Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research fo...Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success.展开更多
The research volume increases at the study rate,causing massive text corpora.Due to these enormous text corpora,we are drowning in data and starving for information.Therefore,recent research employed different text mi...The research volume increases at the study rate,causing massive text corpora.Due to these enormous text corpora,we are drowning in data and starving for information.Therefore,recent research employed different text mining approaches to extract information from this text corpus.These proposed approaches extract meaningful and precise phrases that effectively describe the text’s information.These extracted phrases are commonly termed keyphrases.Further,these key phrases are employed to determine the different fields of study trends.Moreover,these key phrases can also be used to determine the spatiotemporal trends in the various research fields.In this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend analysis.Therefore,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific period.This study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research trend.This data was collected from various open access journals.Further,this research determined the spatiotemporal trends using quality phrasemining.This research also focused on finding the research collaboration of different countries in a particular research subject.The research collaborations of other countries’researchers show the impact on import and export of those countries.The visualization approach is also incorporated to understand the results better.展开更多
The advent and extensive use of computer and increasing development of different technologies it is important to increase the awareness of issues related to the electronic text or text presentation on computer screen....The advent and extensive use of computer and increasing development of different technologies it is important to increase the awareness of issues related to the electronic text or text presentation on computer screen. The usage of web shows the importance of usability and readability of the web applications or sources provide by the web and web textual contents. Web application fails to encounter the user’s requirements in effective manner specially related to textual information, because the designers are unaware from some of the important factors effecting readability, reading from the screen. In this regard, this study is the continuation of the previous work that has been done for the improvement of readability, to handle the readability issues on the basis of Eye Blink for male participants and female participants. To achieve general recommendations for suitable or optimum length of text line for all type of users on the bases of eye blink. Basically during reading from the computer screen focus losses at two positions, when eye blink in the middle of text line and when text line ends. The study specifies suitable length of text line on the basis of Eye Blink, assuming three typographical variables i.e. font style, font color, font size, and with white background, which improve the overall readability or reading from computer screen. The study also shows two important things the degree of understandability and the degree of attractive appearance of different combination.展开更多
Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Mag...Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.展开更多
In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the e...In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.展开更多
This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassemb...This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.展开更多
This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and ...This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and eliminating.The foraging behavior prompts the bison to seek a richer food source for survival.When bison find a food source,they stick around for a while by bathing behavior.The jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating behavior.The eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent individuals.The above behaviors are translated into ABO by mathematical modeling.To assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with constraints.The findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.展开更多
Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial i...Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.展开更多
Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation sector.However,the exchange of beacons and messages ov...Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation sector.However,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy violations.To address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed recently.However,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many attacks.As such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)operations.The formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably secure.The proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and unlinkability.Moreover,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and impersonations.From the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security features.Therefore,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.展开更多
With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security.Although X-ray baggage monitoring is...With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security.Although X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger privacy.To address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation frameworks.However,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset discrepancies.Hence,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as C-BLX.The research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage scans.The proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage scans.More specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage scans.The multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign classes.The contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation results.The proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,respectively.Furthermore,the limitations of the proposed system in extracting precise region segments in intricate noisy settings and potential strategies for overcoming them through post-processing techniques were explored(source code will be available at https://github.com/Divs1159/CNN_BLS.)展开更多
With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover,...With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private infor- mation in different information systems. However, problems such as the cask principle of preserving pri- vate information among different information systems and the dif culty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot pro- vide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a the- ory of privacy computing and a key technology system that includes a privacy computing framework, a formal de nition of privacy computing, four principles that should be followed in privacy computing, ffect algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.展开更多
Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning netwo...Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning network for hand gesture recognition.The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation.To learn short-term features,each video input is segmented into a fixed number of frame groups.A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot.These two entities are fused and fed into a convolutional neural network(Conv Net)for feature extraction.The Conv Nets for all groups share parameters.To learn longterm features,outputs from all Conv Nets are fed into a long short-term memory(LSTM)network,by which a final classification result is predicted.The new model has been tested with two popular hand gesture datasets,namely the Jester dataset and Nvidia dataset.Comparing with other models,our model produced very competitive results.The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.展开更多
Among many safety applications enabled by Dedicated Short Range Communication （DSRC）, truck platooning provides many incentives to commercial companies. This paper studies DSRC Vehicle-to-Vehicle （V2V） performance...Among many safety applications enabled by Dedicated Short Range Communication （DSRC）, truck platooning provides many incentives to commercial companies. This paper studies DSRC Vehicle-to-Vehicle （V2V） performance in truck platooning scenarios through real-world experiments. Commercial DSRC equipments and semi-trailer trucks are used in this study. We mount one DSRC antenna on each side of the truck. One set of dynamic tests and a few sets of static tests are conducted to explore DSRC behaviors under different situations. From the test results, we verified some of our speculations. For example, hilly roads can affect delivery ratio and antennas mounted on opposite sides of a truck can suffer from low delivery ratio at curved roads. In addition, we also found that antennas can sometimes suffer from low delivery ratio even when the trucks are on straight roads, possibly due to reflections from the nearby terrain. Fortunately, the delivery ratio can be greatly improved by using the two side antennas alternately.展开更多
