Journal of Internet Services and Information Security
The main objective of this work is to design an effective web service discovery engine that retri... more The main objective of this work is to design an effective web service discovery engine that retrieves the best matching results based on the measure of syntactic cum semantic similarity between the user query and the web service to be fetched. The idea is to draw bridges across the broad spectrum of lexical entities based on their relativeness. The essence of this work could be extended to support a wide range of applications from ‘being inculcated in search engines to fetch user-relevant data’ to ‘being used for training robots and AI based devices to respond/adhere appropriately to the different phrases of human commands’.With the accelerated revolution of internet, enterprises and organizations highly rely on Service oriented computing. Web services support inter-operation of distributed applications. Humongous amount of web services present on the internet the user is searching for. The paper focuses ondesigninga practical means of fast and relevant retrieval of web services.The...
Machine Learning Methods for Online Education Case
2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)
Online education has become a popular choice for learners of all ages and backgrounds due to its ... more Online education has become a popular choice for learners of all ages and backgrounds due to its accessibility and flexibility. However, providing personalized learning experiences for a diverse range of students in online education can be challenging. Machine learning methods can be used to provide personalized learning experiences and improve student engagement in online education. In this case study, We're going to do some research on machine learning. methods in an online education platform. The platform provides courses in various subjects and is designed to be accessible to students from all over the world. The platform collects data on student behavior, such as the courses they enroll in, the time they spend on each course, and their performance on assignments and quizzes. We will explore several machine learning methods that can be applied to this data, including clustering, classification, and recommendation systems. Clustering algorithms can be used to group students based on their learning behavior and preferences, allowing instructors to provide personalized feedback and course recommendations. Classification algorithms can be used to predict student success in a particular course, allowing instructors to intervene and provide additional support if needed. Recommendation systems can be used to suggest courses to students based on their interests and past behavior. We will also discuss the potential benefits and challenges of using machine learning methods in online education. Benefits include increased student engagement, improved learning outcomes, and more efficient use of resources. Challenges include ensuring data privacy and security, preventing algorithmic bias, and maintaining transparency and fairness in the decisionmaking process. Overall, machine learning methods have the potential to transform online education by providing personalized learning experiences and improving student outcomes. By leveraging the vast amounts of data generated by online education platforms, we can create more effective and efficient learning experiences that meet the needs of students from diverse backgrounds and learning styles. Keywords : Machine Learning, Education, Online class, etc.,
Cloud Broker for Reputation-Enhanced and QoS based IaaS Service Selection
Cloud brokering service is gaining momentum as a promising area of research and business opportun... more Cloud brokering service is gaining momentum as a promising area of research and business opportunity. As cloud computing enables the user to obtain the infrastructure, platform and software required as a service, there is no need to invest huge amount as the capital to run the business and hence more and more companies are moving to the cloud. But collecting the information from the multiple cloud service providers and comparing the various services in order to zero down the right service provider is a time consuming task given the plethora and the variety of cloud service offerings. As many cloud service providers are entering the market, the users look for the cloud brokers who could help them in the dynamic selection and aggregation of services based on their Quality of Service (QoS) requirements and the cost. The other important aspects considered by the users while selecting the service provider is their reputation. This paper introduces a Cloud Broker Service (CBS) framework t...
