Future Computing and Informatics Journal最新文献

Transition region based approaches are recent hybrid segmentation techniques well known for its simplicity and effectiveness. Here, the segmentation effectiveness depends on robust extraction of transition regions. So, we have proposed transition region extraction method for image segmentation. The proposed method initially decomposes the gray image in wavelet domain. Local standard deviation filtering and thresholding operation is used to extract transition region feature matrix. Using this feature matrix, the corresponding prominent wavelet coefficients of different bands are found. The inverse wavelet transform is then applied to the modified coefficients to get edge image with more than one-pixel width. Global thresholding is applied to get transition regions. Further, it undergoes morphological thinning and region filling operation to extract the object regions. Finally, the objects are extracted using the object regions. The proposed method is compared with different image segmentation methods. An experimental result reveals that the proposed method outperforms other methods for segmentation of images containing single and multiple objects. The proposed method can also be applied for worm separation from leaves.

Hidden web contains huge amount of high quality data which are not indexed to search engines. Hidden web refers to web pages which are generated dynamically by embedding backend data matching the search keywords, in server-side templates. They are created for human consumption and makes automated processing cumbersome since structured data is embedded within unstructured HTML tags. In order to enable machine processing, structured data must be detected, extracted and annotated. Many heuristic based approaches DeLa [1], MSAA [2] are available in the literature to perform automatic annotation. Most of these techniques fail if data values didn't contain labels present as part of the attribute value itself or if it is not available explicitly as part of the form interface or query response pages. The proposed technique addresses this issue by collecting domain keywords from multiple websites belonging to the business domain of interest and then, it captures the pattern in the form of semantic rules. Experimental results show that single heuristics is not sufficient to label all the data value groups. The annotators are applied one after the other according to their capability of assigning the most appropriate label. Experiments show that this technique has improved the precision and recall values compared to the existing annotation techniques.

Many medical errors are due to the fact that people in charge of patient or elder's medication have to deal with sorting huge amounts of pills each day. This paper consists on the conception, design and creation of a pillbox prototype intended to solve this deficiency in the medical area as it has the ability of sorting out the pills by itself as well as many other advanced features, with this device being intended to be used by hospitals or retirement homes. This medication pill box is focused on patients who frequently take medications or vitamin supplements, or attendants who deal with the more seasoned or patients. Our smart pill box is programmable that enables medical caretakers or clients to determine the pill amount and timing to take pills, and the service times for every day. Our shrewd pills box contains nine separate sub-boxes. In this manner, medical caretakers or clients can set data for nine distinct pills. At the point when the pill time has been set, the pillbox will remind clients or patients to take pills utilizing sound and light. The warning of pills should be taken will be shown by an android application which is held by the patient. Contrasted and the conventional pill box that requires clients or attendants to stack the crate each day or consistently. Our shrewd pill box would essentially discharge medical attendants or clients' weight on much of the time preloading pills for patients or clients and overlook the measurements which must be taken.

Many developing countries are now experiencing revolution in e-government to deliver fluent and simple services for their citizens. However, governmental sectors face many challenges in using its e-governments’ services and its infrastructure, improving current services or developing new services; as data and applications increasingly inflating, IT budget costs, software licensing and support and difficulties in migration, integration and management for software and hardware. These challenges may lead to failure of e-governments’ projects. Therefore, there is a need for a solution to overcome these challenges. Cloud Computing plays a vital role to solve these problems. This paper demonstrates e-government's obstacles and cloud computing features. Also, it proposes an abstract hybrid model for adapting cloud computing in e-government that overcomes the e-government's challenges. This hybrid proposed model identifies three different patterns of cloud computing which are Local Governmental Cloud “LGC”, Regional Governmental Cloud “RGC” and Wide Governmental Cloud “WGC”. The proposed model determines how the entity connects to each of three clouds and what the relation between them is. In addition, readiness assessment of the services need to migrate into cloud. Finally, a set of recommended cloud aspects and their values for each of three clouds are suggested that ensure implementation of the sorted services.

Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. Most Cloud providers support services under constraints of Service Level Agreement (SLA) definitions. The SLAs are composed of different quality of service (QoS) rules promised by the provider. A cloud environment can be classified into two types: computing clouds and data clouds. In computing cloud, task scheduling plays a vital role in maintaining the quality of service and SLA. Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing. This paper explores the task scheduling algorithm using a hybrid approach, which combines desirable characteristics of two of the most widely used biologically-inspired heuristic algorithms, the genetic algorithms (GAs) and the bacterial foraging (BF) algorithms in the computing cloud. The main contributions of this article are twofold. First, the scheduling algorithm minimizes the makespan and second; it reduces the energy consumption, both economic and ecological perspectives. Experimental results show that the performance of the proposed algorithm outperforms than those of other algorithms regarding convergence, stability, and solution diversity.

The process of capturing, transfer and sharing of information in the form of digital images have become easier due to the use of advanced technologies. Retrieval of desired images from these huge collections of image databases is one of the popular research areas and has its applications in various fields. An image set consists of images containing objects of different colours, shapes, orientations and sizes. The surface texture of the object in an image may also vary from another object in a different image. These factors make the process of image retrieval a difficult one. In this paper, Self-Organising Map is applied on local texture features for organising the brain magnetic resonance images according to their similarity. The correlation among the pixels is considered for the retrieval of most similar images to the input query image. The experimental results obtained prove the effectiveness of the proposed method for medical images.

With the increasing demand, the web service has been the prominent technology for providing good solutions to the interoperability of different kind of systems. Web service supports mainly interoperability properties as it is the major usage of this promising technology. Although several technologies had been evolved before web service technology and this has more advantage of other technologies. This paper has concentrated mainly on the Multifaceted Matchmaking framework for Web Services Discovery using Quality of Services parameters. Traditionally web services have been discovered only with the functional properties like input, output, precondition and effect. Nowadays there is an increase in number of service providers leads to increase in the web services with same functionality. So user need to discover the best services so Quality of Service factors has been evolved. The traditional discovery supports only few quality parameters and so the discovery is easy in retrieval of services. As the parameter increases the matchmaking will be complex during service discovery. So in this proposed work, we have identified 21 QoS parameters which are suitable for service discovery. The information retrieval techniques are used to evaluate the results and results show that the proposed framework is better.

Quality-of-Services (QoS) is one of the most important requirements of cloud users. So, cloud providers continuously try to enhance cloud management tools to guarantee the required QoS and provide users the services with high quality. One of the most important management tools which play a vital role in enhancing QoS is scheduling. Scheduling is the process of assigning users’ tasks into available Virtual Machines (VMs). This paper presents a new task scheduling approach, called Online Potential Finish Time (OPFT), to enhance the cloud data-center broker, which is responsible for the scheduling process, and solve the QoS issue. The main idea of the new approach is inspired from the idea of passing vehicles through the highways. Whenever the width of the road increases, the number of passing vehicles increases. We apply this idea to assign different users’ tasks into the available VMs. The number of tasks that are allocated to a VM is in proportion to the processing power of this VM. Whenever the VM capacity increases, the number of tasks that are assigned into this VM increases. The proposed OPFT approach is evaluated using the CloudSim simulator considering real tasks and real cost model. The experimental results indicate that the proposed OPFT algorithm is more efficient than the FCFS, RR, Min-Min, and MCT algorithms in terms of schedule length, cost, balance degree, response time and resource utilization.

Location Based Services is the popular and geo sensitive service implicated over the smart phone by internet. Nowadays these system find its own enhancement, as they are using device‘s real time geographical information to provide information and entertainment. It allows the user to get the response to the query based on their current location there by location becomes the most basic context for the user. For example these services are used to check in restaurants, coffee shops to get the business reward from the nearest shop or to track the location of a person. The user of the smart phone has certain limitations to overcome when they are using these services. The data set size to be handled and the size of the memory are those main issues which influence the speed of query processing of these devices. In this paper, a new distributed index structure based on vantage point transformation technique is introduced to improve both the query space as well as the data space of the resulting query. The onus of this technique is to produce the better improvement in execution of parallel analytical algorithm in query processing. This methodology not only improves the efficiency of the system but also reduces the number of false positives and false negatives.