Pub Date : 2018-08-29DOI: 10.24297/IJCT.V17I2.7584
R. A. Al-Arasi, Anwar Saif
Nowadays, cloud computing makes it possible for users to use the computing resources like application, software, and hardware, etc., on pay as use model via the internet. One of the core and challenging issue in cloud computing is the task scheduling. Task scheduling problem is an NP-hard problem and is responsible for mapping the tasks to resources in a way to spread the load evenly. The appropriate mapping between resources and tasks reduces makespan and maximizes resource utilization. In this paper, we present and implement an independent task scheduling algorithm that assigns the users' tasks to multiple computing resources. The proposed algorithm is a hybrid algorithm for task scheduling in cloud computing based on a genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm is implemented and simulated using CloudSim simulator. The simulation results show that our proposed algorithm outperforms the GA and PSO algorithms by decreasing the makespan and increasing the resource utilization.
{"title":"HTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment","authors":"R. A. Al-Arasi, Anwar Saif","doi":"10.24297/IJCT.V17I2.7584","DOIUrl":"https://doi.org/10.24297/IJCT.V17I2.7584","url":null,"abstract":"Nowadays, cloud computing makes it possible for users to use the computing resources like application, software, and hardware, etc., on pay as use model via the internet. One of the core and challenging issue in cloud computing is the task scheduling. Task scheduling problem is an NP-hard problem and is responsible for mapping the tasks to resources in a way to spread the load evenly. The appropriate mapping between resources and tasks reduces makespan and maximizes resource utilization. In this paper, we present and implement an independent task scheduling algorithm that assigns the users' tasks to multiple computing resources. The proposed algorithm is a hybrid algorithm for task scheduling in cloud computing based on a genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm is implemented and simulated using CloudSim simulator. The simulation results show that our proposed algorithm outperforms the GA and PSO algorithms by decreasing the makespan and increasing the resource utilization.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131553784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-29DOI: 10.24297/IJCT.V17I2.7674
Isha Chawla
Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Internet technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner.Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. On cloud computing platform, load balancing of the entire system can be dynamically handled by using virtualization technology through which it becomes possible to remap virtual machine and physical resources according to the change in load. However, in order to improve performance, the virtual machines have to fully utilize its resources and services by adapting to computing environment dynamically. The load balancing with proper allocation of resources must be guaranteed in order to improve resource utility. Load balancing is a critical aspect that ensures that all the resources and entities are well balanced such that no resource or entity neither is under loaded nor overloaded. The load balancing algorithms can be static or dynamic. Load balancing in this environment means equal distribution of workload across all the nodes. Load balancing provides a way of achieving the proper utilization of resources and better user satisfaction. Hence, use of an appropriate load balancing algorithm is necessary for selecting the virtual machines or servers. This paper focuses on the load balancing algorithm which distributes the incoming jobs among VMs optimally in cloud data centers. In this paper, we have reviewed several existing load balancing mechanisms and we have tried to address the problems associated with them.
{"title":"Cloud Computing Environment: A Review","authors":"Isha Chawla","doi":"10.24297/IJCT.V17I2.7674","DOIUrl":"https://doi.org/10.24297/IJCT.V17I2.7674","url":null,"abstract":"Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Internet technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner.Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure \"in the cloud\" that supports them. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. On cloud computing platform, load balancing of the entire system can be dynamically handled by using virtualization technology through which it becomes possible to remap virtual machine and physical resources according to the change in load. However, in order to improve performance, the virtual machines have to fully utilize its resources and services by adapting to computing environment dynamically. The load balancing with proper allocation of resources must be guaranteed in order to improve resource utility. Load balancing is a critical aspect that ensures that all the resources and entities are well balanced such that no resource or entity neither is under loaded nor overloaded. The load balancing algorithms can be static or dynamic. Load balancing in this environment means equal distribution of workload across all the nodes. Load balancing provides a way of achieving the proper utilization of resources and better user satisfaction. Hence, use of an appropriate load balancing algorithm is necessary for selecting the virtual machines or servers. This paper focuses on the load balancing algorithm which distributes the incoming jobs among VMs optimally in cloud data centers. In this paper, we have reviewed several existing load balancing mechanisms and we have tried to address the problems associated with them.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127161132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-16DOI: 10.24297/IJCT.V17I2.7606
Bohar Singh, Mehak Aggarwal
Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using KNN algorithm We have used steerable pyramid to extract texture features from query image and classified database images and store them in feature features. Therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time.