International Journal of Intelligent Engineering and Systems, 2018
Social networking websites have become an easiest way to make the common people thoughts and revi... more Social networking websites have become an easiest way to make the common people thoughts and reviews to become public. Among those websites, Twitter data's are in boom, because of heavy interests of people to update their information in that website. Detection of communities for Twitter data has already been done by the other authors, but still communities detected with high strength or quality are lagging behind. In this paper, the data collected from Twitter have gone through sentiment analysis and the final scores of that analysis have been used for the plotting of the graph which acts as an input to the community detection algorithm. The twitter data's communities were detected with the detection of noise too, and upon removal of those noisy data, the strength of the detected communities used to get increase. The detection of the outliers or noise has been done with the help of DBSCAN algorithm and the communities have been detected by Newman Girvan algorithm. In this study the proposed sentiment analysis algorithm and the community detection technique have been successfully implemented and evaluated. The results from the collected data sets from Twitter have shown the communities, which were properly detected with the help of the proposed methodology. The communities were actually grouped according the sentiment scores derived and the number of words, for each tweets. Each community shows the connection according the high and low sentiment scores. The quality of the detected communities has been measured by centrality, modularity and conductance and has been compared with four other community detection algorithms i.e. with Louvain, Walktrap, Leading Eigenvector and Fast Greedy algorithms. The results were positive in maximum times when compared on the basis of the considered metrics with the other community detection algorithms.
International Journal of Intelligent Engineering and Systems, 2017
Throughout the numerous processing in cloud computing, an essential concern is to scheduling jobs... more Throughout the numerous processing in cloud computing, an essential concern is to scheduling jobs in parallel to cloud data centers. An aim of this paper is to design a Fuzzy logic system. Gang forming with scheduling policy improves the performance of computation cost and time of different cloud workloads. In this work a novel framework for cloud workload management is modelled. In which clustering of cloud workload is done based on Manhattan distance based fuzzy clustering and scheduling of workload is done based on Compress & Join Gang Polling Evaluation scheduling algorithm (C&JGPESA). In order to provide effective utilization of resources, Gang scheduling algorithm is used based on their performance to fit the same number of applications in less time slots. Finally proposed work comparison is compared with computation cost, make span and response time and it achieves around 60%, 97% and 80% greater performance than both existing works.
Electronic Health Records (EHRs) have undergone numerous technical improvements in recent years, ... more Electronic Health Records (EHRs) have undergone numerous technical improvements in recent years, including the incorporation of mobile devices with the cloud computing technologies to facilitate medical data exchanges between patients and the healthcare professionals. This cutting-edge architecture enables cyber physical systems housed in the cloud to provide healthcare services with minimal operational costs, high flexibility, security, and EHR accessibility. If patient health information is stored in the hospital database, there will always be a risk of intrusion, i.e., unauthorized file access and information modification by attackers. To address this concern, a decentralized EHR system based on Blockchain technology is proposed. To facilitate secure EHR exchange across various patients and medical providers, a reliable access control method based on smart contracts is developed. The cryptocurrency, specifically Ethereum, is incorporated in the suggested system to protect sensitive health information from potential attackers. In the suggested approach, both physicians and patients are required to be authenticated. Patients can register, and a block with a unique hash value will be generated. Once the patient discusses the disease with the physician, the physician can check the patient's condition and offer drugs. For experimental findings, the public Block chain Ganache and solidity remixbased smart contracts to protect privacy is applied and the Ethers are used as the crypto currencies.
Information retrieval mechanisms from the web are a great need of the hour as the amount of the c... more Information retrieval mechanisms from the web are a great need of the hour as the amount of the content is growing dynamically every day. There are many algorithms which have been proposed in literature mainly relying on the output of the search engines. These algorithms are either content based or snippet based and perform a clustered outcome re-ranking of the content for the user. This work proposes a hybrid approach to content clustering that combines the best of the web information retrieval methods and also uses the personal preference information of the users modeling a wide range of contexts. This work introduces a context mechanism of the users in the overall process and presents taxonomy of the methods to organize the output of the search engines. Experimental results are promising and show that this approach has great promise for a wide range of queries.
