Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework...Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.展开更多
This paper addresses the challenge of energy-conscious scheduling in modern manufacturing by formulating and solving the Energy-Conscious Flexible Job Shop Scheduling Problem.In this problem,each job has a fixed seque...This paper addresses the challenge of energy-conscious scheduling in modern manufacturing by formulating and solving the Energy-Conscious Flexible Job Shop Scheduling Problem.In this problem,each job has a fixed sequence of operations to be performed on parallel machines,and each operation can be assigned to any capable machine.The problem statement aims to schedule every job in a way that minimizes the total energy consumption of the job shop.The paper’s primary objective is to develop a reinforcement learning-based scheduling framework using the Advantage Actor-Critic algorithm to generate energy-efficient schedules that are computationally fast and feasible across diverse job shop scenarios and instance sizes.The scheduling framework captures detailed energy consumption factors,including processing,setup,transportation,idle periods,and machine turn-on events.Machines are modeled with multiple slots to enable parallel operations,and the environment accounts for energy-related dynamics such as machine shutdowns after extended idle time,limited shutdown frequency,and machine-state transitions through heat-up and cool-down phases.Experiments were conducted on 20 benchmark instances extended with three energyconscious penalty levels:the control level,moderate treatment level,and extreme condition.Results show that the proposed approach consistently produces feasible schedules across all tested benchmark instances.Relative to a MILP baseline,it achieves 30%–80% lower energy consumption on larger instances,maintains 100% feasibility(vs.MILP’s 75%),and solves each instance in under 0.47 s.This work contributes to sustainable and intelligent manufacturing practices,supporting the objectives of Industry 4.0.展开更多
The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model n...The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model needs to be generated which can effectively classify the COVID and non-COVID cases.In this work,our aim is to develop a diagnostic model based on deep features using effectiveness of Chest X-ray(CXR)in distinguishing COVID from non-COVID cases.The proposed diagnostic framework utilizes CXR to diagnose COVID-19 and includes Grad-CAM visualizations for a visual interpretation of predicted images.The model’s performance was evaluated using various metrics,including accuracy,precision,recall,F1-score,and Gmean.Several machine learning models,such as random forest,dense neural network,SVM,twin SVM,extreme learning machine,random vector functional link,and kernel ridge regression,were selected to diagnose COVID-19 cases.Transfer learning was used to extract deep features.For feature extraction many CNN-based models such as Inception V3,MobileNet,ResNet50,VGG16 and Xception models are used.It was evident from the experiments that ResNet50 architecture outperformed all other CNN architectures based on AUC.The TWSVM classifier achieved the highest AUC score of 0.98 based on the ResNet50 feature vector.展开更多
In this study, the levels of meteorological parameters like maximum temperature (°F), relative temperature (°F), minimum temperature (°F), humidity (%), dew point (°F), wind speed (mph), rainfall (...In this study, the levels of meteorological parameters like maximum temperature (°F), relative temperature (°F), minimum temperature (°F), humidity (%), dew point (°F), wind speed (mph), rainfall (in), and air pressure (in) were analyzed for all three COVID-19 pandemic waves in the NCT of Delhi, India. After doing statistical analysis, the results showed that only a few parameters, like temperature (maximum, minimum, and relative), dew point, humidity, and air pressure, were linked to the start of COVID-19 pandemic waves, and rainfall had nothing to do with COVID-19 during any of the three waves. So, according to the results of this study, the Indian government should take strict steps to stop the spread of the fourth wave of COVID-19 and any other diseases that can spread in urban areas based on the meteorological conditions.展开更多
IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices...IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems.展开更多
This paper reports the results of an investigation carried out on clay soil stabilized with pond ash(PA),rice husk ash(RHA) and cement. Modified Proctor compaction tests were performed in order to investigate the comp...This paper reports the results of an investigation carried out on clay soil stabilized with pond ash(PA),rice husk ash(RHA) and cement. Modified Proctor compaction tests were performed in order to investigate the compaction behavior of clay, and California bearing ratio(CBR) tests were performed to determine the strength characteristics of clay. For evaluation purpose, the specimens containing different amounts of admixtures were prepared. Clay was replaced with PA and RHA at a dosage of 30%e45% and5%e20%, respectively. The influence of stabilizer types and dosages on mechanical properties of clay was evaluated. In order to study the surface morphology and crystallization characteristics of the soil samples, scanning electron microscopy(SEM) and X-ray diffraction(XRD) analyses were carried out,respectively. The results obtained indicated a decrease in the maximum dry density(MDD) and a simultaneous increase in the optimum moisture content(OMC) with the addition of PA and RHA.Multiple linear regression analysis(MLRA) showed that the predicted values of CBR tests are in good agreement with the experimental values. Developed stabilized soil mixtures showed satisfactory strength and can be used for construction of embankments and stabilization of sub-grade soil. The use of locally available soils, PA, RHA, and cement in the production of stabilized soils for such applications can provide sustainability for the local construction industry.展开更多
Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The ...Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature extraction.An image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test set.In the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features.An image classification will then be implemented by combining a supervised learning model with a support vector machine.The proposed framework is developed based on MATLAB with a graphical user interface.According to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy leaves.Moreover,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.展开更多
EndoSheath bronchoscopy（Vision Sciences,Inc.） uses a sterile,disposable microbial barrier that may meet the growing needs for safe,efficient,and cost effective flexible bronchoscopy.The purpose of this open-label co...EndoSheath bronchoscopy（Vision Sciences,Inc.） uses a sterile,disposable microbial barrier that may meet the growing needs for safe,efficient,and cost effective flexible bronchoscopy.The purpose of this open-label comparative study was to compare and calculate the costs-per-airway-procedure of the reusable fiberscope when used with and without EndoSheath Technology;and to record the turnover time from the completion of the use of each scope until its readiness again for the next use.Seventy-five new patients’ airways requiring airway maneuvers and manipulations with Vision Sciences,Inc.,reusable fiberscope with EndoSheath Technology were evaluated for the costs comparisons with reassessed historical costs data for Olympus scope assisted tracheal intubations.As compared to costs of an intubation（$158.50） with Olympus scope at our institute,the intubation costs with Vision Sciences,Inc.,reusable fiberscope with EndoSheath technology was $81.50（P 〈 0.001）.The mean turnover time was 5.44 min with EndoSheath technology as compared to previously reported 30 min with Olympus fiberscope（P 〈 0.001）.Based on our institutional experience,Vision Sciences,Inc.,reusable fiberscope with EndoSheath technology is significantly cost effective as compared to the Olympus scope with significantly improved turnover times.展开更多
In this paper,the governing equations of linear,isotropic,homogeneous and generalized micropolar thermoelasticity are specialized in a plane.The governing equations are solved for plane harmonic wave solutions.Two sep...In this paper,the governing equations of linear,isotropic,homogeneous and generalized micropolar thermoelasticity are specialized in a plane.The governing equations are solved for plane harmonic wave solutions.Two separate velocity equations are obtained which indicate the existence of four plane waves with distinct speeds.A problem on reflection of plane waves from a thermally insulated/isothermal surface is considered with impedance boundary conditions.Appropriate potentials for incident and reflected waves are formulated which satisfy the boundary conditions at a plane surface.Relations between reflection coefficients as well as the expressions of energy ratios for various reflected waves are obtained.For illustration,the reflection coefficients and energy ratios of reflected waves are computed for relevant material parameters of an aluminium-epoxy composite.Effect of impedance parameters on all reflected waves is shown graphically at each angle of incidence.展开更多
Companies are eager to have a smart supply chain especially when they have adynamic system.Industry 4.0 is a concept which concentrates on mobility andreal-time integration.Thus,it can be considered as a necessary com...Companies are eager to have a smart supply chain especially when they have adynamic system.Industry 4.0 is a concept which concentrates on mobility andreal-time integration.Thus,it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem.The aim of thisresearch is to solve large-scale DVRP(LSDVRP)in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle.In LSDVRP,it isdifficult to get an exact solution and the computational time complexity growsexponentially.To find near-optimal answers for this problem,a hierarchicalapproach consisting of three stages:“clustering,route-construction,routeimprovement”is proposed.The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results.The resultsconfirmed that the proposed methodology is applicable.展开更多
Industry 4.0 is a concept that assists companies in developing a modern supply chain(MSC)system when they are faced with a dynamic process.Because Industry 4.0 focuses on mobility and real-time integration,it is a goo...Industry 4.0 is a concept that assists companies in developing a modern supply chain(MSC)system when they are faced with a dynamic process.Because Industry 4.0 focuses on mobility and real-time integration,it is a good framework for a dynamic vehicle routing problem(DVRP).This research works on DVRP.The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot.Meanwhile,new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders.This paper presents a two-stage hybrid algorithm for solving the DVRP.In the first stage,construction algorithms are applied to develop the initial route.In the second stage,improvement algorithms are applied.Experimental results were designed for different sizes of problems.Analysis results show the effectiveness of the proposed algorithm.展开更多
Our radiotherapy department specializes in all types of conformal therapy including stereotactic body radiotherapy,stereotactic radiosurgery and brachytherapy including interstitial and prostate seed implant.The depar...Our radiotherapy department specializes in all types of conformal therapy including stereotactic body radiotherapy,stereotactic radiosurgery and brachytherapy including interstitial and prostate seed implant.The department also includes a dedicated four-bed day care unit attached to the integrated brachytherapy suite.It is managed by a resident doctor,along with four staff nurses under the supervision of a radiation oncologist.We performed an audit of our day care unit to gain insight into its utilization and needs.展开更多
基金King Saud University,Grant/Award Number:RSP2024R157。
文摘Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.
