With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,l...With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.展开更多
In recent years,the infrastructure,instruments,and resources of network systems are becoming more complex and heterogeneous,with the rapid development of current internet and mobile communication technologies.In order...In recent years,the infrastructure,instruments,and resources of network systems are becoming more complex and heterogeneous,with the rapid development of current internet and mobile communication technologies.In order to efficaciously prepare,control,hold and optimize networking systems,greater intelligence needs to be deployed.However,due to the inherently dispensed characteristic of conventional networks,Machine Learning(ML)techniques are hard to implement and deployed to govern and operate networks.Software-Defined Networking(SDN)brings us new possibilities to offer intelligence in the networks.SDN’s characteristics(e.g.,logically centralized control,global network view,software-based site visitor analysis,and dynamic updating of forwarding rules)make it simpler to apply machine learning strategies.Various perspectives of fiber-optic communications including fiber nonlinearity coverage,optical performance checking,cognitive shortcoming detection/anticipation,and arranging and improvement of softwaredefined networks are examined in Machine Learning(ML)applications.This research paper has presented an imaginative framework concept called Intelligent Software Defined Network(ISDN)for Cognitive Routing Optimization(CRO)using Deep Extreme Learning Machine(DELM)approach(ISDN-CRO-DELM)in light of the new challenges in the development and operation of communication systems,and capturing motivation from how living creatures deal with difficulty and usability.The proposed methodology develops around the planned applications of progressive DELM methods and,specifically,probabilistic generative models for framework wide learning,demonstrating,improvement,and information description.Furthermore,ISDN-CRO-DELM,suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level.MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.展开更多
Using the asymptotic iteration method, we obtain the S-wave solution for a short-range three-parameter central potential with 1/r singularity and with a non-orbital barrier. To the best of our knowledge, this is the f...Using the asymptotic iteration method, we obtain the S-wave solution for a short-range three-parameter central potential with 1/r singularity and with a non-orbital barrier. To the best of our knowledge, this is the first attempt at calculating the energy spectrum for this potential, which was introduced by H. Bahlouli and A. D. Alhaidari and for which they obtained the “potential parameter spectrum”. Our results are also independently verified using a direct method of diagonalizing the Hamiltonian matrix in the J-matrix basis.展开更多
In a recent reformulation of quantum mechanics, the properties of the physical system are derived from orthogonal polynomials that make up the expansion coefficients of the wavefunction in a complete set of square int...In a recent reformulation of quantum mechanics, the properties of the physical system are derived from orthogonal polynomials that make up the expansion coefficients of the wavefunction in a complete set of square integrable basis. Here, we show how to reconstruct the potential function so that a correspondence with the standard formulation could be established. However, the correspondence places restriction on the kinematics of such problems.展开更多
Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to loca...Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs.However,manual channel picking is both time consuming and tedious.Moreover,similar to any other process dependent on human intervention,manual channel picking is error prone and inconsistent.To address these issues,automatic channel detection is both necessary and important for efficient and accurate seismic interpretation.Modern systems make use of real-time image processing techniques for different tasks.Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies.In this paper,we propose an innovative automatic channel detection algorithm based on machine learning techniques.The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process.The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches.We provide a field data example to demonstrate the performance of the new algorithm.The training phase gave a maximum accuracy of 84.6%for the classifier and it performed even better in the testing phase,giving a maximum accuracy of 90%.展开更多