Innovative Data Communication Technologies and Application
In India, some of the bollywood movies like the "Lord of the rings"; "Avatar" etc. are exemplific... more In India, some of the bollywood movies like the "Lord of the rings"; "Avatar" etc. are exemplifications of the magic that the Visual Effects (VFX) and the 3D animation techniques could create in a movie. In order to create this magic, the animation frames undergo the "rendering" process which takes a long time and contributes to long production time as it is a computationally intensive task. In order to decrease the production time and to satiate the high quality demand; the animation studios have resorted to the cloud renderfarm services. However, the animator's community betrothed to use the services are baffled by the plethora of cloud renderfarm service models and need a classification framework and a recommender system to recommend appropriate services. This work classifies the cloud renderfarm service models by identifying three key characteristics of the services and their components based on which the services could be recommended. The three key characteristics identified are: (a) Functional characteristics, (b) Non Functional characteristics and (c) Cost. The classification helps to analyze and compare the cloud renderfarm services based on these three key characteristics and identify and recommend the suitable, cost effective services for a particular rendering job. The proposed classification framework is explained and verified using the analysis and comparison of 11 popular cloud renderfarm services which could be incorporated in building a recommender system for cloud renderfarm services.
This paper analyses the extent to which financial integration impacts the manner in which terms o... more This paper analyses the extent to which financial integration impacts the manner in which terms of trade affect business cycles in emerging economies. Using a small open economy model, we show that as capital account openness increases in an economy that faces trade shocks, business cycle volatility reduces. For an economy with limited financial openness, and a relatively open trade account, a model with exogenous terms of trade shocks is able to replicate the features of the business cycle.
Mobile Ontology Design for Semantic Web: A Case Study
Malaysian Journal of Computer Science, 2011
The era of ubiquitous computing and communication has started to change the dynamics of design of... more The era of ubiquitous computing and communication has started to change the dynamics of design of systems in a big way. Mobile computing has become the norm and solutions that can automatically understand user intent are the need of the hour. For this, ontology design for mobile computing is crucial. Designing ontology systems that are tailored towards heterogeneous mobile environments imposes demands such as data management, reasoning systems and memory efficiency. The systems must be expressive to include the dimensions of the context, scalable and focused towards the users. In this paper we discuss the design of the ontology for mobile environment and a semantic encoding system that can manage the data. The experimental results show the promise of the work and applicability to various operations in the domain.
Journal of Internet Services and Information Security
The main objective of this work is to design an effective web service discovery engine that retri... more The main objective of this work is to design an effective web service discovery engine that retrieves the best matching results based on the measure of syntactic cum semantic similarity between the user query and the web service to be fetched. The idea is to draw bridges across the broad spectrum of lexical entities based on their relativeness. The essence of this work could be extended to support a wide range of applications from ‘being inculcated in search engines to fetch user-relevant data’ to ‘being used for training robots and AI based devices to respond/adhere appropriately to the different phrases of human commands’.With the accelerated revolution of internet, enterprises and organizations highly rely on Service oriented computing. Web services support inter-operation of distributed applications. Humongous amount of web services present on the internet the user is searching for. The paper focuses ondesigninga practical means of fast and relevant retrieval of web services.The...
