A Survey: Soft Computing in Intelligent Information Retrieval Systems

Ahmad, Mohd Wazih; Ansari, M. A.

doi:10.1109/iccsa.2012.15

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…Traditional search engines collect metadata from every web document, and store the metadata into an inverted index, this kind of engines mostly support static keyword searches to find such documents (Pokorný, 2004). Information retrieval algorithms (Ahmad and Ansari, 2012) are then used to determine the best answer to user queries. These search engines are not capable of discovering dynamic IoT devices and services as search objects in both types of search engines are very different from each other (Guinard et al, 2010).…”

Section: Massive Scalingmentioning

confidence: 99%

Internet of things: a survey of challenges and issues

Sarhan¹

2018

IJITCA

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Internet of things (IoT) is the promising and future internet. The IoT is a network of connected sensors, actuators, and everyday objects that are used in various domains, such as healthcare, airports, and military. As it connects everything around us to the internet, the IoT poses a number of severe challenges and issues as compared to the conventional internet. Currently, there are massive studies on the IoT, these studies mostly cover IoT vision, enabling technologies, applications, or services. So far, a limited number of surveys point out comprehensively the challenges and issues of the IoT which considered unique to this future internet and which must be faced and tackled by different research communities. In this paper, well-known IoT challenges and issues (e.g., reliable cooperation, standards, protocols, operational, data, and software) have been surveyed alongside many directions. Furthermore, the paper also raises awareness of work being achieved across a number of research communities to help whoever decided to approach this hot discipline in order to contribute to its development.

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Section: Massive Scalingmentioning

confidence: 99%

Internet of things: a survey of challenges and issues

Sarhan¹

2018

IJITCA

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“…It is well known that the more precise a solution is, the more computation and communication cost the solution needs. Unlike conventional computing, soft computing is tolerant of imprecision, uncertainty, partial truth and approximation to achieve tractability, robustness, and low solution cost (Ahmad and Ansari 2012;Metre et al 2012;Wahab et al 2009;Yu and Kaynak 2009). Therefore, based on soft computing techniques (Kim and Bien 2008;Mitra et al 2002;Murakami and Honda 2008;Yager et al 2014), we develop our dummy generation algorithms to provide preferred location privacy protection ability for mobile cloud user in the long-term level with a low cost.…”

Section: Introductionmentioning

confidence: 97%

Long-term location privacy protection for location-based services in mobile cloud computing

Tang

You

et al. 2015

Soft Comput

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The popularity of mobile devices, especially intelligent mobile phones, significantly prompt various location-based services (LBSs) in cloud systems. These services not only greatly facilitate people's daily lives, but also cause serious threats that users' location information may be misused or leaked by service providers. The dummy-based privacy protection techniques have significant advantages over others because they neither rely on trusted servers nor need adequate number of trustworthy peers. Existing dummy-based location privacy protection schemes, however, cannot yet provide long-term privacy protection. In this paper, we propose four principles for the dummy-based long-term location privacy protection (LT-LPP). Based on the principles, we propose a set of long-term consistent dummy generation algorithms for the LT-LPP. Our approach is built on soft computing techniques and can balance the preferred privacy protection and computing cost. Comprehensive experimental results demonstrate that our approach is effective to both long-term privacy protection and fake path generation for LBSs in mobile clouds.

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“…Extracting knowledge from big databases and document databases has long been a challenge because of the large number of documents that make it hard to select the most relevant data. For that reason, a lot of retrieval algorithms have been developed (Ahmad and Ansari 2012;Boden et al 2012;Karol and Mangat 2013;Koval and Návrat 2012;Wang et al 2013) applying distinct sophisticated techniques: fuzzy, artificial neural network (ANN), clustering, machine learning, and hybrids.…”

Section: Introductionmentioning

confidence: 99%

Intelligent information extraction from scholarly document databases

Fernandez¹

2020

JISIB

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Extracting knowledge from big document databases has long been a challenge.Most researchers do a literature review and manage their document databases with tools thatjust provide a bibliography and when retrieving information (a list of concepts and ideas), thereis a severe lack of functionality. Researchers do need to extract specific information from theirscholarly document databases depending on their predefined breakdown structure. Thosedatabases usually contain a few hundred documents, information requirements are distinct ineach research project, and technique algorithms are not always the answer. As most retrievingand information extraction algorithms require manual training, supervision, and tuning, itcould be shorter and more efficient to do it by hand and dedicate time and effort to perform aneffective semantic search list definition that is the key to obtain the desired results. A robustrelative importance index definition is the final step to obtain a ranked importance concept listthat will be helpful both to measure trends and to find a quick path to the most appropriatepaper in each case.

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A Survey: Soft Computing in Intelligent Information Retrieval Systems

Cited by 12 publications

References 19 publications

Internet of things: a survey of challenges and issues

Internet of things: a survey of challenges and issues

Long-term location privacy protection for location-based services in mobile cloud computing

Intelligent information extraction from scholarly document databases

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