Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health co...Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.展开更多
Over the last decade,a significant increase has been observed in the use of web-based Information systems that process sensitive information,e.g.,personal,financial,medical.With this increased use,the security of such...Over the last decade,a significant increase has been observed in the use of web-based Information systems that process sensitive information,e.g.,personal,financial,medical.With this increased use,the security of such systems became a crucial aspect to ensure safety,integrity and authenticity of the data.To achieve the objectives of data safety,security testing is performed.However,with growth and diversity of information systems,it is challenging to apply security testing for each and every system.Therefore,it is important to classify the assets based on their required level of security using an appropriate technique.In this paper,we propose an asset security classification technique to classify the System Under Test(SUT)based on various factors such as system exposure,data criticality and security requirements.We perform an extensive evaluation of our technique on a sample of 451 information systems.Further,we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities.Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.展开更多
This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United Sta...This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.展开更多
The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obsta...The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.展开更多
Computing students face the problem with time and quality of the work while managing their graduation/senior projects.Rapid Application Development(RAD)model is based on continual user involvement for the process of r...Computing students face the problem with time and quality of the work while managing their graduation/senior projects.Rapid Application Development(RAD)model is based on continual user involvement for the process of requirement gathering via prototyping.After each iteration,the developers can validate the requirements that are completed in the iteration.Managing a project with RAD is easier but not flexible.On the other hand,Agile project management techniques focus on flexibility,agility,teamwork and quality based on user stories.Continual user involvement is avoided,which requires extensive maintenance time for fixing iteration and release of the story points.This also makes it necessary to provide onsite training to the users of the application.This research provides the pros and cons of RAD and Agile project management techniques,to help students in deciding the best approach for managing their graduation projects.For the evaluation of these techniques,similar case studies were given to the senior project students(having similar CGPAs)for building similar functionality-based applications.The two projects“Life Organizer”developed and managed using RAD and“Smart Patient Assistant(SPA)”developed and managed through Agile methodology were evaluated against the quality assurance criteria for senior projects.The study found that the project developed with RAD methodology performed 13.33%better in providing extensive and elaborated documentation than the students following the Agile technique.On the other hand,SPA-Agile based project,due to teamwork had 2.5%better implementation than Life Organizer-RAD based project.展开更多
With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is diffe...With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is different in shapes,sizes,characteristics,and configurations.It can be classified on the purpose of its deployment,either in the civilian or military domain.Drone’s manufacturing,equipment installation,power supply,multi-rotor system,and embedded sensors are not issues for researchers.The main thing is to utilize a drone for a complex and sensitive task using an infrastructureless/self-organization/resource-less network type called Flying Ad Hoc Network(FANET).Monitoring data transmission traffic,emergency and rescue operations,border surveillance,search and physical phenomenon sensing,and so on can be achieved by developing a robust mutual authentication and cross-verification scheme for IoD deployment civilian drones.Although several protocols are available in the literature,they are either design issues or suffering from other vulnerabilities;still,no one claims with conviction about foolproof security mechanisms.Therefore,in this paper,the researchers highlighted the major deficits in prior protocols of the domain,i.e.,these protocols are either vulnerable to forgery,side channel,stolen-verifier attacks,or raised the outdated data transmission flaw.In order to overcome these loopholes and provide a solution to the existing vulnerabilities,this paper proposed an improved and robust public key infrastructure(PKI)based authentication scheme for the IoD environment.The proposed protocol’s security analysis section has been conducted formally using BAN(Burrows-Abadi-Needham)logic,ProVerif2.03 simulation,and informally using discussion/pragmatic illustration.While the performance analysis section of the paper has been assessed by considering storage,computation,and communication cost.Upon comparing the proposed protocol with prior works,it has been demonstrated that it is efficient and effective and recommended for practical implementation in the IoD environment.展开更多
The emergence of deep fake videos in recent years has made image falsification a real danger.A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employi...The emergence of deep fake videos in recent years has made image falsification a real danger.A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content.Because of how clever these videos are frequently,Manipulation is challenging to spot.Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human.In earlier times,it was not so easy to alter the videos,which required expertise in the domain and time.Nowadays,the generation of fake videos has become easier and with a high level of realism in the video.Deepfakes are forgeries and altered visual data that appear in still photos or video footage.Numerous automatic identification systems have been developed to solve this issue,however they are constrained to certain datasets and performpoorly when applied to different datasets.This study aims to develop an ensemble learning model utilizing a convolutional neural network(CNN)to handle deepfakes or Face2Face.We employed ensemble learning,a technique combining many classifiers to achieve higher prediction performance than a single classifier,boosting themodel’s accuracy.The performance of the generated model is evaluated on Face Forensics.This work is about building a new powerful model for automatically identifying deep fake videos with the DeepFake-Detection-Challenges(DFDC)dataset.We test our model using the DFDC,one of the most difficult datasets and get an accuracy of 96%.展开更多
Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life a...Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life activities. This trend has been rapidly advancing towards the newgeneration of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquirecontextual information from the surrounding environment autonomously,perform reasoning on it, and then adapt their behaviors accordingly. With theproliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However,it is often challenging because the imperfect nature of context can cause theinconsistent behavior of the system. In this paper, we propose a contextaware intelligent decision support formalism to assist cognitively impairedpeople in managing their routine life activities. For this, we present a semanticknowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language(SWRL) rules. The set of contextualized information and the set of rulesacquired from the ontology can be used to model Context-aware Multi-AgentSystems (CMAS) in order to autonomously plan all activities of the users andnotify users to act accordingly. To illustrate the use of the proposed formalism,we model a case study of Mild Cognitive Impaired (MCI) patients usingColored Petri Nets (CPN) to show the reasoning process on how the contextaware agents collaboratively plan activities on the user’s behalf and validatethe correctness properties of the system.展开更多
Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this paper.The benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc...Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this paper.The benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication technologies.Heavy data traffic,huge capacity,minimal level of dynamic latency,etc.are some of the future requirements in 5G+and 6G communication systems.In emerging communication,technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain communication.In this paper,the state of the complex system considered as a complex network(the connection between the brain cells,neurons,etc.)needs measurement for analyzing the functions of the neurons during brain communication.Here,we measure the state of the complex system through observability.Using 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of neurons.When IoT or any sensors fail to measure the state of the connectivity in the 5G+or 6G communication due to external noise and attacks,some information about the sensor nodes during the communication will be lost.Similarly,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and connections.Therefore,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability conditions.In this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex network.To analyze the observability recovery,we can use a contraction detection algorithm with complex network properties.Our survey paper shows that contraction size will allow us to improve the performance of brain communication,stability of neurons,etc.,through the clustering coefficient considered in the contraction detection algorithm.In addition,we discuss the scalability of IoT communication using 5G+/6G-based photonic technology.展开更多
This study is designed to develop Artificial Intelligence(AI)based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays(CXRs).The frontline physicians and radiologists suf...This study is designed to develop Artificial Intelligence(AI)based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays(CXRs).The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs.In this study,AI-based analysis tools were developed that can precisely classify COVID-19 lung infection.Publicly available datasets of COVID-19(N=1525),non-COVID-19 normal(N=1525),viral pneumonia(N=1342)and bacterial pneumonia(N=2521)from the Italian Society of Medical and Interventional Radiology(SIRM),Radiopaedia,The Cancer Imaging Archive(TCIA)and Kaggle repositories were taken.A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed.Additionally,the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning(ML)algorithms,which twice trained the learning algorithms.The ResNet101 with optimized parameters yielded improved performance to default parameters.The extracted features from ResNet101 are fed to the k-nearest neighbor(KNN)and support vector machine(SVM)yielded the highest 3-class classification performance of 99.86%and 99.46%,respectively.The results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of CXRs.The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.展开更多
Context:Since the end of 2019,the COVID-19 pandemic had a worst impact on world’s economy,healthcare,and education.There are several aspects where the impact of COVID-19 could be visualized.Among these,one aspect is ...Context:Since the end of 2019,the COVID-19 pandemic had a worst impact on world’s economy,healthcare,and education.There are several aspects where the impact of COVID-19 could be visualized.Among these,one aspect is the productivity of researcher,which plays a significant role in the success of an organization.Problem:There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization.Method:We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions.Our study aims to find out the impact of COVID-19 on the researcher’s productivity that are working in different fields.In this study,we conduct a questionnaire-based analysis,which included feedback of 152 researchers of certain domains.These researchers are currently involved in different research activities.Subsequently,we perform a statistical analysis to analyze the collected responses and report the findings.Findings:The results indicate the substantial impact of COVID-19 pandemics on the researcher’s productivity in terms of mental disturbance,lack of regular meetings,and field visits for the collection of primary data.Conclusion:Finally,it is concluded that researcher’s daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion.These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization.展开更多