Machine Learning Methods for Online Education Case
2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)
Online education has become a popular choice for learners of all ages and backgrounds due to its ... more Online education has become a popular choice for learners of all ages and backgrounds due to its accessibility and flexibility. However, providing personalized learning experiences for a diverse range of students in online education can be challenging. Machine learning methods can be used to provide personalized learning experiences and improve student engagement in online education. In this case study, We're going to do some research on machine learning. methods in an online education platform. The platform provides courses in various subjects and is designed to be accessible to students from all over the world. The platform collects data on student behavior, such as the courses they enroll in, the time they spend on each course, and their performance on assignments and quizzes. We will explore several machine learning methods that can be applied to this data, including clustering, classification, and recommendation systems. Clustering algorithms can be used to group students based on their learning behavior and preferences, allowing instructors to provide personalized feedback and course recommendations. Classification algorithms can be used to predict student success in a particular course, allowing instructors to intervene and provide additional support if needed. Recommendation systems can be used to suggest courses to students based on their interests and past behavior. We will also discuss the potential benefits and challenges of using machine learning methods in online education. Benefits include increased student engagement, improved learning outcomes, and more efficient use of resources. Challenges include ensuring data privacy and security, preventing algorithmic bias, and maintaining transparency and fairness in the decisionmaking process. Overall, machine learning methods have the potential to transform online education by providing personalized learning experiences and improving student outcomes. By leveraging the vast amounts of data generated by online education platforms, we can create more effective and efficient learning experiences that meet the needs of students from diverse backgrounds and learning styles. Keywords : Machine Learning, Education, Online class, etc.,
Cloud Broker for Reputation-Enhanced and QoS based IaaS Service Selection
Cloud brokering service is gaining momentum as a promising area of research and business opportun... more Cloud brokering service is gaining momentum as a promising area of research and business opportunity. As cloud computing enables the user to obtain the infrastructure, platform and software required as a service, there is no need to invest huge amount as the capital to run the business and hence more and more companies are moving to the cloud. But collecting the information from the multiple cloud service providers and comparing the various services in order to zero down the right service provider is a time consuming task given the plethora and the variety of cloud service offerings. As many cloud service providers are entering the market, the users look for the cloud brokers who could help them in the dynamic selection and aggregation of services based on their Quality of Service (QoS) requirements and the cost. The other important aspects considered by the users while selecting the service provider is their reputation. This paper introduces a Cloud Broker Service (CBS) framework t...
International Journal of Intelligent Engineering and Systems, 2018
Social networking websites have become an easiest way to make the common people thoughts and revi... more Social networking websites have become an easiest way to make the common people thoughts and reviews to become public. Among those websites, Twitter data's are in boom, because of heavy interests of people to update their information in that website. Detection of communities for Twitter data has already been done by the other authors, but still communities detected with high strength or quality are lagging behind. In this paper, the data collected from Twitter have gone through sentiment analysis and the final scores of that analysis have been used for the plotting of the graph which acts as an input to the community detection algorithm. The twitter data's communities were detected with the detection of noise too, and upon removal of those noisy data, the strength of the detected communities used to get increase. The detection of the outliers or noise has been done with the help of DBSCAN algorithm and the communities have been detected by Newman Girvan algorithm. In this study the proposed sentiment analysis algorithm and the community detection technique have been successfully implemented and evaluated. The results from the collected data sets from Twitter have shown the communities, which were properly detected with the help of the proposed methodology. The communities were actually grouped according the sentiment scores derived and the number of words, for each tweets. Each community shows the connection according the high and low sentiment scores. The quality of the detected communities has been measured by centrality, modularity and conductance and has been compared with four other community detection algorithms i.e. with Louvain, Walktrap, Leading Eigenvector and Fast Greedy algorithms. The results were positive in maximum times when compared on the basis of the considered metrics with the other community detection algorithms.
International Journal of Intelligent Engineering and Systems, 2017
Throughout the numerous processing in cloud computing, an essential concern is to scheduling jobs... more Throughout the numerous processing in cloud computing, an essential concern is to scheduling jobs in parallel to cloud data centers. An aim of this paper is to design a Fuzzy logic system. Gang forming with scheduling policy improves the performance of computation cost and time of different cloud workloads. In this work a novel framework for cloud workload management is modelled. In which clustering of cloud workload is done based on Manhattan distance based fuzzy clustering and scheduling of workload is done based on Compress & Join Gang Polling Evaluation scheduling algorithm (C&JGPESA). In order to provide effective utilization of resources, Gang scheduling algorithm is used based on their performance to fit the same number of applications in less time slots. Finally proposed work comparison is compared with computation cost, make span and response time and it achieves around 60%, 97% and 80% greater performance than both existing works.