In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interac...In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.展开更多
Educational institutions are soft targets for the terrorist with massive and defenseless people.In the recent past,numbers of such attacks have been executed around the world.Conducting research,in order to provide a ...Educational institutions are soft targets for the terrorist with massive and defenseless people.In the recent past,numbers of such attacks have been executed around the world.Conducting research,in order to provide a secure environment to the educational institutions is a challenging task.This effort is motivated by recent assaults,made at Army Public School Peshawar,following another attack at Charsada University,Khyber Pukhtun Khwa,Pakistan and also the Santa Fe High School Texas,USA massacre.This study uses the basic technologies of edge computing,cloud computing and IoT to design a smart emergency alarm system framework.IoT is engaged in developing this world smarter,can contribute significantly to design the Smart Security Framework(SSF)for educational institutions.In the emergency situation,all the command and control centres must be informed within seconds to halt or minimize the loss.In this article,the SSF is proposed.This framework works on three layers.The first layer is the sensors and smart devices layer.All these sensors and smart devices are connected to the Emergency Control Room(ECR),which is the second layer of the proposed framework.The second layer uses edge computing technologies to process massive data and information locally.The third layer uses cloud computing techniques to transmit and process data and information to different command and control centres.The proposed system was tested on Cisco Packet Tracer 7.The result shows that this approach can play an efficient role in security alert,not only in the educational institutions but also in other organizations too.展开更多
With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is necessary.The main purpose of this study is...With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is necessary.The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments.The proposed system first takes an input image pre-process it and then detects faces in the entire image.After that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group.The proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups dataset.The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time.The proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.展开更多
Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding.Such sceneunderstanding task is a demanding part of several technologies,like augmented reality-base...Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding.Such sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist guide.Incorporating visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene understanding.In this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an image.Initially,after acquisition and preprocessing,the image is segmented by using CNN.Then,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are computed.After the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion technique.Then,to select theminimal set of features,genetic algorithm-based feature selection is used.In order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is applied.Once objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation approach.Finally,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image.The experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.展开更多
The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive...The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab.展开更多
Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitne...Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games.The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room.To track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the game.Further,the sensor data is sent to a machine learning model,which detects the game played by the user.The detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human motion.The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity.The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational sector.Extensive experimentation proved the validity of the proposed system.展开更多
基金supported by the BK21 FOUR(Fostering Outstanding Universities for Research)funded by the Ministry of Education and National Research Foundation of Korea.
文摘Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success.
文摘The research volume increases at the study rate,causing massive text corpora.Due to these enormous text corpora,we are drowning in data and starving for information.Therefore,recent research employed different text mining approaches to extract information from this text corpus.These proposed approaches extract meaningful and precise phrases that effectively describe the text’s information.These extracted phrases are commonly termed keyphrases.Further,these key phrases are employed to determine the different fields of study trends.Moreover,these key phrases can also be used to determine the spatiotemporal trends in the various research fields.In this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend analysis.Therefore,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific period.This study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research trend.This data was collected from various open access journals.Further,this research determined the spatiotemporal trends using quality phrasemining.This research also focused on finding the research collaboration of different countries in a particular research subject.The research collaborations of other countries’researchers show the impact on import and export of those countries.The visualization approach is also incorporated to understand the results better.
文摘The advent and extensive use of computer and increasing development of different technologies it is important to increase the awareness of issues related to the electronic text or text presentation on computer screen. The usage of web shows the importance of usability and readability of the web applications or sources provide by the web and web textual contents. Web application fails to encounter the user’s requirements in effective manner specially related to textual information, because the designers are unaware from some of the important factors effecting readability, reading from the screen. In this regard, this study is the continuation of the previous work that has been done for the improvement of readability, to handle the readability issues on the basis of Eye Blink for male participants and female participants. To achieve general recommendations for suitable or optimum length of text line for all type of users on the bases of eye blink. Basically during reading from the computer screen focus losses at two positions, when eye blink in the middle of text line and when text line ends. The study specifies suitable length of text line on the basis of Eye Blink, assuming three typographical variables i.e. font style, font color, font size, and with white background, which improve the overall readability or reading from computer screen. The study also shows two important things the degree of understandability and the degree of attractive appearance of different combination.
基金This research is funded by the Researchers Supporting Project Number(RSPD2024R1027),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.
文摘In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.
文摘This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.
基金the National Natural Science Foundation of China(Grant No.U1731128)the Natural Science Foundation of Liaoning Province(Grant No.2019-MS-174)+1 种基金the Foundation of Liaoning Province Education Administration(Grant No.LJKZ0279)the Team of Artificial Intelligence Theory and Application for the financial support.
文摘This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and eliminating.The foraging behavior prompts the bison to seek a richer food source for survival.When bison find a food source,they stick around for a while by bathing behavior.The jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating behavior.The eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent individuals.The above behaviors are translated into ABO by mathematical modeling.To assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with constraints.The findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.
基金financially supported by the Deanship of Scientific Research at King Khalid University under Research Grant Number(R.G.P.2/549/44).
文摘Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.