Cardio Vascular disease(CVD),involving the heart and blood vessels is one of the most leading causes of death throughout the world.There are several risk factors for causing heart diseases like sedentary lifestyle,unh...Cardio Vascular disease(CVD),involving the heart and blood vessels is one of the most leading causes of death throughout the world.There are several risk factors for causing heart diseases like sedentary lifestyle,unhealthy diet,obesity,diabetes,hypertension,smoking and consumption of alcohol,stress,hereditary factory etc.Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being.At present,the highly stressful life with bad lifestyle activities causes heart disease at a very young age.The main aim of this research is to predict the premature heart disease based on machine learning algorithms.This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself.Support Vector Machine(SVM)is used for segregating the CVD patients based on their symptoms and medical observation.The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time.A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.展开更多
OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technol...OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires.The accuracy of OMR systems is very important due to the environment inwhich they are used.TheOMRalgorithm relies on pixel projection or Hough transform to determine the exact answer in the document.These techniques rely on majority voting to approximate a predetermined shape.The performance of these systems depends on precise input from dedicated hardware.Printing and scanning OMR tables introduces artifacts that make table processing error-prone.This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques.Depending on the type of artifact introduced,accuracy is affected differently.We classified the types of errors and their frequency according to the artifacts in the OMR system.As a major contribution,we propose an improved algorithm that fixes errors due to skewness.Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents.As a minor contribution,our proposal also improves the accuracy of detecting markers in OMR documents.The results show an improvement in accuracy over existing algorithms in each of the identified problems.This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.展开更多
Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhum...Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time scenario.Fifty (50) volunteers of different ages participated in the experimental study.Using the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.展开更多
As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data ...As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human faces.The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach.In this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face data.An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering.The close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral pattern.To identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same subject.The behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit measurements.The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,etc.The complexity measures have also been calculated under worst-case and best-case situations.展开更多
The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated aspects.It is essential to learn about product revie...The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated aspects.It is essential to learn about product reviews;however,to react to such reviews,extracting aspects of the entity to which these reviews belong is equally important.Aspect-based Sentiment Analysis(ABSA)refers to aspects extracted from an opinionated text.The literature proposes different approaches for ABSA;however,most research is focused on supervised approaches,which require labeled datasets with manual sentiment polarity labeling and aspect tagging.This study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories.Hence,the study deals with two main sub-tasks in ABSA,named Aspect Category Detection(ACD)and Aspect Term Extraction(ATE).In the first sub-task,aspects categories are extracted using topic modeling and filtered by an oracle further,and it is fed to zero-shot learning as the prompts and the augmented text.The predicted categories are the input to find similar phrases curated with extracting meaningful phrases(e.g.,Nouns,Proper Nouns,NER(Named Entity Recognition)entities)to detect the aspect terms.The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment(MAMS)dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.展开更多
Robotic manipulators are widely used in applications that require fast and precise motion.Such devices,however,are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors wit...Robotic manipulators are widely used in applications that require fast and precise motion.Such devices,however,are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part.To address these issues,the Linear Matrix Inequalities(LMIs)and Parallel Distributed Compensation(PDC)approaches are implemented in the Takagy–Sugeno Fuzzy Model(T-SFM).We propose the following methodology;initially,the state space equations of the nonlinear manipulator model are derived.Next,a Takagy–Sugeno Fuzzy Model(T-SFM)technique is used for linearizing the state space equations of the nonlinear manipulator.The T-SFM controller is developed using the Parallel Distributed Compensation(PDC)method.The prime concept of the designed controller is to compensate for all the fuzzy rules.Furthermore,the Linear Matrix Inequalities(LMIs)are applied to generate adequate cases to ensure stability and control.Convex programming methods are applied to solve the developed LMIs problems.Simulations developed for the proposed model show that the proposed controller stabilized the system with zero tracking error in less than 1.5 s.展开更多
Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and mor...Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and more online security and data hijacking attacks,the confidentiality,integrity and availability of data are considered serious issues in HIoT applications.In this regard,lightweight block ciphers(LBCs)are promising in resourceconstrained environment where security is the primary consideration.The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance,cost,and security.Most of the existing LBCs primarily focus on text data or grayscale images.The main focus of this paper is about securing color images in a cost-effective way.We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones,and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems.In order to reduce computational complexity and simulation time,the proposed Lightweight Symmetric Block Cipher(LSBC)exploits chaos-based confusion-diffusion operations at the inter-block level using a single round.The strength of LSBC is assessed by cryptanalysis,while it is ranked by comparing it to other privacy-preserving schemes.Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks,which proves that our LSBC is a good candidate for image security in HIoT.展开更多
While designing and developing encryption algorithms for text and images,the main focus has remained on security.This has led to insufficient attention on the improvement of encryption efficiency,enhancement of hyperc...While designing and developing encryption algorithms for text and images,the main focus has remained on security.This has led to insufficient attention on the improvement of encryption efficiency,enhancement of hyperchaotic sequence randomness,and dynamic DNA-based S-box.In this regard,a new symmetric block cipher scheme has been proposed.It uses dynamic DNA-based S-box connected with MD5 and a hyperchaotic system to produce confusion and diffusion for encrypting color images.Our proposed scheme supports various size color images.It generates three DNA based Sboxes for substitution namely DNA_1_s-box,DNA_2_s-box and DNA_3_sbox,each of size 16×16.Next,the 4D hyperchaotic system followed by MD5 is employed in a novel way to enhance security.The three DNAbased S-boxes are generated from real DNA sequences taken from National Center for Biotechnology Information(NCBI)databases and are dependent on the mean intensity value of an input image,thus effectively introducing content-based confusion.Finally,Conservative Site-Specific Recombination(CSSR)is applied on the output DNA received from DNA based S-boxes.The experimental results indicate that the proposed encryption scheme is more secure,robust,and computationally efficient than some of the recently published similar works.Being computational efficient,our proposed scheme is feasible on many emergent resource-constrained platforms.展开更多
Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharact...Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the yield.Python is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable crop.We implemented the proposed system in the field.The efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.展开更多
文摘Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.
基金the Higher Education Commission(HEC),Pakistan throughits initiative of National Center for Cyber Security for the affiliated Security Testing-Innovative SecuredSystems Lab(ISSL)established at University of Engineering&Technology(UET)Peshawar,Grant No.2(1078)/HEC/M&E/2018/707.
文摘Over the last decade,a significant increase has been observed in the use of web-based Information systems that process sensitive information,e.g.,personal,financial,medical.With this increased use,the security of such systems became a crucial aspect to ensure safety,integrity and authenticity of the data.To achieve the objectives of data safety,security testing is performed.However,with growth and diversity of information systems,it is challenging to apply security testing for each and every system.Therefore,it is important to classify the assets based on their required level of security using an appropriate technique.In this paper,we propose an asset security classification technique to classify the System Under Test(SUT)based on various factors such as system exposure,data criticality and security requirements.We perform an extensive evaluation of our technique on a sample of 451 information systems.Further,we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities.Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.
文摘This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.
文摘The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.
文摘Computing students face the problem with time and quality of the work while managing their graduation/senior projects.Rapid Application Development(RAD)model is based on continual user involvement for the process of requirement gathering via prototyping.After each iteration,the developers can validate the requirements that are completed in the iteration.Managing a project with RAD is easier but not flexible.On the other hand,Agile project management techniques focus on flexibility,agility,teamwork and quality based on user stories.Continual user involvement is avoided,which requires extensive maintenance time for fixing iteration and release of the story points.This also makes it necessary to provide onsite training to the users of the application.This research provides the pros and cons of RAD and Agile project management techniques,to help students in deciding the best approach for managing their graduation projects.For the evaluation of these techniques,similar case studies were given to the senior project students(having similar CGPAs)for building similar functionality-based applications.The two projects“Life Organizer”developed and managed using RAD and“Smart Patient Assistant(SPA)”developed and managed through Agile methodology were evaluated against the quality assurance criteria for senior projects.The study found that the project developed with RAD methodology performed 13.33%better in providing extensive and elaborated documentation than the students following the Agile technique.On the other hand,SPA-Agile based project,due to teamwork had 2.5%better implementation than Life Organizer-RAD based project.
