The novel zirconium oxide, nickel oxide and zinc oxide nanoparticles supported activated carbons（Zr-AC, Ni-AC, Zn-AC） were successfully fabricated through microwave irradiation method. The synthesized nanoparticles ...The novel zirconium oxide, nickel oxide and zinc oxide nanoparticles supported activated carbons（Zr-AC, Ni-AC, Zn-AC） were successfully fabricated through microwave irradiation method. The synthesized nanoparticles were characterized using XRD, HR-SEM, XPS and BET. The optical properties of Zr-AC, Ni-AC and Zn-AC composites were investigated using UV–Vis diffuse reflectance spectroscopy. The photocatalytic efficiency was verified in the degradation of textile dyeing wastewater（TDW） in UV light irradiation. The chemical oxygen demand（COD） of TDW was observed at regular intervals to calculate the removal rate of COD. Zn-AC composites showed impressive photocatalytic enrichment, which can be ascribed to the enhanced absorbance in the UV light region, the effective adsorptive capacity to dye molecules, the assisted charge transfer and the inhibited recombination of electron-hole pairs. The maximum TDW degradation（82% COD removal） was achieved with Zn-AC. A possible synergy mechanism on the surface of Zn-AC was also designed. Zn-AC could be reused five times without exceptional loss of its activity.展开更多
Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of th...Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam optimization.The key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box models.The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies.展开更多
In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation process.Since the conventional irrigation system needs massive quantity of wat...In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation process.Since the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.展开更多
Metal matrix composites reinforced with graphite particles provide better machinability and tribological properties. The present study attempts to find the optimal level of machining parameters for multi-performance c...Metal matrix composites reinforced with graphite particles provide better machinability and tribological properties. The present study attempts to find the optimal level of machining parameters for multi-performance characteristics in turning of Al-SiC-Gr hybrid composites using grey-fuzzy algorithm. The hybrid composites with 5%, 7.5% and 10% combined equal mass fraction of SiC-Gr particles were used for the study and their corresponding tensile strength values are 170, 210, 204 MPa respectively. Al-10%（SiC-Gr） hybrid composite provides better machinability when compared with composites with 5% and 7.5% of SiC-Gr. Grey-fuzzy logic approach offers improved grey-fuzzy reasoning grade and has less uncertainties in the output when compared with grey relational technique. The confirmatory test reveals an increase in grey-fuzzy reasoning grade from 0.619 to 0.891, which substantiates the improvement in multi-performance characteristics at the optimal level of process parameters setting.展开更多
Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images ...Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.展开更多
Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the con...Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.展开更多
In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare applications.IoT gadgets generate huge volumes of patient data in healthcare domain,which can be ex...In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare applications.IoT gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile gadgets.Chronic Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the globe.The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as FPA-DNN.The steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and classification.Primarily,the IoT gadgets are utilized in the collection of a patient’s health information.The proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed data.The application of FPA helps in tuning the DNN parameters for better classification performance.The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset.The results were examined under different aspects.The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.展开更多
A thrust for looking multifunctional materials for applications in civil engineering structures has attracted interest among researchers across the globe.Cement based Ba0.85Ca0.15Zr0.1Ti0.88Sn0.02O3(BCZT.Sn)composites...A thrust for looking multifunctional materials for applications in civil engineering structures has attracted interest among researchers across the globe.Cement based Ba0.85Ca0.15Zr0.1Ti0.88Sn0.02O3(BCZT.Sn)composites were prepared for electrocaloric applications with varying BCZT.Sn to cement ratio.Hysteresis loops showed some signature of saturation in cement composites.However,loops of pure sample were saturated due to its ferroelectric nature.Furthermore,these composites were explored for the first time in solid state refrigeration technology namely electrocaloric effect(ECE).Peak electrocaloric performance shows an adiabatic temperature changes of 0.71,0.64 and 0.50 K and isothermal entropy changes of 0.86,0.80 and 0.65 J/(kg.K)for BCZT.Sn,10%and 15%cement composites,respectively,under application of 0-29 kV/cm electric field.The adiabatic temperature change in cement based composites is comparable with that of the BCZT-Sn ferroelectric ceramics.Furthermore,the dielectric constant(εr)of composites with different ceramic contents at room temperature reveals that dielectric constant increases with an increase in BCZT-Sn proportion in composites.These cement based BCZT.Sn composite materials may be used in solid state refrigeration as they are fairly competitive with the pristine sample.展开更多
Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras avai...Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.展开更多