{"title":"Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism","authors":"Bohar Singh, Mehak Aggarwal","doi":"10.24297/IJCT.V17I2.7606","DOIUrl":"https://doi.org/10.24297/IJCT.V17I2.7606","url":null,"abstract":"Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using KNN algorithm We have used steerable pyramid to extract texture features from query image and classified database images and store them in feature features. Therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131636240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-16DOI: 10.24297/IJCT.V17I2.7607
Bohar Singh, Mehak Aggarwal
In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s. To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database. These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color. For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing. Since anautomated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.
{"title":"A Review On Content Based Image Retrieval","authors":"Bohar Singh, Mehak Aggarwal","doi":"10.24297/IJCT.V17I2.7607","DOIUrl":"https://doi.org/10.24297/IJCT.V17I2.7607","url":null,"abstract":"In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s. To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database. These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color. For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing. Since anautomated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126049197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-30DOI: 10.24297/IJCT.V17I2.7551
Gurjeet Singh, M. Garg
Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Cloud computing uses the internet and the central remote servers to support different data and applications. It is an internet based technology. It permits the users to approach their personal files at any computer with internet access. The cloud computing flexibility is a function of the allocation of resources on authority’s request. Cloud computing provides the act of uniting. Scientific computing in the 21st century has evolved from fixed to distributed work environment. The current trend of CloudComputing (CC) allows accessing business applications from anywhere just by connecting to the Internet. Evidence shows that, switching to CC organizations' annual expenditure and maintenance are being reduced to a greater extent. However, there are several challenges that come along with various benefits of cloud computing. Among these include securityaspects. Our aim is to identify security challenges for adapting cloud computing and their solutions from real world for the challenge that do not have any proper mitigation strategies identified. This non-existence of global standards and guidelines could be help academics to know the state of practice and formulatebetter methods/standards to provide secure interoperability. The identified cloud computing security challenges and solutions can be referred by practitioners to understand which areas of security need to be concentrated while adapting/migrating to a cloud computing environment.
{"title":"Data Security In Cloud Computing: A Review","authors":"Gurjeet Singh, M. Garg","doi":"10.24297/IJCT.V17I2.7551","DOIUrl":"https://doi.org/10.24297/IJCT.V17I2.7551","url":null,"abstract":"Cloud computing is Internet (\"cloud\") based development and use of computer technology (\"computing\"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Cloud computing uses the internet and the central remote servers to support different data and applications. It is an internet based technology. It permits the users to approach their personal files at any computer with internet access. The cloud computing flexibility is a function of the allocation of resources on authority’s request. Cloud computing provides the act of uniting. Scientific computing in the 21st century has evolved from fixed to distributed work environment. The current trend of CloudComputing (CC) allows accessing business applications from anywhere just by connecting to the Internet. Evidence shows that, switching to CC organizations' annual expenditure and maintenance are being reduced to a greater extent. However, there are several challenges that come along with various benefits of cloud computing. Among these include securityaspects. Our aim is to identify security challenges for adapting cloud computing and their solutions from real world for the challenge that do not have any proper mitigation strategies identified. This non-existence of global standards and guidelines could be help academics to know the state of practice and formulatebetter methods/standards to provide secure interoperability. The identified cloud computing security challenges and solutions can be referred by practitioners to understand which areas of security need to be concentrated while adapting/migrating to a cloud computing environment.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132173547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-06DOI: 10.24297/IJCT.V17I2.7168
Doaa M. Hasan, A. Eldin, Ayman E. Khedr, H. Fahmy
Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear, which makes it not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer, and discuss the limitations and challenges of these methods.
