Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,...Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their goods.Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years.The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones.This paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other things.Online reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are dynamic.Then,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave Bayes.The performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics.展开更多
Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine ...Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.展开更多
One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna structures.By...One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna structures.By combining the multi-input multi-output(MIMO)antenna structure with non-orthogonal multiple access(NOMA),which is a new multiplexing method,the fading effects of the channels are not only reduced but also high data rate transmission is ensured.However,when the maximum likelihood(ML)algorithm that has high performance on coherent detection,is used as a symbol detector in MIMO NOMA systems,the computational complexity of the system increases due to higher-order constellations and antenna sizes.As a result,the implementation of this algorithm will be impractical.In this study,the backtracking search algorithm(BSA)is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems.To emphasize the efficiency of the proposed algorithm,simulations have been made for the system with various antenna sizes.As can be seen from the obtained results,a considerable reduction in complexity has occurred using BSA compared to the ML algorithm,also the bit error performance of the system is increased compared to other algorithms.展开更多
文摘Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their goods.Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years.The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones.This paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other things.Online reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are dynamic.Then,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave Bayes.The performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics.
文摘Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.
基金supported by the Scientific Research Projects Coordination Unit of Bandirma Onyedi Eylül University.Project Number BAP-19-MF-1004-005.
文摘One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna structures.By combining the multi-input multi-output(MIMO)antenna structure with non-orthogonal multiple access(NOMA),which is a new multiplexing method,the fading effects of the channels are not only reduced but also high data rate transmission is ensured.However,when the maximum likelihood(ML)algorithm that has high performance on coherent detection,is used as a symbol detector in MIMO NOMA systems,the computational complexity of the system increases due to higher-order constellations and antenna sizes.As a result,the implementation of this algorithm will be impractical.In this study,the backtracking search algorithm(BSA)is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems.To emphasize the efficiency of the proposed algorithm,simulations have been made for the system with various antenna sizes.As can be seen from the obtained results,a considerable reduction in complexity has occurred using BSA compared to the ML algorithm,also the bit error performance of the system is increased compared to other algorithms.