基金supported by Teaching Reform Project of Shenzhen University of Technology under Grant No.20231016.
文摘Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation sector.However,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy violations.To address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed recently.However,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many attacks.As such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)operations.The formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably secure.The proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and unlinkability.Moreover,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and impersonations.From the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security features.Therefore,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.
基金supported by research funds from Khalifa University,No.CIRA-2021-052the Advanced Technology Research Center Program(ASPIRE),No.AARE20-279.
文摘With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security.Although X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger privacy.To address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation frameworks.However,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset discrepancies.Hence,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as C-BLX.The research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage scans.The proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage scans.More specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage scans.The multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign classes.The contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation results.The proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,respectively.Furthermore,the limitations of the proposed system in extracting precise region segments in intricate noisy settings and potential strategies for overcoming them through post-processing techniques were explored(source code will be available at https://github.com/Divs1159/CNN_BLS.)
文摘With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private infor- mation in different information systems. However, problems such as the cask principle of preserving pri- vate information among different information systems and the dif culty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot pro- vide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a the- ory of privacy computing and a key technology system that includes a privacy computing framework, a formal de nition of privacy computing, four principles that should be followed in privacy computing, ffect algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.
文摘Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning network for hand gesture recognition.The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation.To learn short-term features,each video input is segmented into a fixed number of frame groups.A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot.These two entities are fused and fed into a convolutional neural network(Conv Net)for feature extraction.The Conv Nets for all groups share parameters.To learn longterm features,outputs from all Conv Nets are fed into a long short-term memory(LSTM)network,by which a final classification result is predicted.The new model has been tested with two popular hand gesture datasets,namely the Jester dataset and Nvidia dataset.Comparing with other models,our model produced very competitive results.The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.
文摘Among many safety applications enabled by Dedicated Short Range Communication （DSRC）, truck platooning provides many incentives to commercial companies. This paper studies DSRC Vehicle-to-Vehicle （V2V） performance in truck platooning scenarios through real-world experiments. Commercial DSRC equipments and semi-trailer trucks are used in this study. We mount one DSRC antenna on each side of the truck. One set of dynamic tests and a few sets of static tests are conducted to explore DSRC behaviors under different situations. From the test results, we verified some of our speculations. For example, hilly roads can affect delivery ratio and antennas mounted on opposite sides of a truck can suffer from low delivery ratio at curved roads. In addition, we also found that antennas can sometimes suffer from low delivery ratio even when the trucks are on straight roads, possibly due to reflections from the nearby terrain. Fortunately, the delivery ratio can be greatly improved by using the two side antennas alternately.
基金supported by Priority Research Centers Program through NRF funded by MEST(2018R1A6A1A03024003)the Grand Information Technology Research Center support program IITP-2020-2020-0-01612 supervised by the IITP by MSIT,Korea.
文摘In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.
文摘Educational institutions are soft targets for the terrorist with massive and defenseless people.In the recent past,numbers of such attacks have been executed around the world.Conducting research,in order to provide a secure environment to the educational institutions is a challenging task.This effort is motivated by recent assaults,made at Army Public School Peshawar,following another attack at Charsada University,Khyber Pukhtun Khwa,Pakistan and also the Santa Fe High School Texas,USA massacre.This study uses the basic technologies of edge computing,cloud computing and IoT to design a smart emergency alarm system framework.IoT is engaged in developing this world smarter,can contribute significantly to design the Smart Security Framework(SSF)for educational institutions.In the emergency situation,all the command and control centres must be informed within seconds to halt or minimize the loss.In this article,the SSF is proposed.This framework works on three layers.The first layer is the sensors and smart devices layer.All these sensors and smart devices are connected to the Emergency Control Room(ECR),which is the second layer of the proposed framework.The second layer uses edge computing technologies to process massive data and information locally.The third layer uses cloud computing techniques to transmit and process data and information to different command and control centres.The proposed system was tested on Cisco Packet Tracer 7.The result shows that this approach can play an efficient role in security alert,not only in the educational institutions but also in other organizations too.
基金This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2018R1D1A1A02085645)Also,this work was supported by the KoreaMedical Device Development Fund grant funded by the Korean government(the Ministry of Science and ICT,the Ministry of Trade,Industry and Energy,the Ministry of Health&Welfare,theMinistry of Food and Drug Safety)(Project Number:202012D05-02).
文摘With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is necessary.The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments.The proposed system first takes an input image pre-process it and then detects faces in the entire image.After that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group.The proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups dataset.The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time.The proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.
基金This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding.Such sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist guide.Incorporating visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene understanding.In this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an image.Initially,after acquisition and preprocessing,the image is segmented by using CNN.Then,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are computed.After the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion technique.Then,to select theminimal set of features,genetic algorithm-based feature selection is used.In order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is applied.Once objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation approach.Finally,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image.The experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.
基金Taif University Researchers Supporting Project number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab.
基金This researchwas supported by aGrant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea。
文摘Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games.The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room.To track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the game.Further,the sensor data is sent to a machine learning model,which detects the game played by the user.The detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human motion.The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity.The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational sector.Extensive experimentation proved the validity of the proposed system.