{"title":"In-Silico Methodologies for Cancer Multidrug Optimization","authors":"Doaa M. Hasan, A. Eldin, Ayman E. Khedr, H. Fahmy","doi":"10.24297/IJCT.V17I2.7168","DOIUrl":"https://doi.org/10.24297/IJCT.V17I2.7168","url":null,"abstract":"Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear, which makes it not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer, and discuss the limitations and challenges of these methods.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127513384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless Ad-hoc networks have lack of mass deployment. An Ad hoc wireless network has a dynamic nature that leads to constant changes in its network topology. There is an infrastructure less and decentralized network which need a robust dynamic routing protocol. This article presents performance comparison of wireless Ad-hoc network on different routing protocols. Network simulator QualNet 5.0.2 has been used to evaluate the performance of wireless networks with various routing protocols.
{"title":"Comparative Performance Analysis of Different Routing Protocols in Wireless Ad-hoc networks (IEEE 802.11)","authors":"Mahendra Kumar, A. Jain","doi":"10.24297/IJCT.V8I3.3398","DOIUrl":"https://doi.org/10.24297/IJCT.V8I3.3398","url":null,"abstract":"Wireless Ad-hoc networks have lack of mass deployment. An Ad hoc wireless network has a dynamic nature that leads to constant changes in its network topology. There is an infrastructure less and decentralized network which need a robust dynamic routing protocol. This article presents performance comparison of wireless Ad-hoc network on different routing protocols. Network simulator QualNet 5.0.2 has been used to evaluate the performance of wireless networks with various routing protocols.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124409419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-21DOI: 10.24297/IJCT.V4I2C1.4179
R. Gowri, S. Kanmani, M. Santhosh, S. Naresh
This paper proposes an adaptive and interactive agent based intelligent tutoring system for cognitive ability realization and improvement (ITSCARE - Intelligent Tutoring System for Cognitive Ability Realization and Improvement). ITSCARE allows the learners to realize their cognitive ability and to improve their cognition while studying the course. It provides different types of course materials which are dynamically adapted. It also increases the confidence level of the learners and provides an effective learning experience. This system also provides game based learning which influences the learners to get motivated and focus on the course. After the completion of each chapter, a test is conducted to predict the cognitive ability where students are assessed using their help seeking skills during the test and cognitive skill factors such as memory, concentration, attention in detail etc,. It uses politeness style to provide the test results and feed back to the students which keep the learner interest in the subject. Collaborative learning among the learner is improved by conducting quiz competition where a group of students participate and a winner is chosen. It uses different type of software agent to predict and improve the cognitive skill.
本文提出了一种基于自适应交互式agent的认知能力实现与提升智能辅导系统(ITSCARE—intelligent tutoring system for cognitive ability realization and improvement)。ITSCARE让学习者在学习过程中认识到自己的认知能力，提高自己的认知能力。它提供了动态调整的不同类型的课程材料。它还增加了学习者的信心水平，并提供了有效的学习经验。该系统还提供基于游戏的学习，从而影响学习者获得动力并专注于课程。每章完成后，进行认知能力预测测试，利用学生在测试中的求助技能和认知技能因素，如记忆力、注意力、细节注意力等，评估学生的认知能力。它采用礼貌的方式向学生提供测试结果并反馈给学生，从而保持学习者对主题的兴趣。通过进行一组学生参加并选出获胜者的智力竞赛，提高了学习者之间的协作学习。它使用不同类型的软件代理来预测和提高认知技能。
{"title":"An Adaptive and Interactive Agent Based ITS for Cognitive Skill Prediction and Improvement","authors":"R. Gowri, S. Kanmani, M. Santhosh, S. Naresh","doi":"10.24297/IJCT.V4I2C1.4179","DOIUrl":"https://doi.org/10.24297/IJCT.V4I2C1.4179","url":null,"abstract":"This paper proposes an adaptive and interactive agent based intelligent tutoring system for cognitive ability realization and improvement (ITSCARE - Intelligent Tutoring System for Cognitive Ability Realization and Improvement). ITSCARE allows the learners to realize their cognitive ability and to improve their cognition while studying the course. It provides different types of course materials which are dynamically adapted. It also increases the confidence level of the learners and provides an effective learning experience. This system also provides game based learning which influences the learners to get motivated and focus on the course. After the completion of each chapter, a test is conducted to predict the cognitive ability where students are assessed using their help seeking skills during the test and cognitive skill factors such as memory, concentration, attention in detail etc,. It uses politeness style to provide the test results and feed back to the students which keep the learner interest in the subject. Collaborative learning among the learner is improved by conducting quiz competition where a group of students participate and a winner is chosen. It uses different type of software agent to predict and improve the cognitive skill.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126478088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-21DOI: 10.24297/IJCT.V4I2C1.4181