We present a formulation of quantum mechanics based on the theory of orthogonal polynomials.The wavefunction is expanded over a complete set of square integrable basis where the expansion coefficients are orthogonal p...We present a formulation of quantum mechanics based on the theory of orthogonal polynomials.The wavefunction is expanded over a complete set of square integrable basis where the expansion coefficients are orthogonal polynomials in the energy and physical parameters.Information about the corresponding physical systems(both structural and dynamical)are derived from the properties of these polynomials.We demonstrate that an advantage of this formulation is that the class of exactly solvable quantum mechanical problems becomes larger than in the conventional formulation(see,for example,table 3 in the text).We limit our investigation in this work to the Askey classification scheme of hypergeometric orthogonal polynomials and focus on the Wilson polynomial and two of its limiting cases(the Meixner–Pollaczek and continuous dual Hahn polynomials).Nonetheless,the formulation is amenable to other classes of orthogonal polynomials.展开更多
The aim of this work is to find exact solutions of the Dirac equation in(1+1) space-time beyond the already known class.We consider exact spin(and pseudo-spin) symmetric Dirac equations where the scalar potential is e...The aim of this work is to find exact solutions of the Dirac equation in(1+1) space-time beyond the already known class.We consider exact spin(and pseudo-spin) symmetric Dirac equations where the scalar potential is equal to plus(and minus) the vector potential.We also include pseudo-scalar potentials in the interaction.The spinor wavefunction is written as a bounded sum in a complete set of square integrable basis,which is chosen such that the matrix representation of the Dirac wave operator is tridiagonal and symmetric.This makes the matrix wave equation a symmetric three-term recursion relation for the expansion coefficients of the wavefunction.We solve the recursion relation exactly in terms of orthogonal polynomials and obtain the state functions and corresponding relativistic energy spectrum and phase shift.展开更多
The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of sensors.Although the IoT evolution led to quality of life enhancement,many of its devices are insecure.The lack of...The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of sensors.Although the IoT evolution led to quality of life enhancement,many of its devices are insecure.The lack of robust key management systems,efficient identity authentication,low fault tolerance,and many other issues lead to IoT devices being easily targeted by attackers.In this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network(CPANs)in an Industrial IoT(IIoT)environment.This study proposed Authenblue protocol as a new Blockchainbased authentication protocol.To enhance the authentication process and make it more secure,Authenblue modified the way of generating IIoT identifiers and the shared secret keys used by the IIoT devices to raise the efficiency of the authentication protocol.Authenblue enhance the authentication protocol that other models rely on by enhancing the approach used to generate the User Identifier(UI).The UI values changed from being static values,sensors MAC addresses,to be generated values in the inception phase.This approach makes the process of renewing the sensor keys more secure by renewing their UI values instead of changing the secret key.In this study,Authenblue has been simulated in the Network Simulator 3(NS3).Simulation results show an improved performance compared to the related work.展开更多
文摘With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.
基金supported by Data and Artificial Intelligence Scientific Chair at Umm AlQura University.
文摘In recent years,the infrastructure,instruments,and resources of network systems are becoming more complex and heterogeneous,with the rapid development of current internet and mobile communication technologies.In order to efficaciously prepare,control,hold and optimize networking systems,greater intelligence needs to be deployed.However,due to the inherently dispensed characteristic of conventional networks,Machine Learning(ML)techniques are hard to implement and deployed to govern and operate networks.Software-Defined Networking(SDN)brings us new possibilities to offer intelligence in the networks.SDN’s characteristics(e.g.,logically centralized control,global network view,software-based site visitor analysis,and dynamic updating of forwarding rules)make it simpler to apply machine learning strategies.Various perspectives of fiber-optic communications including fiber nonlinearity coverage,optical performance checking,cognitive shortcoming detection/anticipation,and arranging and improvement of softwaredefined networks are examined in Machine Learning(ML)applications.This research paper has presented an imaginative framework concept called Intelligent Software Defined Network(ISDN)for Cognitive Routing Optimization(CRO)using Deep Extreme Learning Machine(DELM)approach(ISDN-CRO-DELM)in light of the new challenges in the development and operation of communication systems,and capturing motivation from how living creatures deal with difficulty and usability.The proposed methodology develops around the planned applications of progressive DELM methods and,specifically,probabilistic generative models for framework wide learning,demonstrating,improvement,and information description.Furthermore,ISDN-CRO-DELM,suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level.MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.