基金funded in part by theU.S.Department of Energy(DOE)Office of Energy Efficiency and Renewable Energy’s Advanced Manufacturing Office(AMO)through the Industrial Training and Assessment Center(ITAC)program.
文摘This paper addresses the challenge of energy-conscious scheduling in modern manufacturing by formulating and solving the Energy-Conscious Flexible Job Shop Scheduling Problem.In this problem,each job has a fixed sequence of operations to be performed on parallel machines,and each operation can be assigned to any capable machine.The problem statement aims to schedule every job in a way that minimizes the total energy consumption of the job shop.The paper’s primary objective is to develop a reinforcement learning-based scheduling framework using the Advantage Actor-Critic algorithm to generate energy-efficient schedules that are computationally fast and feasible across diverse job shop scenarios and instance sizes.The scheduling framework captures detailed energy consumption factors,including processing,setup,transportation,idle periods,and machine turn-on events.Machines are modeled with multiple slots to enable parallel operations,and the environment accounts for energy-related dynamics such as machine shutdowns after extended idle time,limited shutdown frequency,and machine-state transitions through heat-up and cool-down phases.Experiments were conducted on 20 benchmark instances extended with three energyconscious penalty levels:the control level,moderate treatment level,and extreme condition.Results show that the proposed approach consistently produces feasible schedules across all tested benchmark instances.Relative to a MILP baseline,it achieves 30%–80% lower energy consumption on larger instances,maintains 100% feasibility(vs.MILP’s 75%),and solves each instance in under 0.47 s.This work contributes to sustainable and intelligent manufacturing practices,supporting the objectives of Industry 4.0.
文摘The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model needs to be generated which can effectively classify the COVID and non-COVID cases.In this work,our aim is to develop a diagnostic model based on deep features using effectiveness of Chest X-ray(CXR)in distinguishing COVID from non-COVID cases.The proposed diagnostic framework utilizes CXR to diagnose COVID-19 and includes Grad-CAM visualizations for a visual interpretation of predicted images.The model’s performance was evaluated using various metrics,including accuracy,precision,recall,F1-score,and Gmean.Several machine learning models,such as random forest,dense neural network,SVM,twin SVM,extreme learning machine,random vector functional link,and kernel ridge regression,were selected to diagnose COVID-19 cases.Transfer learning was used to extract deep features.For feature extraction many CNN-based models such as Inception V3,MobileNet,ResNet50,VGG16 and Xception models are used.It was evident from the experiments that ResNet50 architecture outperformed all other CNN architectures based on AUC.The TWSVM classifier achieved the highest AUC score of 0.98 based on the ResNet50 feature vector.
文摘In this study, the levels of meteorological parameters like maximum temperature (°F), relative temperature (°F), minimum temperature (°F), humidity (%), dew point (°F), wind speed (mph), rainfall (in), and air pressure (in) were analyzed for all three COVID-19 pandemic waves in the NCT of Delhi, India. After doing statistical analysis, the results showed that only a few parameters, like temperature (maximum, minimum, and relative), dew point, humidity, and air pressure, were linked to the start of COVID-19 pandemic waves, and rainfall had nothing to do with COVID-19 during any of the three waves. So, according to the results of this study, the Indian government should take strict steps to stop the spread of the fourth wave of COVID-19 and any other diseases that can spread in urban areas based on the meteorological conditions.
文摘IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems.
