of computational complexities, low accuracies, and inability to generalize. To solve these problems relating to path loss predictions, this article presents an optimal path loss propagation model developed at 3.4 GHz with the use of fuzzy logic. We introduced Fuzzy logic to accurately represent all forms of uncertainties in the data spectrum as the signal propagates from the transceiver to the receiver, thereby producing accurate results. Experimental data were collected across Cyprus at 3.4 GHz and compared with three existing path loss models. The fuzzy-logic path loss prediction model was then developed and compared with the experimental data and with each of the theoretical empirical models, the newly developed model predicted signal loss with the greatest accuracy as it gives the lowest root-mean-square error. The newly developed model is very efficient for signal propagation and path loss prediction.
文摘Road transport is been used for moving people and all kinds of goods throughout the world. However, it is one mode of transportation that is prone to accidents and it faces a plethora of never-ending challenges, such as the frequent loss of lives and valuables when accident occurs. The best course of action to handle these issues is to set up an autonomous incident detection system using wireless communication, 5G technologies and the Internet of Things. IoT is a seamless technology that increases the connectivity between humans and machines. It is web-based, and improves communication between vehicle to vehicle, vehicle to infrastructures, transfer of data and information to predict incident occurrences through various networks and frameworks such as eCall, OneM2M and integration of mobile broadband. Additionally, internet of things is being adopted for public safety;for instance, it can speed up first responders’ response times to situations by displaying the best routes to a scene of an accident. The rapid development of 5G is happening in parallel with developments of internet of things (IoT), artificial intelligence (AI), and smart platforms for novel applications such as mission-critical communications. 5G is a new generation technology that operates on the Ultra High Spectrum Band UHSB. It is an innovation that uses the pedestrians-vehicle-road-cloud, and the communication between vehicle locations and temperature of high-quality connection. It is essential for intelligent transport systems because it allows for information sharing, prediction of incidences as safety is the primary concern of road transport. This review examines accident detection through 5G technology, integrated mobile broadband, and multiple inputs multiple outputs (MIMO) wireless system. Finally, we conclude by examining recent technology, challenges, present and future research trends.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R503),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological condition.It disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and balance.Traditional methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this problem.This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy.This paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat over-fitting.We use gene expression data for MS research in the GEO GSE17048 dataset.The dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the dataset.Meanwhile,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.
文摘In the current scenario,information technology is taking maximum benefits from the“Intelligent Systems and Internet of Things(IoT)”.In order to get quality articles,we have circulated the call for papers to the researchers and academician and received numbers of quality papers.However,due to scope of special issue and review reports,we were able to add papers in this issue.The vip editors are thankful to the authors for supporting a long wait and finally it is before you.The aim of the special issue was to explore ample knowledge and submit some good pieces of papers for Big Data Mining and Analytics.We have received many papers for this special issue and based on the comments of the reviewers and quality of the research papers,we have identified 7 articles for this special issue.This special issue is addressing the original research on the theory,“Intelligent Systems and Internet of Things”,with the aim to contribute better work to the researcher and academic fraternity.We are very much thankful to the authors for their support and for keeping their faith in our vip editorial process.It takes more than a year to complete this issue,we are sure that their work will be well recognized by the researchers and readers who are working in engineering and informatics domain.
文摘The Internet of Things(IoT)is one of the most prominent technologies emerged in recent years.It has a broad spectrum of applications in diverse disciplines.The IoT is evolving as a significant area of future technology and gaining much attention from various walks of life.IoT has revolutionized various application domains,such as home automation,industrial automation,medical aids,mobile healthcare,elderly assistance,intelligent energy management and smart grids,automotive,traffic management,and many others.These applications will make use of the potentially enormous amount and variety of data generated by such objects to provide new services to citizens,companies,and public administrations.
