COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different...COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.展开更多
To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been made.The main threat to food items is from diseases and pests which affect the quality and...To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been made.The main threat to food items is from diseases and pests which affect the quality and quantity of food.Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food.Still these mechanisms require manual efforts and human expertise to diagnose diseases.In the current decade Artificial Intelligence is used to automate different processes,including agricultural processes,such as automatic harvesting.Machine Learning techniques are becoming popular to process images and identify different objects.We can use Machine Learning algorithms for disease identification in plants for automatic harvesting that can help us to increase the quantity of the food produced and reduce crop losses.In this paper,we develop a novel Convolutional Neural Network(CNN)model that can detect diseases in peach plants and fruits.The proposed method can also locate the region of disease and help farmers to find appropriate treatments to protect peach crops.For the detection of diseases in Peaches VGG-19 architecture is utilized.For the localization of disease regions Mask R-CNN is utilized.The proposed technique is evaluated using different techniques and has demonstrated 94%accuracy.We hope that the system can help farmers to increase peach production to meet food demands.展开更多
In a digital world moving at a breakneck speed,consultancy services have emerged as one of the prominent resources for seeking effective,sustainable and economically viable solutions to a given crisis.The present day ...In a digital world moving at a breakneck speed,consultancy services have emerged as one of the prominent resources for seeking effective,sustainable and economically viable solutions to a given crisis.The present day consultancy services are aided by the use of multiple tools and techniques.However,ensuring the security of these tools and techniques is an important concern for the consultants because even a slight malfunction of any tool could alter the results drastically.Consultants usually tackle these functions after establishing the clients’needs and developing the appropriate strategy.Nevertheless,most of the consultants tend to focus more on the intended outcomes only and often ignore the security-specific issues.Our research study is an initiative to recommend the use of a hybrid computational technique based on fuzzy Analytical Hierarchy Process(AHP)and fuzzy Technique for Order Preference by Similarity to Ideal Solutions(TOPSIS)for prioritizing the tools and techniques that are used in consultancy services on the basis of their security features and efficacy.The empirical analysis conducted in this context shows that after implementing the assessment process,the rank of the tools and techniques obtained is:A7>A1>A4>A2>A3>A5>A6>A7,and General Electric McKinsey(GE-McKinsey)Nine-box Matrix(A7)obtained the highest rank.Thus,the outcomes show that this order of selection of the tools and techniques will give the most effective and secure services.The awareness about using the best tools and techniques in consultancy services is as important as selecting the most secure tool for solving a given problem.In this league,the results obtained in this study would be a conclusive and a reliable reference for the consultants.展开更多
This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease.In addition to forecasting the occurrences of conjunctivitis incidences,the pr...This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease.In addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble model.Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019.Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(Exponential Smoothing),HW(Holt Winter)is proposed and applied on the dataset.Predictive analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance measures.The result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased significantly.Considering the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model respectively.This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely.展开更多
The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities.The network has ...The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities.The network has unfortunately become a means to perpetuate crimes like illegal drugs and firearm trafficking,violence and terrorist activities among others.The government and law enforcement agencies are working relentlessly to control the misuse of Tor network.This is a study in the similar league,with an attempt to suggest a link-based ranking technique to rank and identify the influential hidden services in the Tor dark web.The proposed method considers the extent of connectivity to the surface web services and values of the centrality metrics of a hidden service in the web graph for ranking.The modified PageRank algorithm is used to obtain the overall rankings of the hidden services in the dataset.Several graph metrics were used to evaluate the effectiveness of the proposed technique with other commonly known ranking procedures in literature.The proposed ranking technique is shown to produce good results in identifying the influential domains in the tor network.展开更多
The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online.The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it.To aid the la...The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online.The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it.To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets,this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets.In this work,we have used partof-speech(PoS)tagged features in conjunction with n-gram models to construct the feature set for the ensemble model.We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set.The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset.The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms.This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web.展开更多
Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps ha...Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps have been found to be less durable in recent years;thus reducing their business continuity.High security features of a web application are worthless unless they provide effective services to the user and meet the standards of commercial viability.Hence,there is a necessity to link in the gap between durability and security of the web application.Indeed,security mechanisms must be used to enhance durability as well as the security of the web application.Although durability and security are not related directly,some of their factors influence each other indirectly.Characteristics play an important role in reducing the void between durability and security.In this respect,the present study identifies key characteristics of security and durability that affect each other indirectly and directly,including confidentiality,integrity availability,human trust and trustworthiness.The importance of all the attributes in terms of their weight is essential for their influence on the whole security during the development procedure of web application.To estimate the efficacy of present study,authors employed the Hesitant Fuzzy Analytic Hierarchy Process(H-Fuzzy AHP).The outcomes of our investigations and conclusions will be a useful reference for the web application developers in achieving a more secure and durable web application.展开更多
基金This work was supported by the Taif University Researchers supporting Project Number(TURSP-2020/254).
文摘COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.
基金The authors received funding for this study from Taif University Researchers Supporting Project No.(TURSP-2020/254),Taif University,Taif,Saudi Arabia.