文摘Using the asymptotic iteration method, we obtain the S-wave solution for a short-range three-parameter central potential with 1/r singularity and with a non-orbital barrier. To the best of our knowledge, this is the first attempt at calculating the energy spectrum for this potential, which was introduced by H. Bahlouli and A. D. Alhaidari and for which they obtained the “potential parameter spectrum”. Our results are also independently verified using a direct method of diagonalizing the Hamiltonian matrix in the J-matrix basis.
基金support by the Saudi Center for Theoretical Physics (SCTP) during the progress of this work
文摘In a recent reformulation of quantum mechanics, the properties of the physical system are derived from orthogonal polynomials that make up the expansion coefficients of the wavefunction in a complete set of square integrable basis. Here, we show how to reconstruct the potential function so that a correspondence with the standard formulation could be established. However, the correspondence places restriction on the kinematics of such problems.
文摘Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs.However,manual channel picking is both time consuming and tedious.Moreover,similar to any other process dependent on human intervention,manual channel picking is error prone and inconsistent.To address these issues,automatic channel detection is both necessary and important for efficient and accurate seismic interpretation.Modern systems make use of real-time image processing techniques for different tasks.Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies.In this paper,we propose an innovative automatic channel detection algorithm based on machine learning techniques.The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process.The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches.We provide a field data example to demonstrate the performance of the new algorithm.The training phase gave a maximum accuracy of 84.6%for the classifier and it performed even better in the testing phase,giving a maximum accuracy of 90%.
基金partially support by the Saudi Center for Theoretical Physics(SCTP).
文摘We present a formulation of quantum mechanics based on the theory of orthogonal polynomials.The wavefunction is expanded over a complete set of square integrable basis where the expansion coefficients are orthogonal polynomials in the energy and physical parameters.Information about the corresponding physical systems(both structural and dynamical)are derived from the properties of these polynomials.We demonstrate that an advantage of this formulation is that the class of exactly solvable quantum mechanical problems becomes larger than in the conventional formulation(see,for example,table 3 in the text).We limit our investigation in this work to the Askey classification scheme of hypergeometric orthogonal polynomials and focus on the Wilson polynomial and two of its limiting cases(the Meixner–Pollaczek and continuous dual Hahn polynomials).Nonetheless,the formulation is amenable to other classes of orthogonal polynomials.
基金King Fahd University of Petroleum and Minerals (KFUPM) for their support under research grant RG1502the material support and encouragements of the Saudi Center for Theoretical Physics (SCTP)
文摘The aim of this work is to find exact solutions of the Dirac equation in(1+1) space-time beyond the already known class.We consider exact spin(and pseudo-spin) symmetric Dirac equations where the scalar potential is equal to plus(and minus) the vector potential.We also include pseudo-scalar potentials in the interaction.The spinor wavefunction is written as a bounded sum in a complete set of square integrable basis,which is chosen such that the matrix representation of the Dirac wave operator is tridiagonal and symmetric.This makes the matrix wave equation a symmetric three-term recursion relation for the expansion coefficients of the wavefunction.We solve the recursion relation exactly in terms of orthogonal polynomials and obtain the state functions and corresponding relativistic energy spectrum and phase shift.
文摘The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of sensors.Although the IoT evolution led to quality of life enhancement,many of its devices are insecure.The lack of robust key management systems,efficient identity authentication,low fault tolerance,and many other issues lead to IoT devices being easily targeted by attackers.In this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network(CPANs)in an Industrial IoT(IIoT)environment.This study proposed Authenblue protocol as a new Blockchainbased authentication protocol.To enhance the authentication process and make it more secure,Authenblue modified the way of generating IIoT identifiers and the shared secret keys used by the IIoT devices to raise the efficiency of the authentication protocol.Authenblue enhance the authentication protocol that other models rely on by enhancing the approach used to generate the User Identifier(UI).The UI values changed from being static values,sensors MAC addresses,to be generated values in the inception phase.This approach makes the process of renewing the sensor keys more secure by renewing their UI values instead of changing the secret key.In this study,Authenblue has been simulated in the Network Simulator 3(NS3).Simulation results show an improved performance compared to the related work.
