AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during the...The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.展开更多
Laterally with the birth of the Internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal writings.Twi...Laterally with the birth of the Internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal writings.Twitter is micro-blogging short text and social networking services,with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andfields.This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event.Although sentiment examination of these texts is useful for a variety of reasons,it is typically seen as a difficult undertaking due to the fact that these messages are frequently short,informal,loud,and rich in linguistic ambiguities such as polysemy.Furthermore,most contemporary sentiment analysis algorithms are based on clean data.In this paper,we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and Inverse Document Frequency(TF-IDF)and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing noise.We also utilise the Random Forest network to detect the emotion of a tweet.To authenticate the proposed approach performance,we conduct extensive tests on publically accessible datasets,and thefindings reveal that the suggested technique significantly outperforms sentiment classification in multi-class emotion text data.展开更多
In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile u...In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of applications.Spectrum prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio spectrum.Long short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum access.The proposed scheme provides an average maximum waiting time gain of 2.88 ms.The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.展开更多
Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifical...Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network.However,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and buildings.In this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication.This RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement strategies.It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles.The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments.The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.展开更多
The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blo...The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.展开更多
In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration...In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration to distribution.Energy management from the distribution side is a critical problem for balancing load demand.A unique energy manage-ment strategy(EMS)is being developed for office building equipment.That includes renewable energy integration,automation,and control based on the Artificial Neural Network(ANN)system using Matlab Simulink.This strategy reduces electric power consumption and balances the load demand of the traditional grid.This strategy is developed by taking inputs from an office building electricity consumption behavior study,a power generation study of a solar photovoltaic system,and the supply pattern of a grid in peak and non-peak hours.All this is done in consideration of the Indian scenario,where real-time data of month-wise ANN-based intelligent switching has been established for intermittent renewable sources and peak load reduction,as well as average load reduction,has been demonstrated along with the power control loop without the battery system.展开更多
Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subject...Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.展开更多
The processor is greatly hampered by the large dataset of picture or multimedia data.The logic of approximation hardware is moving in the direction of multimedia processing with a given amount of acceptable mistake.Th...The processor is greatly hampered by the large dataset of picture or multimedia data.The logic of approximation hardware is moving in the direction of multimedia processing with a given amount of acceptable mistake.This study proposes various higher-order approximate counter-based compressor(CBC)using input shuffled 6:3 CBC.In the Wallace multiplier using a CBC is a significant factor in partial product reduction.So the design of 10-4,11-4,12-4,13-4 and 14-4 CBC are proposed in this paper using an input shuffled 6:3 compressor to attain two stage multiplications.The input shuffling aims to reduce the output combination of the 6:3 compressor from 64 to 27.Design of 15-4,10-4,9-4,and 7-3 CBCs are performed using the proposed 6:3 compressor and the results obtained are compared with the existing models.These existing models are constructed using multiplexers and 5-3 CBC.When compared to input shuffled 5-3 the proposed 6:3 compressor shows better results in terms of area,power and delay.An approximation is performed on the 6:3 compressor to further reduce the computational energy of the system which is optimal for multimedia applications.The major contribution of this work is the development of two stage multiplier using various proposed CBC.All designs of the approximate compressor(AC)and true compressor(TC)are analysed with 8 ×8 and 16 × 16 imagemultiplication.The proposed multipliers also provide adequate levels of accuracy,according to the MATLAB simulations,in addition to greater hardware efficiency.As the result approximate circuits over image processing shows the stunning performance in many deep learning network in the current research which is only oriented to multimedia.展开更多
The term“steganography”is derived from the Greek words steganos,which means“verified,concealed,or guaranteed”,and graphein,which means“writing”.The primary motivation for considering steganography is to prevent ...The term“steganography”is derived from the Greek words steganos,which means“verified,concealed,or guaranteed”,and graphein,which means“writing”.The primary motivation for considering steganography is to prevent unapproved individuals from obtaining disguised data.With the ultimate goal of comprehending the fundamental inspiration driving the steganography procedures,there should be no significant change in the example report.The Least Significant Bit(LSB)system,which is one of the methodologies for concealing propelled picture data,is examined in this assessment.In this evaluation,another procedure for data stowing indefinitely is proposed with the ultimate goal of limiting the progressions occurring in the spread record while hiding the data with the LSB technique and making the best cover to make it difficult to get concealed data.The RGB(Red,Green,and Blue)pixel esteem based stegnography technique is proposed in this proposition.The claim to fame of this calculation is that,unlike other stegnography calculations,we do not change the pixels unless absolutely necessary.展开更多
To the Editor,Diabetes mellitus(DM)patients have a higher incidence of musculoskeletal issues compared to those without diabetes.With diabetes-related complications increasing,early detection of musculoskeletal diseas...To the Editor,Diabetes mellitus(DM)patients have a higher incidence of musculoskeletal issues compared to those without diabetes.With diabetes-related complications increasing,early detection of musculoskeletal diseases is crucial for better quality of life and reduced morbidity.Limited joint mobility(LJM),also known as diabetic stiff hand or diabetic cheiroarthropathy(DCA),is an often overlooked complication of type 2 DM.It manifests as painless stiffness in hands,restricted joint movement,and tight skin.Contractures in hand joints can spread to major joints like elbows and knees,severely impacting daily tasks and quality of life.Physical examination aids in diagnosing LJM and related rheumatic conditions like scleroderma.展开更多
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
文摘The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.