文摘To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been made.The main threat to food items is from diseases and pests which affect the quality and quantity of food.Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food.Still these mechanisms require manual efforts and human expertise to diagnose diseases.In the current decade Artificial Intelligence is used to automate different processes,including agricultural processes,such as automatic harvesting.Machine Learning techniques are becoming popular to process images and identify different objects.We can use Machine Learning algorithms for disease identification in plants for automatic harvesting that can help us to increase the quantity of the food produced and reduce crop losses.In this paper,we develop a novel Convolutional Neural Network(CNN)model that can detect diseases in peach plants and fruits.The proposed method can also locate the region of disease and help farmers to find appropriate treatments to protect peach crops.For the detection of diseases in Peaches VGG-19 architecture is utilized.For the localization of disease regions Mask R-CNN is utilized.The proposed technique is evaluated using different techniques and has demonstrated 94%accuracy.We hope that the system can help farmers to increase peach production to meet food demands.
基金Funding for this study was received from the Taif University Researchers Supporting Projects at Taif University,Kingdom of Saudi Arabia under Grant No.TURSP-2020/254.
文摘In a digital world moving at a breakneck speed,consultancy services have emerged as one of the prominent resources for seeking effective,sustainable and economically viable solutions to a given crisis.The present day consultancy services are aided by the use of multiple tools and techniques.However,ensuring the security of these tools and techniques is an important concern for the consultants because even a slight malfunction of any tool could alter the results drastically.Consultants usually tackle these functions after establishing the clients’needs and developing the appropriate strategy.Nevertheless,most of the consultants tend to focus more on the intended outcomes only and often ignore the security-specific issues.Our research study is an initiative to recommend the use of a hybrid computational technique based on fuzzy Analytical Hierarchy Process(AHP)and fuzzy Technique for Order Preference by Similarity to Ideal Solutions(TOPSIS)for prioritizing the tools and techniques that are used in consultancy services on the basis of their security features and efficacy.The empirical analysis conducted in this context shows that after implementing the assessment process,the rank of the tools and techniques obtained is:A7>A1>A4>A2>A3>A5>A6>A7,and General Electric McKinsey(GE-McKinsey)Nine-box Matrix(A7)obtained the highest rank.Thus,the outcomes show that this order of selection of the tools and techniques will give the most effective and secure services.The awareness about using the best tools and techniques in consultancy services is as important as selecting the most secure tool for solving a given problem.In this league,the results obtained in this study would be a conclusive and a reliable reference for the consultants.
基金The authors would like to express their gratitude to Taif University,Taif,Saudi Arabia for providing administrative and technical support.This work was supported by the Taif University Researchers supporting Project number(TURSP-2020/254).
文摘This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease.In addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble model.Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019.Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(Exponential Smoothing),HW(Holt Winter)is proposed and applied on the dataset.Predictive analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance measures.The result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased significantly.Considering the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model respectively.This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely.
基金supported by Taif University Researchers Supporting Project Number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities.The network has unfortunately become a means to perpetuate crimes like illegal drugs and firearm trafficking,violence and terrorist activities among others.The government and law enforcement agencies are working relentlessly to control the misuse of Tor network.This is a study in the similar league,with an attempt to suggest a link-based ranking technique to rank and identify the influential hidden services in the Tor dark web.The proposed method considers the extent of connectivity to the surface web services and values of the centrality metrics of a hidden service in the web graph for ranking.The modified PageRank algorithm is used to obtain the overall rankings of the hidden services in the dataset.Several graph metrics were used to evaluate the effectiveness of the proposed technique with other commonly known ranking procedures in literature.The proposed ranking technique is shown to produce good results in identifying the influential domains in the tor network.
基金Funding for this study is received from the Taif University Research Supporting Projects at Taif University,Kingdom of Saudi Arabia under Grant No.TURSP-2020/254.
文摘The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online.The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it.To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets,this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets.In this work,we have used partof-speech(PoS)tagged features in conjunction with n-gram models to construct the feature set for the ensemble model.We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set.The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset.The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms.This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web.
基金funded by the Taif University Researchers Supporting Projects at Taif University,Kingdom of Saudi Arabia,under Grant Number:TURSP-2020/231.
文摘Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps have been found to be less durable in recent years;thus reducing their business continuity.High security features of a web application are worthless unless they provide effective services to the user and meet the standards of commercial viability.Hence,there is a necessity to link in the gap between durability and security of the web application.Indeed,security mechanisms must be used to enhance durability as well as the security of the web application.Although durability and security are not related directly,some of their factors influence each other indirectly.Characteristics play an important role in reducing the void between durability and security.In this respect,the present study identifies key characteristics of security and durability that affect each other indirectly and directly,including confidentiality,integrity availability,human trust and trustworthiness.The importance of all the attributes in terms of their weight is essential for their influence on the whole security during the development procedure of web application.To estimate the efficacy of present study,authors employed the Hesitant Fuzzy Analytic Hierarchy Process(H-Fuzzy AHP).The outcomes of our investigations and conclusions will be a useful reference for the web application developers in achieving a more secure and durable web application.