Rachita Sharma, S. Dubey
This paper describes the introduction of Supervised and Unsupervised Techniques with the comparison of SOFM (Self Organized Feature Map) used for Satellite Imagery. In this we have explained the way of spatial and temporal changes detection used in forecasting in satellite imagery. Forecasting is based on time series of images using Artificial Neural Network. Recently neural networks have gained a lot of interest in time series prediction due to their ability to learn effectively nonlinear dependencies from large volume of possibly noisy data with a learning algorithm. Unsupervised neural networks reveal useful information from the temporal sequence and they reported power in cluster analysis and dimensionality reduction. In unsupervised learning, no pre classification and pre labeling of the input data is needed. SOFM is one of the unsupervised neural network used for time series prediction .In time series prediction the goal is to construct a model that can predict the future of the measured process under interest. There are various approaches to time series prediction that have been used over the years. It is a research area having application in diverse fields like weather forecasting, speech recognition, remote sensing. Advances in remote sensing technology and availability of high resolution images in recent years have motivated many researchers to study patterns in the images for the purpose of trend analysis
{"title":"ANALYSIS OF SOM & SOFM TECHNIQUES USED IN SATELLITE IMAGERY","authors":"Rachita Sharma, S. Dubey","doi":"10.24297/IJCT.V4I2C1.4181","DOIUrl":"https://doi.org/10.24297/IJCT.V4I2C1.4181","url":null,"abstract":"This paper describes the introduction of Supervised and Unsupervised Techniques with the comparison of SOFM (Self Organized Feature Map) used for Satellite Imagery. In this we have explained the way of spatial and temporal changes detection used in forecasting in satellite imagery. Forecasting is based on time series of images using Artificial Neural Network. Recently neural networks have gained a lot of interest in time series prediction due to their ability to learn effectively nonlinear dependencies from large volume of possibly noisy data with a learning algorithm. Unsupervised neural networks reveal useful information from the temporal sequence and they reported power in cluster analysis and dimensionality reduction. In unsupervised learning, no pre classification and pre labeling of the input data is needed. SOFM is one of the unsupervised neural network used for time series prediction .In time series prediction the goal is to construct a model that can predict the future of the measured process under interest. There are various approaches to time series prediction that have been used over the years. It is a research area having application in diverse fields like weather forecasting, speech recognition, remote sensing. Advances in remote sensing technology and availability of high resolution images in recent years have motivated many researchers to study patterns in the images for the purpose of trend analysis","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128947582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-21DOI: 10.24297/IJCT.V4I2C1.4174
P. Bhattacharya, Shilpi Gupta, Nupur Pal
Wireless Sensor Networks are proving themselves as a boon to the surroundings and its deployment is a prominent area to be considered for minimum energy consumption. In this paper, we have considered deployment of a sensor grid network for 36 and 100 number of sensor nodes. Diagonally opposite nodes are considered to be source and destination. Using a modified energy model, the energy consumption for direct path and that with different number of relays are calculated. The maximum distance up to which direct transmission is possible and the number of required relays for higher transmission distances are then found out.
{"title":"Determination of Optimal Number of Relays Using a New Energy Model for WSN","authors":"P. Bhattacharya, Shilpi Gupta, Nupur Pal","doi":"10.24297/IJCT.V4I2C1.4174","DOIUrl":"https://doi.org/10.24297/IJCT.V4I2C1.4174","url":null,"abstract":"Wireless Sensor Networks are proving themselves as a boon to the surroundings and its deployment is a prominent area to be considered for minimum energy consumption. In this paper, we have considered deployment of a sensor grid network for 36 and 100 number of sensor nodes. Diagonally opposite nodes are considered to be source and destination. Using a modified energy model, the energy consumption for direct path and that with different number of relays are calculated. The maximum distance up to which direct transmission is possible and the number of required relays for higher transmission distances are then found out.","PeriodicalId":161820,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114707209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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