文摘Laterally with the birth of the Internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal writings.Twitter is micro-blogging short text and social networking services,with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andfields.This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event.Although sentiment examination of these texts is useful for a variety of reasons,it is typically seen as a difficult undertaking due to the fact that these messages are frequently short,informal,loud,and rich in linguistic ambiguities such as polysemy.Furthermore,most contemporary sentiment analysis algorithms are based on clean data.In this paper,we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and Inverse Document Frequency(TF-IDF)and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing noise.We also utilise the Random Forest network to detect the emotion of a tweet.To authenticate the proposed approach performance,we conduct extensive tests on publically accessible datasets,and thefindings reveal that the suggested technique significantly outperforms sentiment classification in multi-class emotion text data.
文摘In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of applications.Spectrum prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio spectrum.Long short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum access.The proposed scheme provides an average maximum waiting time gain of 2.88 ms.The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.
文摘Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network.However,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and buildings.In this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication.This RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement strategies.It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles.The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments.The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.
文摘The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.
文摘In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration to distribution.Energy management from the distribution side is a critical problem for balancing load demand.A unique energy manage-ment strategy(EMS)is being developed for office building equipment.That includes renewable energy integration,automation,and control based on the Artificial Neural Network(ANN)system using Matlab Simulink.This strategy reduces electric power consumption and balances the load demand of the traditional grid.This strategy is developed by taking inputs from an office building electricity consumption behavior study,a power generation study of a solar photovoltaic system,and the supply pattern of a grid in peak and non-peak hours.All this is done in consideration of the Indian scenario,where real-time data of month-wise ANN-based intelligent switching has been established for intermittent renewable sources and peak load reduction,as well as average load reduction,has been demonstrated along with the power control loop without the battery system.
文摘Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.
文摘The processor is greatly hampered by the large dataset of picture or multimedia data.The logic of approximation hardware is moving in the direction of multimedia processing with a given amount of acceptable mistake.This study proposes various higher-order approximate counter-based compressor(CBC)using input shuffled 6:3 CBC.In the Wallace multiplier using a CBC is a significant factor in partial product reduction.So the design of 10-4,11-4,12-4,13-4 and 14-4 CBC are proposed in this paper using an input shuffled 6:3 compressor to attain two stage multiplications.The input shuffling aims to reduce the output combination of the 6:3 compressor from 64 to 27.Design of 15-4,10-4,9-4,and 7-3 CBCs are performed using the proposed 6:3 compressor and the results obtained are compared with the existing models.These existing models are constructed using multiplexers and 5-3 CBC.When compared to input shuffled 5-3 the proposed 6:3 compressor shows better results in terms of area,power and delay.An approximation is performed on the 6:3 compressor to further reduce the computational energy of the system which is optimal for multimedia applications.The major contribution of this work is the development of two stage multiplier using various proposed CBC.All designs of the approximate compressor(AC)and true compressor(TC)are analysed with 8 ×8 and 16 × 16 imagemultiplication.The proposed multipliers also provide adequate levels of accuracy,according to the MATLAB simulations,in addition to greater hardware efficiency.As the result approximate circuits over image processing shows the stunning performance in many deep learning network in the current research which is only oriented to multimedia.
文摘The term“steganography”is derived from the Greek words steganos,which means“verified,concealed,or guaranteed”,and graphein,which means“writing”.The primary motivation for considering steganography is to prevent unapproved individuals from obtaining disguised data.With the ultimate goal of comprehending the fundamental inspiration driving the steganography procedures,there should be no significant change in the example report.The Least Significant Bit(LSB)system,which is one of the methodologies for concealing propelled picture data,is examined in this assessment.In this evaluation,another procedure for data stowing indefinitely is proposed with the ultimate goal of limiting the progressions occurring in the spread record while hiding the data with the LSB technique and making the best cover to make it difficult to get concealed data.The RGB(Red,Green,and Blue)pixel esteem based stegnography technique is proposed in this proposition.The claim to fame of this calculation is that,unlike other stegnography calculations,we do not change the pixels unless absolutely necessary.
文摘To the Editor,Diabetes mellitus(DM)patients have a higher incidence of musculoskeletal issues compared to those without diabetes.With diabetes-related complications increasing,early detection of musculoskeletal diseases is crucial for better quality of life and reduced morbidity.Limited joint mobility(LJM),also known as diabetic stiff hand or diabetic cheiroarthropathy(DCA),is an often overlooked complication of type 2 DM.It manifests as painless stiffness in hands,restricted joint movement,and tight skin.Contractures in hand joints can spread to major joints like elbows and knees,severely impacting daily tasks and quality of life.Physical examination aids in diagnosing LJM and related rheumatic conditions like scleroderma.