Electronic Health Records (EHRs) have undergone numerous technical improvements in recent years, ... more Electronic Health Records (EHRs) have undergone numerous technical improvements in recent years, including the incorporation of mobile devices with the cloud computing technologies to facilitate medical data exchanges between patients and the healthcare professionals. This cutting-edge architecture enables cyber physical systems housed in the cloud to provide healthcare services with minimal operational costs, high flexibility, security, and EHR accessibility. If patient health information is stored in the hospital database, there will always be a risk of intrusion, i.e., unauthorized file access and information modification by attackers. To address this concern, a decentralized EHR system based on Blockchain technology is proposed. To facilitate secure EHR exchange across various patients and medical providers, a reliable access control method based on smart contracts is developed. The cryptocurrency, specifically Ethereum, is incorporated in the suggested system to protect sensitive health information from potential attackers. In the suggested approach, both physicians and patients are required to be authenticated. Patients can register, and a block with a unique hash value will be generated. Once the patient discusses the disease with the physician, the physician can check the patient's condition and offer drugs. For experimental findings, the public Block chain Ganache and solidity remixbased smart contracts to protect privacy is applied and the Ethers are used as the crypto currencies.
Information retrieval mechanisms from the web are a great need of the hour as the amount of the c... more Information retrieval mechanisms from the web are a great need of the hour as the amount of the content is growing dynamically every day. There are many algorithms which have been proposed in literature mainly relying on the output of the search engines. These algorithms are either content based or snippet based and perform a clustered outcome re-ranking of the content for the user. This work proposes a hybrid approach to content clustering that combines the best of the web information retrieval methods and also uses the personal preference information of the users modeling a wide range of contexts. This work introduces a context mechanism of the users in the overall process and presents taxonomy of the methods to organize the output of the search engines. Experimental results are promising and show that this approach has great promise for a wide range of queries.
Innovative Data Communication Technologies and Application
In India, some of the bollywood movies like the "Lord of the rings"; "Avatar" etc. are exemplific... more In India, some of the bollywood movies like the "Lord of the rings"; "Avatar" etc. are exemplifications of the magic that the Visual Effects (VFX) and the 3D animation techniques could create in a movie. In order to create this magic, the animation frames undergo the "rendering" process which takes a long time and contributes to long production time as it is a computationally intensive task. In order to decrease the production time and to satiate the high quality demand; the animation studios have resorted to the cloud renderfarm services. However, the animator's community betrothed to use the services are baffled by the plethora of cloud renderfarm service models and need a classification framework and a recommender system to recommend appropriate services. This work classifies the cloud renderfarm service models by identifying three key characteristics of the services and their components based on which the services could be recommended. The three key characteristics identified are: (a) Functional characteristics, (b) Non Functional characteristics and (c) Cost. The classification helps to analyze and compare the cloud renderfarm services based on these three key characteristics and identify and recommend the suitable, cost effective services for a particular rendering job. The proposed classification framework is explained and verified using the analysis and comparison of 11 popular cloud renderfarm services which could be incorporated in building a recommender system for cloud renderfarm services.
This paper analyses the extent to which financial integration impacts the manner in which terms o... more This paper analyses the extent to which financial integration impacts the manner in which terms of trade affect business cycles in emerging economies. Using a small open economy model, we show that as capital account openness increases in an economy that faces trade shocks, business cycle volatility reduces. For an economy with limited financial openness, and a relatively open trade account, a model with exogenous terms of trade shocks is able to replicate the features of the business cycle.
Mobile Ontology Design for Semantic Web: A Case Study
Malaysian Journal of Computer Science, 2011
The era of ubiquitous computing and communication has started to change the dynamics of design of... more The era of ubiquitous computing and communication has started to change the dynamics of design of systems in a big way. Mobile computing has become the norm and solutions that can automatically understand user intent are the need of the hour. For this, ontology design for mobile computing is crucial. Designing ontology systems that are tailored towards heterogeneous mobile environments imposes demands such as data management, reasoning systems and memory efficiency. The systems must be expressive to include the dimensions of the context, scalable and focused towards the users. In this paper we discuss the design of the ontology for mobile environment and a semantic encoding system that can manage the data. The experimental results show the promise of the work and applicability to various operations in the domain.
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Papers by Aisha Banu