文摘With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is different in shapes,sizes,characteristics,and configurations.It can be classified on the purpose of its deployment,either in the civilian or military domain.Drone’s manufacturing,equipment installation,power supply,multi-rotor system,and embedded sensors are not issues for researchers.The main thing is to utilize a drone for a complex and sensitive task using an infrastructureless/self-organization/resource-less network type called Flying Ad Hoc Network(FANET).Monitoring data transmission traffic,emergency and rescue operations,border surveillance,search and physical phenomenon sensing,and so on can be achieved by developing a robust mutual authentication and cross-verification scheme for IoD deployment civilian drones.Although several protocols are available in the literature,they are either design issues or suffering from other vulnerabilities;still,no one claims with conviction about foolproof security mechanisms.Therefore,in this paper,the researchers highlighted the major deficits in prior protocols of the domain,i.e.,these protocols are either vulnerable to forgery,side channel,stolen-verifier attacks,or raised the outdated data transmission flaw.In order to overcome these loopholes and provide a solution to the existing vulnerabilities,this paper proposed an improved and robust public key infrastructure(PKI)based authentication scheme for the IoD environment.The proposed protocol’s security analysis section has been conducted formally using BAN(Burrows-Abadi-Needham)logic,ProVerif2.03 simulation,and informally using discussion/pragmatic illustration.While the performance analysis section of the paper has been assessed by considering storage,computation,and communication cost.Upon comparing the proposed protocol with prior works,it has been demonstrated that it is efficient and effective and recommended for practical implementation in the IoD environment.
文摘The emergence of deep fake videos in recent years has made image falsification a real danger.A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content.Because of how clever these videos are frequently,Manipulation is challenging to spot.Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human.In earlier times,it was not so easy to alter the videos,which required expertise in the domain and time.Nowadays,the generation of fake videos has become easier and with a high level of realism in the video.Deepfakes are forgeries and altered visual data that appear in still photos or video footage.Numerous automatic identification systems have been developed to solve this issue,however they are constrained to certain datasets and performpoorly when applied to different datasets.This study aims to develop an ensemble learning model utilizing a convolutional neural network(CNN)to handle deepfakes or Face2Face.We employed ensemble learning,a technique combining many classifiers to achieve higher prediction performance than a single classifier,boosting themodel’s accuracy.The performance of the generated model is evaluated on Face Forensics.This work is about building a new powerful model for automatically identifying deep fake videos with the DeepFake-Detection-Challenges(DFDC)dataset.We test our model using the DFDC,one of the most difficult datasets and get an accuracy of 96%.
文摘Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life activities. This trend has been rapidly advancing towards the newgeneration of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquirecontextual information from the surrounding environment autonomously,perform reasoning on it, and then adapt their behaviors accordingly. With theproliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However,it is often challenging because the imperfect nature of context can cause theinconsistent behavior of the system. In this paper, we propose a contextaware intelligent decision support formalism to assist cognitively impairedpeople in managing their routine life activities. For this, we present a semanticknowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language(SWRL) rules. The set of contextualized information and the set of rulesacquired from the ontology can be used to model Context-aware Multi-AgentSystems (CMAS) in order to autonomously plan all activities of the users andnotify users to act accordingly. To illustrate the use of the proposed formalism,we model a case study of Mild Cognitive Impaired (MCI) patients usingColored Petri Nets (CPN) to show the reasoning process on how the contextaware agents collaboratively plan activities on the user’s behalf and validatethe correctness properties of the system.
基金support from the USA-based research group(Computing and Engineering,Indiana University)the KSA-based research group(Department of Computer Science,King Abdulaziz University).