文摘This paper reports the results of an investigation carried out on clay soil stabilized with pond ash(PA),rice husk ash(RHA) and cement. Modified Proctor compaction tests were performed in order to investigate the compaction behavior of clay, and California bearing ratio(CBR) tests were performed to determine the strength characteristics of clay. For evaluation purpose, the specimens containing different amounts of admixtures were prepared. Clay was replaced with PA and RHA at a dosage of 30%e45% and5%e20%, respectively. The influence of stabilizer types and dosages on mechanical properties of clay was evaluated. In order to study the surface morphology and crystallization characteristics of the soil samples, scanning electron microscopy(SEM) and X-ray diffraction(XRD) analyses were carried out,respectively. The results obtained indicated a decrease in the maximum dry density(MDD) and a simultaneous increase in the optimum moisture content(OMC) with the addition of PA and RHA.Multiple linear regression analysis(MLRA) showed that the predicted values of CBR tests are in good agreement with the experimental values. Developed stabilized soil mixtures showed satisfactory strength and can be used for construction of embankments and stabilization of sub-grade soil. The use of locally available soils, PA, RHA, and cement in the production of stabilized soils for such applications can provide sustainability for the local construction industry.
文摘Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature extraction.An image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test set.In the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features.An image classification will then be implemented by combining a supervised learning model with a support vector machine.The proposed framework is developed based on MATLAB with a graphical user interface.According to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy leaves.Moreover,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.
基金partially funded by Vision Sciences,Inc.,Orangeburg,New York,USA.No financial interests were reported
文摘EndoSheath bronchoscopy（Vision Sciences,Inc.） uses a sterile,disposable microbial barrier that may meet the growing needs for safe,efficient,and cost effective flexible bronchoscopy.The purpose of this open-label comparative study was to compare and calculate the costs-per-airway-procedure of the reusable fiberscope when used with and without EndoSheath Technology;and to record the turnover time from the completion of the use of each scope until its readiness again for the next use.Seventy-five new patients’ airways requiring airway maneuvers and manipulations with Vision Sciences,Inc.,reusable fiberscope with EndoSheath Technology were evaluated for the costs comparisons with reassessed historical costs data for Olympus scope assisted tracheal intubations.As compared to costs of an intubation（$158.50） with Olympus scope at our institute,the intubation costs with Vision Sciences,Inc.,reusable fiberscope with EndoSheath technology was $81.50（P 〈 0.001）.The mean turnover time was 5.44 min with EndoSheath technology as compared to previously reported 30 min with Olympus fiberscope（P 〈 0.001）.Based on our institutional experience,Vision Sciences,Inc.,reusable fiberscope with EndoSheath technology is significantly cost effective as compared to the Olympus scope with significantly improved turnover times.
文摘In this paper,the governing equations of linear,isotropic,homogeneous and generalized micropolar thermoelasticity are specialized in a plane.The governing equations are solved for plane harmonic wave solutions.Two separate velocity equations are obtained which indicate the existence of four plane waves with distinct speeds.A problem on reflection of plane waves from a thermally insulated/isothermal surface is considered with impedance boundary conditions.Appropriate potentials for incident and reflected waves are formulated which satisfy the boundary conditions at a plane surface.Relations between reflection coefficients as well as the expressions of energy ratios for various reflected waves are obtained.For illustration,the reflection coefficients and energy ratios of reflected waves are computed for relevant material parameters of an aluminium-epoxy composite.Effect of impedance parameters on all reflected waves is shown graphically at each angle of incidence.
文摘Companies are eager to have a smart supply chain especially when they have adynamic system.Industry 4.0 is a concept which concentrates on mobility andreal-time integration.Thus,it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem.The aim of thisresearch is to solve large-scale DVRP(LSDVRP)in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle.In LSDVRP,it isdifficult to get an exact solution and the computational time complexity growsexponentially.To find near-optimal answers for this problem,a hierarchicalapproach consisting of three stages:“clustering,route-construction,routeimprovement”is proposed.The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results.The resultsconfirmed that the proposed methodology is applicable.
文摘Industry 4.0 is a concept that assists companies in developing a modern supply chain(MSC)system when they are faced with a dynamic process.Because Industry 4.0 focuses on mobility and real-time integration,it is a good framework for a dynamic vehicle routing problem(DVRP).This research works on DVRP.The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot.Meanwhile,new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders.This paper presents a two-stage hybrid algorithm for solving the DVRP.In the first stage,construction algorithms are applied to develop the initial route.In the second stage,improvement algorithms are applied.Experimental results were designed for different sizes of problems.Analysis results show the effectiveness of the proposed algorithm.
文摘Our radiotherapy department specializes in all types of conformal therapy including stereotactic body radiotherapy,stereotactic radiosurgery and brachytherapy including interstitial and prostate seed implant.The department also includes a dedicated four-bed day care unit attached to the integrated brachytherapy suite.It is managed by a resident doctor,along with four staff nurses under the supervision of a radiation oncologist.We performed an audit of our day care unit to gain insight into its utilization and needs.
