The growth of Wireless Sensor Networks(WSNs)has revolutionized thefield of technology and it is used in different application frameworks.Unmanned edges and other critical locations can be monitored using the navigatio...The growth of Wireless Sensor Networks(WSNs)has revolutionized thefield of technology and it is used in different application frameworks.Unmanned edges and other critical locations can be monitored using the navigation sensor node.The WSN required low energy consumption to provide a high network and guarantee the ultimate goal.The main objective of this work is to propose hybrid energy optimization in local aware environments.The hybrid proposed work consists of clustering,optimization,direct and indirect communication and routing.The aim of this research work is to provide and framework for reduced energy and trusted communication with the shortest path to reach source to destination in WSN and an extending lifetime of wireless sensors.The proposed Artificial Fish Swarm Optimization algorithm is used for energy optimization in military applications which is simulated using Network Simulator(NS)tool.This work optimizes the energy level and the same is compared with various genetic algorithms(GA)and also the cluster selection process was compared with thefission-fusion(FF)selection method.The results of the proposed work show,improvement in energy optimization,throughput and time delay.展开更多
Electronic learning(e-learning)has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements...Electronic learning(e-learning)has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web.Post covid-19 pandemic,online learning has become the most essential and inevitable medium of learning in primary,secondary and higher education.In recent times,Massive Open Online Courses(MOOCs)have transformed the current education strategy by offering a technology-rich and flexible form of online learning.A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question(MCQ)assessment in most of the MOOC courses.Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score.This is due to the possibility of random and smart guesses,learners can attempt,as MCQ assessments are more vulnerable than essay type assessments.This paper presents the architecture,development,evaluation of the I-Quiz system,an intelligent assessment tool,which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level.The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques.The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners.A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming.The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period.The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner.The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.展开更多
In today’s world of connectivity there is a huge amount of data than we could imagine.The number of network users are increasing day by day and there are large number of social networks which keeps the users connecte...In today’s world of connectivity there is a huge amount of data than we could imagine.The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time.These social networks give the complete independence to the user to post the data either political,commercial or entertainment value.Some data may be sensitive and have a greater impact on the society as a result.The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter.Due to the large user base and its openness there is a huge possibility to spread spam messages in this network.Spam detection is a technique to identify and mark data as a false data value.There are lot of machine learning approaches proposed to detect spam in social networks.The efficiency of any spam detection algorithm is determined by its cost factor and accuracy.Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language.The performance of the proposed model is evaluated using simulation results.展开更多
文摘The growth of Wireless Sensor Networks(WSNs)has revolutionized thefield of technology and it is used in different application frameworks.Unmanned edges and other critical locations can be monitored using the navigation sensor node.The WSN required low energy consumption to provide a high network and guarantee the ultimate goal.The main objective of this work is to propose hybrid energy optimization in local aware environments.The hybrid proposed work consists of clustering,optimization,direct and indirect communication and routing.The aim of this research work is to provide and framework for reduced energy and trusted communication with the shortest path to reach source to destination in WSN and an extending lifetime of wireless sensors.The proposed Artificial Fish Swarm Optimization algorithm is used for energy optimization in military applications which is simulated using Network Simulator(NS)tool.This work optimizes the energy level and the same is compared with various genetic algorithms(GA)and also the cluster selection process was compared with thefission-fusion(FF)selection method.The results of the proposed work show,improvement in energy optimization,throughput and time delay.
文摘Electronic learning(e-learning)has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web.Post covid-19 pandemic,online learning has become the most essential and inevitable medium of learning in primary,secondary and higher education.In recent times,Massive Open Online Courses(MOOCs)have transformed the current education strategy by offering a technology-rich and flexible form of online learning.A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question(MCQ)assessment in most of the MOOC courses.Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score.This is due to the possibility of random and smart guesses,learners can attempt,as MCQ assessments are more vulnerable than essay type assessments.This paper presents the architecture,development,evaluation of the I-Quiz system,an intelligent assessment tool,which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level.The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques.The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners.A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming.The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period.The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner.The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.
文摘In today’s world of connectivity there is a huge amount of data than we could imagine.The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time.These social networks give the complete independence to the user to post the data either political,commercial or entertainment value.Some data may be sensitive and have a greater impact on the society as a result.The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter.Due to the large user base and its openness there is a huge possibility to spread spam messages in this network.Spam detection is a technique to identify and mark data as a false data value.There are lot of machine learning approaches proposed to detect spam in social networks.The efficiency of any spam detection algorithm is determined by its cost factor and accuracy.Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language.The performance of the proposed model is evaluated using simulation results.