文摘Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this paper.The benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication technologies.Heavy data traffic,huge capacity,minimal level of dynamic latency,etc.are some of the future requirements in 5G+and 6G communication systems.In emerging communication,technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain communication.In this paper,the state of the complex system considered as a complex network(the connection between the brain cells,neurons,etc.)needs measurement for analyzing the functions of the neurons during brain communication.Here,we measure the state of the complex system through observability.Using 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of neurons.When IoT or any sensors fail to measure the state of the connectivity in the 5G+or 6G communication due to external noise and attacks,some information about the sensor nodes during the communication will be lost.Similarly,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and connections.Therefore,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability conditions.In this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex network.To analyze the observability recovery,we can use a contraction detection algorithm with complex network properties.Our survey paper shows that contraction size will allow us to improve the performance of brain communication,stability of neurons,etc.,through the clustering coefficient considered in the contraction detection algorithm.In addition,we discuss the scalability of IoT communication using 5G+/6G-based photonic technology.
文摘This study is designed to develop Artificial Intelligence(AI)based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays(CXRs).The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs.In this study,AI-based analysis tools were developed that can precisely classify COVID-19 lung infection.Publicly available datasets of COVID-19(N=1525),non-COVID-19 normal(N=1525),viral pneumonia(N=1342)and bacterial pneumonia(N=2521)from the Italian Society of Medical and Interventional Radiology(SIRM),Radiopaedia,The Cancer Imaging Archive(TCIA)and Kaggle repositories were taken.A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed.Additionally,the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning(ML)algorithms,which twice trained the learning algorithms.The ResNet101 with optimized parameters yielded improved performance to default parameters.The extracted features from ResNet101 are fed to the k-nearest neighbor(KNN)and support vector machine(SVM)yielded the highest 3-class classification performance of 99.86%and 99.46%,respectively.The results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of CXRs.The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.
文摘Context:Since the end of 2019,the COVID-19 pandemic had a worst impact on world’s economy,healthcare,and education.There are several aspects where the impact of COVID-19 could be visualized.Among these,one aspect is the productivity of researcher,which plays a significant role in the success of an organization.Problem:There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization.Method:We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions.Our study aims to find out the impact of COVID-19 on the researcher’s productivity that are working in different fields.In this study,we conduct a questionnaire-based analysis,which included feedback of 152 researchers of certain domains.These researchers are currently involved in different research activities.Subsequently,we perform a statistical analysis to analyze the collected responses and report the findings.Findings:The results indicate the substantial impact of COVID-19 pandemics on the researcher’s productivity in terms of mental disturbance,lack of regular meetings,and field visits for the collection of primary data.Conclusion:Finally,it is concluded that researcher’s daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion.These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization.
文摘Cardio Vascular disease(CVD),involving the heart and blood vessels is one of the most leading causes of death throughout the world.There are several risk factors for causing heart diseases like sedentary lifestyle,unhealthy diet,obesity,diabetes,hypertension,smoking and consumption of alcohol,stress,hereditary factory etc.Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being.At present,the highly stressful life with bad lifestyle activities causes heart disease at a very young age.The main aim of this research is to predict the premature heart disease based on machine learning algorithms.This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself.Support Vector Machine(SVM)is used for segregating the CVD patients based on their symptoms and medical observation.The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time.A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.
基金King Saud University for funding this work through Researchers Supporting Project number(RSP2022R426).
文摘OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires.The accuracy of OMR systems is very important due to the environment inwhich they are used.TheOMRalgorithm relies on pixel projection or Hough transform to determine the exact answer in the document.These techniques rely on majority voting to approximate a predetermined shape.The performance of these systems depends on precise input from dedicated hardware.Printing and scanning OMR tables introduces artifacts that make table processing error-prone.This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques.Depending on the type of artifact introduced,accuracy is affected differently.We classified the types of errors and their frequency according to the artifacts in the OMR system.As a major contribution,we propose an improved algorithm that fixes errors due to skewness.Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents.As a minor contribution,our proposal also improves the accuracy of detecting markers in OMR documents.The results show an improvement in accuracy over existing algorithms in each of the identified problems.This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups RGP.2/212/1443.
文摘Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time scenario.Fifty (50) volunteers of different ages participated in the experimental study.Using the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.
