Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word,if it is properly called in different accents,humans do have audio signals that are distinct from one another.A...Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word,if it is properly called in different accents,humans do have audio signals that are distinct from one another.Among the most common issues with speech,the processing is discrepancies in pronunciation,accent,and enunciation.This research study examines the issues of detecting,fixing,and summarising accent defects of average Arabic individuals in English-speaking speech.The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues.The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder.As a result,some of the most essential types of changes in pronunciation that are significant for speech recognition are performed,and accent defects defining such differences are presented.Consequently,the suggested technique increases the Speaker’s accuracy.SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository.These prerecorded sets are used to train Hidden Markov Models(HMM)as well as weighted graph systems.Their speeches are quite clear and might be considered natural.The proposed interface is optimized for use with an integrated phonetic pronounced dataset,as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects.The proposed interface detects,analyses,and assists English learners in correcting utterance faults,overcoming problems,and improving their pronunciations.展开更多
Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the wildfires.However,the power constraint of sensor nodes i...Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the wildfires.However,the power constraint of sensor nodes is one of the main design limitations of WSNs,which leads to limited operation time of nodes and late fire detection.In the past years,wireless power transfer(WPT)technology has been known as a proper solution to prolong the operation time of sensor nodes.In WPT-based mechanisms,wireless mobile chargers(WMC)are utilized to recharge the batteries of sensor nodes wirelessly.Likewise,the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge,and expensive devices.However,the high price of energy-harvesting devices hinders the use of this technology in large and dense networks,as such networks require multiple WMCs to improve the quality of service to the sensor nodes.To solve this problem,multiple power banks can be employed instead of utilizing WMCs.Furthermore,the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works.However,the sensor nodes are equipped with radio frequency(RF)technology,which allows them to exchange energy wirelessly.Consequently,critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes.Therefore,in this paper,an energy-efficient and cost-effective wireless power transmission(ECWPT)scheme is presented to improve the network lifetime and performance in forest fire detection-based systems.Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time(12.6%);network throughput(60.7%);data delivery ratio(20.9%);and network overhead(35%)as compared to previous related schemes.In conclusion,the proposed scheme significantly improves network energy efficiency for WSN.展开更多
In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English l...In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.展开更多
Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi Arabia.Makkah is one of the most important cities in Saudi Arabia that needs to be protected from des...Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi Arabia.Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification.The vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate change.The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area.In this paper,we propose an automatic desertification detection system based on Deep Learning techniques.Aerial images are classified using Convolutional Neural Networks(CNN)to detect land state variation in real-time.CNNs have been widely used for computer vision applications,such as image classification,image segmentation,and quality enhancement.The proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded implementation.It has achieved high efficiency with 96.47% accuracy.In light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.展开更多
Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should start.This research a...Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should start.This research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order calculus.In this fractional-order,differentiation is well focused on the enhancement of echocardiographic images.This provoked for developing a non-linear filter mask for image enhancement.The designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth areas.The novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and edges.Thereafter,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the image.It is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative analysis.The wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been used.The proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous methods.The values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the images.The results of the simulation validate the effectiveness of the proposed algorithm.展开更多
基金funded by the National Plan for Science,Technology and Innovation(MAARIFAH)-King Abdulaziz City for Science and Technology(KACST)-Kingdom of Saudi Arabia-Project Number(10-INF-1406-03).
文摘Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word,if it is properly called in different accents,humans do have audio signals that are distinct from one another.Among the most common issues with speech,the processing is discrepancies in pronunciation,accent,and enunciation.This research study examines the issues of detecting,fixing,and summarising accent defects of average Arabic individuals in English-speaking speech.The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues.The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder.As a result,some of the most essential types of changes in pronunciation that are significant for speech recognition are performed,and accent defects defining such differences are presented.Consequently,the suggested technique increases the Speaker’s accuracy.SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository.These prerecorded sets are used to train Hidden Markov Models(HMM)as well as weighted graph systems.Their speeches are quite clear and might be considered natural.The proposed interface is optimized for use with an integrated phonetic pronounced dataset,as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects.The proposed interface detects,analyses,and assists English learners in correcting utterance faults,overcoming problems,and improving their pronunciations.
文摘Compared with the traditional techniques of forest fires detection,wireless sensor network(WSN)is a very promising green technology in detecting efficiently the wildfires.However,the power constraint of sensor nodes is one of the main design limitations of WSNs,which leads to limited operation time of nodes and late fire detection.In the past years,wireless power transfer(WPT)technology has been known as a proper solution to prolong the operation time of sensor nodes.In WPT-based mechanisms,wireless mobile chargers(WMC)are utilized to recharge the batteries of sensor nodes wirelessly.Likewise,the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge,and expensive devices.However,the high price of energy-harvesting devices hinders the use of this technology in large and dense networks,as such networks require multiple WMCs to improve the quality of service to the sensor nodes.To solve this problem,multiple power banks can be employed instead of utilizing WMCs.Furthermore,the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works.However,the sensor nodes are equipped with radio frequency(RF)technology,which allows them to exchange energy wirelessly.Consequently,critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes.Therefore,in this paper,an energy-efficient and cost-effective wireless power transmission(ECWPT)scheme is presented to improve the network lifetime and performance in forest fire detection-based systems.Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time(12.6%);network throughput(60.7%);data delivery ratio(20.9%);and network overhead(35%)as compared to previous related schemes.In conclusion,the proposed scheme significantly improves network energy efficiency for WSN.
文摘In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.
基金by Makkah Digital Gate Initiative under grant no.(MDP-IRI-3-2020).
文摘Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi Arabia.Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification.The vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate change.The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area.In this paper,we propose an automatic desertification detection system based on Deep Learning techniques.Aerial images are classified using Convolutional Neural Networks(CNN)to detect land state variation in real-time.CNNs have been widely used for computer vision applications,such as image classification,image segmentation,and quality enhancement.The proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded implementation.It has achieved high efficiency with 96.47% accuracy.In light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.
基金This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R195),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should start.This research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order calculus.In this fractional-order,differentiation is well focused on the enhancement of echocardiographic images.This provoked for developing a non-linear filter mask for image enhancement.The designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth areas.The novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and edges.Thereafter,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the image.It is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative analysis.The wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been used.The proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous methods.The values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the images.The results of the simulation validate the effectiveness of the proposed algorithm.
