In this modern era, platforms for digital/social media and video games are growing daily. People are becoming dependent on them from all ages and with many positive aspects, but there are drawbacks as well, one of whi...In this modern era, platforms for digital/social media and video games are growing daily. People are becoming dependent on them from all ages and with many positive aspects, but there are drawbacks as well, one of which is cyberbullying. Cyberbullying is a form of bullying that uses technological platforms to bully others. It has effects on victims mentally, emotionally, and physically, which include low self-esteem, acting violently, despair, increased stress/anxiety, depression, self-harming/suicide, etc. Findings from this research study justify that it affects young people more, impacting their emotional development and overall safety. Real-time cyberbullying detection identifies and protects the target from further abuse and its effects. This study aids in determining the seriousness of the issue and the vulnerabilities that individuals can take advantage of to bully others. Additionally, it will help to understand how various features of cyberbullying detection function assist in developing a strong and trustworthy system and making a healthy online community. Natural Language Processing (NLP) models assess the textual content and analyze hashtags and comments. Similarly, image context is analyzed using Optical Character Recognition (OCR), which converts images into a machine-readable format for further examination. There are also Deep Neural Network models, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM). CNN is utilized for text/picture classification, LSTM is used for long-term dependency learning, and BLSTM expands the network’s input by encoding data in both forward and backward directions. Classifiers like Support Vector Machine (SVM) and Naïve Bayes help detect cyberbullying. A working cyberbullying detection system can detect cyberbullying on multiple platforms. A deeper understanding of each machine learning algorithm allows one to build a model that improves upon their predecessors. With models being developed for different attributes providing results with high accuracy, the cyberbullying detection system contributes by leading us to a healthier online community.展开更多
In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the bio...In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.展开更多
Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and ...Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.展开更多
This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable ...This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.展开更多
In today’s digital background,sentiment analysis has become an essential factor of Natural Language Processing(NLP),offering valuable insights from vast online data sources.This paper presents a comparative analysis ...In today’s digital background,sentiment analysis has become an essential factor of Natural Language Processing(NLP),offering valuable insights from vast online data sources.This paper presents a comparative analysis of sentiment analysis techniques leveraging machine learning.As digital content continues to expand rapidly,decoding public sentiment has become increasingly important for businesses and researchers.The study examines various approaches,including traditional machine learning methods like Support Vector Machines(SVM)and Na飗e Bayes(NB),as well as deep learning models such as Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks.It also explores hybrid frameworks that combine the strength of both paradigms.By evaluating the advantages and limitations of these models,especially within the context of e-commerce,this review provides a comprehensive understanding of their performance.Additionally,the paper addresses critical challenges such as real-time sentiment detection and multi-label classification.Through a synthesis of existing research,it highlights promising directions for future work and contributes to the development of more accurate and practical sentiment analysis solutions across various applications.展开更多
Universal quantum computers are far from achieving practical applications.The D-Wave quantum computer is initially designed for combinatorial optimizations.Therefore,exploring the potential applications of the D-Wave ...Universal quantum computers are far from achieving practical applications.The D-Wave quantum computer is initially designed for combinatorial optimizations.Therefore,exploring the potential applications of the D-Wave device in the field of cryptography is of great importance.First,although we optimize the general quantum Hamiltonian on the basis of the structure of the multiplication table(factor up to 1005973),this study attempts to explore the simplification of Hamiltonian derived from the binary structure of the integers to be factored.A simple factorization on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the D-Wave device.Second,by using the quantum computing cryptography based on the D-Wave 2000 Q system,this research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired Simulated Annealing(QISA)framework.Good functions and a high-performance platform are introduced,and additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found.Further comparison between QISA and Quantum Annealing(QA)on six-variable bent functions not only shows the potential speedup of QA,but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a practical cryptography design.展开更多
The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challeng...The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity.Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats.Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural network.This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication.The proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.展开更多
文摘In this modern era, platforms for digital/social media and video games are growing daily. People are becoming dependent on them from all ages and with many positive aspects, but there are drawbacks as well, one of which is cyberbullying. Cyberbullying is a form of bullying that uses technological platforms to bully others. It has effects on victims mentally, emotionally, and physically, which include low self-esteem, acting violently, despair, increased stress/anxiety, depression, self-harming/suicide, etc. Findings from this research study justify that it affects young people more, impacting their emotional development and overall safety. Real-time cyberbullying detection identifies and protects the target from further abuse and its effects. This study aids in determining the seriousness of the issue and the vulnerabilities that individuals can take advantage of to bully others. Additionally, it will help to understand how various features of cyberbullying detection function assist in developing a strong and trustworthy system and making a healthy online community. Natural Language Processing (NLP) models assess the textual content and analyze hashtags and comments. Similarly, image context is analyzed using Optical Character Recognition (OCR), which converts images into a machine-readable format for further examination. There are also Deep Neural Network models, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM). CNN is utilized for text/picture classification, LSTM is used for long-term dependency learning, and BLSTM expands the network’s input by encoding data in both forward and backward directions. Classifiers like Support Vector Machine (SVM) and Naïve Bayes help detect cyberbullying. A working cyberbullying detection system can detect cyberbullying on multiple platforms. A deeper understanding of each machine learning algorithm allows one to build a model that improves upon their predecessors. With models being developed for different attributes providing results with high accuracy, the cyberbullying detection system contributes by leading us to a healthier online community.
文摘In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.
基金the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,for funding this work under the Research Groups Funding Program Grant Code Number(NU/RG/SERC/12/43).
文摘Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.
文摘This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.
文摘In today’s digital background,sentiment analysis has become an essential factor of Natural Language Processing(NLP),offering valuable insights from vast online data sources.This paper presents a comparative analysis of sentiment analysis techniques leveraging machine learning.As digital content continues to expand rapidly,decoding public sentiment has become increasingly important for businesses and researchers.The study examines various approaches,including traditional machine learning methods like Support Vector Machines(SVM)and Na飗e Bayes(NB),as well as deep learning models such as Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks.It also explores hybrid frameworks that combine the strength of both paradigms.By evaluating the advantages and limitations of these models,especially within the context of e-commerce,this review provides a comprehensive understanding of their performance.Additionally,the paper addresses critical challenges such as real-time sentiment detection and multi-label classification.Through a synthesis of existing research,it highlights promising directions for future work and contributes to the development of more accurate and practical sentiment analysis solutions across various applications.
基金supported by the Special Zone Project of National Defense Innovation,the National Natural Science Foundation of China(Nos.61572304 and 61272096)the Key Program of the National Natural Science Foundation of China(No.61332019)+2 种基金the Shanghai Sailing Plan of“Science and Technology Innovation Action Plan”(No.21YF1415100)Fujian Provincial Natural Science Foundation Project(No.2021J01129)Open Research Fund of State Key Laboratory of Cryptology。
文摘Universal quantum computers are far from achieving practical applications.The D-Wave quantum computer is initially designed for combinatorial optimizations.Therefore,exploring the potential applications of the D-Wave device in the field of cryptography is of great importance.First,although we optimize the general quantum Hamiltonian on the basis of the structure of the multiplication table(factor up to 1005973),this study attempts to explore the simplification of Hamiltonian derived from the binary structure of the integers to be factored.A simple factorization on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the D-Wave device.Second,by using the quantum computing cryptography based on the D-Wave 2000 Q system,this research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired Simulated Annealing(QISA)framework.Good functions and a high-performance platform are introduced,and additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found.Further comparison between QISA and Quantum Annealing(QA)on six-variable bent functions not only shows the potential speedup of QA,but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a practical cryptography design.
文摘The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity.Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats.Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural network.This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication.The proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.