In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic.Though IoT network...In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic.Though IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging problem.Conventional intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and complexity.Therefore,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT networks.The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data normalization.To manage big data,Hadoop ecosystem is employed.Besides,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection efficiency.Moreover,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data.The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance.To validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects.The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.展开更多
基金financial support rendered by the Salesians of Don BoscoDimapur Province+1 种基金NagalandNorth East India
文摘The novel zirconium oxide, nickel oxide and zinc oxide nanoparticles supported activated carbons（Zr-AC, Ni-AC, Zn-AC） were successfully fabricated through microwave irradiation method. The synthesized nanoparticles were characterized using XRD, HR-SEM, XPS and BET. The optical properties of Zr-AC, Ni-AC and Zn-AC composites were investigated using UV–Vis diffuse reflectance spectroscopy. The photocatalytic efficiency was verified in the degradation of textile dyeing wastewater（TDW） in UV light irradiation. The chemical oxygen demand（COD） of TDW was observed at regular intervals to calculate the removal rate of COD. Zn-AC composites showed impressive photocatalytic enrichment, which can be ascribed to the enhanced absorbance in the UV light region, the effective adsorptive capacity to dye molecules, the assisted charge transfer and the inhibited recombination of electron-hole pairs. The maximum TDW degradation（82% COD removal） was achieved with Zn-AC. A possible synergy mechanism on the surface of Zn-AC was also designed. Zn-AC could be reused five times without exceptional loss of its activity.
文摘Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam optimization.The key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box models.The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies.
文摘In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation process.Since the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.
文摘Metal matrix composites reinforced with graphite particles provide better machinability and tribological properties. The present study attempts to find the optimal level of machining parameters for multi-performance characteristics in turning of Al-SiC-Gr hybrid composites using grey-fuzzy algorithm. The hybrid composites with 5%, 7.5% and 10% combined equal mass fraction of SiC-Gr particles were used for the study and their corresponding tensile strength values are 170, 210, 204 MPa respectively. Al-10%（SiC-Gr） hybrid composite provides better machinability when compared with composites with 5% and 7.5% of SiC-Gr. Grey-fuzzy logic approach offers improved grey-fuzzy reasoning grade and has less uncertainties in the output when compared with grey relational technique. The confirmatory test reveals an increase in grey-fuzzy reasoning grade from 0.619 to 0.891, which substantiates the improvement in multi-performance characteristics at the optimal level of process parameters setting.
文摘Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.
文摘Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.
基金This research was supported by Taif University Researchers Supporting Project Number(TURSP-2020/216),Taif University,Taif,Saudi Arabia.
文摘In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare applications.IoT gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile gadgets.Chronic Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the globe.The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as FPA-DNN.The steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and classification.Primarily,the IoT gadgets are utilized in the collection of a patient’s health information.The proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed data.The application of FPA helps in tuning the DNN parameters for better classification performance.The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset.The results were examined under different aspects.The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.
文摘A thrust for looking multifunctional materials for applications in civil engineering structures has attracted interest among researchers across the globe.Cement based Ba0.85Ca0.15Zr0.1Ti0.88Sn0.02O3(BCZT.Sn)composites were prepared for electrocaloric applications with varying BCZT.Sn to cement ratio.Hysteresis loops showed some signature of saturation in cement composites.However,loops of pure sample were saturated due to its ferroelectric nature.Furthermore,these composites were explored for the first time in solid state refrigeration technology namely electrocaloric effect(ECE).Peak electrocaloric performance shows an adiabatic temperature changes of 0.71,0.64 and 0.50 K and isothermal entropy changes of 0.86,0.80 and 0.65 J/(kg.K)for BCZT.Sn,10%and 15%cement composites,respectively,under application of 0-29 kV/cm electric field.The adiabatic temperature change in cement based composites is comparable with that of the BCZT-Sn ferroelectric ceramics.Furthermore,the dielectric constant(εr)of composites with different ceramic contents at room temperature reveals that dielectric constant increases with an increase in BCZT-Sn proportion in composites.These cement based BCZT.Sn composite materials may be used in solid state refrigeration as they are fairly competitive with the pristine sample.
基金This work was supported by the Deanship of Scientific Research at King Khalid University through a General Research Project under Grant Number GRP/41/42.
文摘Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.
文摘In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic.Though IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging problem.Conventional intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and complexity.Therefore,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT networks.The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data normalization.To manage big data,Hadoop ecosystem is employed.Besides,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection efficiency.Moreover,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data.The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance.To validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects.The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.