文摘As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human faces.The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach.In this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face data.An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering.The close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral pattern.To identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same subject.The behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit measurements.The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,etc.The complexity measures have also been calculated under worst-case and best-case situations.
文摘The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated aspects.It is essential to learn about product reviews;however,to react to such reviews,extracting aspects of the entity to which these reviews belong is equally important.Aspect-based Sentiment Analysis(ABSA)refers to aspects extracted from an opinionated text.The literature proposes different approaches for ABSA;however,most research is focused on supervised approaches,which require labeled datasets with manual sentiment polarity labeling and aspect tagging.This study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories.Hence,the study deals with two main sub-tasks in ABSA,named Aspect Category Detection(ACD)and Aspect Term Extraction(ATE).In the first sub-task,aspects categories are extracted using topic modeling and filtered by an oracle further,and it is fed to zero-shot learning as the prompts and the augmented text.The predicted categories are the input to find similar phrases curated with extracting meaningful phrases(e.g.,Nouns,Proper Nouns,NER(Named Entity Recognition)entities)to detect the aspect terms.The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment(MAMS)dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.
文摘Robotic manipulators are widely used in applications that require fast and precise motion.Such devices,however,are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part.To address these issues,the Linear Matrix Inequalities(LMIs)and Parallel Distributed Compensation(PDC)approaches are implemented in the Takagy–Sugeno Fuzzy Model(T-SFM).We propose the following methodology;initially,the state space equations of the nonlinear manipulator model are derived.Next,a Takagy–Sugeno Fuzzy Model(T-SFM)technique is used for linearizing the state space equations of the nonlinear manipulator.The T-SFM controller is developed using the Parallel Distributed Compensation(PDC)method.The prime concept of the designed controller is to compensate for all the fuzzy rules.Furthermore,the Linear Matrix Inequalities(LMIs)are applied to generate adequate cases to ensure stability and control.Convex programming methods are applied to solve the developed LMIs problems.Simulations developed for the proposed model show that the proposed controller stabilized the system with zero tracking error in less than 1.5 s.
基金This work was supported by the King Saud University (in Riyadh, SaudiArabia) through the Researcher Supporting Project Number (RSP–2021/387).
文摘Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and more online security and data hijacking attacks,the confidentiality,integrity and availability of data are considered serious issues in HIoT applications.In this regard,lightweight block ciphers(LBCs)are promising in resourceconstrained environment where security is the primary consideration.The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance,cost,and security.Most of the existing LBCs primarily focus on text data or grayscale images.The main focus of this paper is about securing color images in a cost-effective way.We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones,and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems.In order to reduce computational complexity and simulation time,the proposed Lightweight Symmetric Block Cipher(LSBC)exploits chaos-based confusion-diffusion operations at the inter-block level using a single round.The strength of LSBC is assessed by cryptanalysis,while it is ranked by comparing it to other privacy-preserving schemes.Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks,which proves that our LSBC is a good candidate for image security in HIoT.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493.
文摘While designing and developing encryption algorithms for text and images,the main focus has remained on security.This has led to insufficient attention on the improvement of encryption efficiency,enhancement of hyperchaotic sequence randomness,and dynamic DNA-based S-box.In this regard,a new symmetric block cipher scheme has been proposed.It uses dynamic DNA-based S-box connected with MD5 and a hyperchaotic system to produce confusion and diffusion for encrypting color images.Our proposed scheme supports various size color images.It generates three DNA based Sboxes for substitution namely DNA_1_s-box,DNA_2_s-box and DNA_3_sbox,each of size 16×16.Next,the 4D hyperchaotic system followed by MD5 is employed in a novel way to enhance security.The three DNAbased S-boxes are generated from real DNA sequences taken from National Center for Biotechnology Information(NCBI)databases and are dependent on the mean intensity value of an input image,thus effectively introducing content-based confusion.Finally,Conservative Site-Specific Recombination(CSSR)is applied on the output DNA received from DNA based S-boxes.The experimental results indicate that the proposed encryption scheme is more secure,robust,and computationally efficient than some of the recently published similar works.Being computational efficient,our proposed scheme is feasible on many emergent resource-constrained platforms.
文摘Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the yield.Python is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable crop.We implemented the proposed system in the field.The efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.
