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
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
In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-of...In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.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.
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.
文摘In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.
