Swarm intelligence in anomaly detection systems: an overview

Mishra, Sanju; Sagban, Rafid; Al-Sultan, Ali Yakoob; Gandhi, Niketa

doi:10.1080/1206212x.2018.1521895

Cited by 37 publications

(20 citation statements)

References 31 publications

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“…Also the accuracy could be further improved by selecting other features like texture and color. Finally, other methods such as data mining and artificial intelligence methods [20] could also be tested to see if they are effective in detecting and classifying burn wounds in future studies.…”

Section: Resultsmentioning

confidence: 99%

Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

Yadav

Sharma

Singh

et al. 2019

IEEE J. Transl. Eng. Health Med.

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Burn is one of the serious public health problems. Usually, burn diagnoses are based on expert medical and clinical experience and it is necessary to have a medical or clinical expert to conduct an examination in restorative clinics or at emergency rooms in hospitals. But sometimes a patient may have a burn where there is no specialized facility available, and in such a case a computerized automatic burn assessment tool may aid diagnosis. Burn area, depth, and location are the critical factors in determining the severity of burns. In this paper, a classification model to diagnose burns is presented using automated machine learning. The objective of the research is to develop the feature extraction model to classify the burn. The proposed method based on support vector machine (SVM) is evaluated on a standard data set of burns—BIP_US database. Training is performed by classifying images into two classes, i.e., those that need grafts and those that are non-graft. The 74 images of test data set are tested with the proposed SVM based method and according to the ground truth, the accuracy of 82.43% was achieved for the SVM based model, which was higher than the 79.73% achieved in past work using the multidimensional scaling analysis (MDS) approach.

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Section: Resultsmentioning

confidence: 99%

Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

Yadav

Sharma

Singh

et al. 2019

IEEE J. Transl. Eng. Health Med.

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“…As an example, the first paper of this group is a survey in which summarizes the security issues esp. anomaly detecting and possible presented countermeasures and their pros and cons [5]. They describe the different swarm-based anomaly detection methods and provide new architecture.…”

Section: Performance Improvements/of Applicationsmentioning

confidence: 99%

Internet of everything, networks, applications, and computing systems (IoENACS)

Shojafar

Sookhak

2019

International Journal of Computers and Applications

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“…Therefore, it is important to reduce the amount of resource overhead in order to minimize energy consumption in resource-constrained mobile devices. Finally, how to design an optimized LPPM that satisfies the service quality and resource overhead according to user privacy requirements and given attack model is significantly important research direction for location privacy [77].…”

Section: Privacy Protection Challenges and Comparison Analysismentioning

confidence: 99%

A Survey of System Architectures, Privacy Preservation, and Main Research Challenges on Location-Based Services

2019

KSII TIIS

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Location-based services (LBSs) have become popular in recent years due to the ever-increasing usage of smart mobile devices and mobile applications through networks. Although LBS application provides great benefits to mobile users, it also raises a sever privacy concern of users due to the untrusted service providers. In the lack of privacy enhancing mechanisms, most applications of the LBS may discourage the user's acceptance of location services in general, and endanger the user's privacy in particular. Therefore, it is a great interest to discuss on the recent privacy-preserving mechanisms in LBSs. Many existing location-privacy protection-mechanisms (LPPMs) make great efforts to increase the attacker's uncertainty on the user's actual whereabouts by generating a multiple of fake-locations together with user's actual positions. In this survey, we present a study and analysis of existing LPPMs and the state-of-art privacy measures in service quality aware LBS applications. We first study the general architecture of privacy qualification system for LBSs by surveying the existing framework and outlining its main feature components. We then give an overview of the basic privacy requirements to be considered in the design and evaluation of LPPMs. Furthermore, we discuss the classification and countermeasure solutions of existing LPPMs for mitigating the current LBS privacy protection challenges. These classifications include anonymization, obfuscation, and an encryption-based technique, as well as the combination of them is called a hybrid mechanism. Finally, we discuss several open issues and research challenges based on the latest progresses for on-going LBS and location privacy research. IntroductionRecent advances in mobile computing makes location based service (LBS) increasingly popular, which attract millions of individuals. LBS refers to a location information and convenient services provided to mobile users based on the geographic position and other information of users' mobile devices to obtain their real location data [1]- [2]. In this context, user oriented LBS applications are developed to obtain location related information relevant to their current position and surroundings. Common examples include direct location sharing with friends (e.g., uploading real-time location data and tagging services) and sharing with business sectors (e.g., search for near-by services, location check-in and search near-by friends), which help the user to share and determine their current position with friends in the communication networks [3]- [4]. Other typical examples of LBS applications include map applications (e.g., Google Maps), point of interest retrieval (e.g., Around-Me), coupons or discount offers (e.g., Group-on), GPS navigation (e.g., Tom-tom) and location-aware social networks (e.g., Face-book, Weibo, Foursquare, Wechat) [5]. A more large-scale LBS application could ask users around a region or even country to disclose their exact locations for security and safety purpose (e.g., in military, medical care, e...

show abstract

Swarm intelligence in anomaly detection systems: an overview

Cited by 37 publications

References 31 publications

Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

Internet of everything, networks, applications, and computing systems (IoENACS)

A Survey of System Architectures, Privacy Preservation, and Main Research Challenges on Location-Based Services

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