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Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems

Tao H, Rahman MA, Jing W, Li Y, Li J, Al-Saffar A, et al.

Work, 2021;68(3):903-912.
PMID: 33720867 DOI: 10.3233/WOR-203424

BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users.
OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.
RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.
CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

Matched MeSH terms: Information Storage and Retrieval
Fulltext Towards dynamic remote data auditing in computational clouds

Sookhak M, Akhunzada A, Gani A, Khurram Khan M, Anuar NB

ScientificWorldJournal, 2014;2014:269357.
PMID: 25121114 DOI: 10.1155/2014/269357

Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these techniques, however, are only applicable for static archive data and are not subject to audit the dynamically updated outsourced data. We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete. Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost. The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.

Matched MeSH terms: Information Storage and Retrieval/methods*
Fulltext Stochastic optimized relevance feedback particle swarm optimization for content based image retrieval

Imran M, Hashim R, Noor Elaiza AK, Irtaza A

ScientificWorldJournal, 2014;2014:752090.
PMID: 25121136 DOI: 10.1155/2014/752090

One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

Matched MeSH terms: Information Storage and Retrieval/methods*
Fulltext A study on strategic provisioning of cloud computing services

Whaiduzzaman M, Haque MN, Rejaul Karim Chowdhury M, Gani A

ScientificWorldJournal, 2014;2014:894362.
PMID: 25032243 DOI: 10.1155/2014/894362

Cloud computing is currently emerging as an ever-changing, growing paradigm that models "everything-as-a-service." Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning. Hence, continuous service provisioning that satisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms. We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified.

Matched MeSH terms: Information Storage and Retrieval/methods*; Information Storage and Retrieval/standards
Fulltext Big data: survey, technologies, opportunities, and challenges

Khan N, Yaqoob I, Hashem IA, Inayat Z, Ali WK, Alam M, et al.

ScientificWorldJournal, 2014;2014:712826.
PMID: 25136682 DOI: 10.1155/2014/712826

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

Matched MeSH terms: Information Storage and Retrieval
Fulltext A comprehensive review on adaptability of network forensics frameworks for mobile cloud computing

Khan S, Shiraz M, Wahab AW, Gani A, Han Q, Rahman ZB

ScientificWorldJournal, 2014;2014:547062.
PMID: 25097880 DOI: 10.1155/2014/547062

Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC.

Matched MeSH terms: Information Storage and Retrieval/methods*
Fulltext Creation of reliable relevance judgments in information retrieval systems evaluation experimentation through crowdsourcing: a review

Samimi P, Ravana SD

ScientificWorldJournal, 2014;2014:135641.
PMID: 24977172 DOI: 10.1155/2014/135641

Test collection is used to evaluate the information retrieval systems in laboratory-based evaluation experimentation. In a classic setting, generating relevance judgments involves human assessors and is a costly and time consuming task. Researchers and practitioners are still being challenged in performing reliable and low-cost evaluation of retrieval systems. Crowdsourcing as a novel method of data acquisition is broadly used in many research fields. It has been proven that crowdsourcing is an inexpensive and quick solution as well as a reliable alternative for creating relevance judgments. One of the crowdsourcing applications in IR is to judge relevancy of query document pair. In order to have a successful crowdsourcing experiment, the relevance judgment tasks should be designed precisely to emphasize quality control. This paper is intended to explore different factors that have an influence on the accuracy of relevance judgments accomplished by workers and how to intensify the reliability of judgments in crowdsourcing experiment.

Matched MeSH terms: Information Storage and Retrieval/methods*
Fulltext Cloud service selection using multicriteria decision analysis

Whaiduzzaman M, Gani A, Anuar NB, Shiraz M, Haque MN, Haque IT

ScientificWorldJournal, 2014;2014:459375.
PMID: 24696645 DOI: 10.1155/2014/459375

Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.

Matched MeSH terms: Information Storage and Retrieval/methods*
Fulltext Trusted computing strengthens cloud authentication

Ghazizadeh E, Zamani M, Ab Manan JL, Alizadeh M

ScientificWorldJournal, 2014;2014:260187.
PMID: 24701149 DOI: 10.1155/2014/260187

Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model.

Matched MeSH terms: Information Storage and Retrieval/standards; Information Storage and Retrieval/trends
Fulltext A TOTP-based enhanced route optimization procedure for mobile IPv6 to reduce handover delay and signalling overhead

Shah PA, Hasbullah HB, Lawal IA, Aminu Mu'azu A, Tang Jung L

ScientificWorldJournal, 2014;2014:506028.
PMID: 24688398 DOI: 10.1155/2014/506028

Due to the proliferation of handheld mobile devices, multimedia applications like Voice over IP (VoIP), video conferencing, network music, and online gaming are gaining popularity in recent years. These applications are well known to be delay sensitive and resource demanding. The mobility of mobile devices, running these applications, across different networks causes delay and service disruption. Mobile IPv6 was proposed to provide mobility support to IPv6-based mobile nodes for continuous communication when they roam across different networks. However, the Route Optimization procedure in Mobile IPv6 involves the verification of mobile node's reachability at the home address and at the care-of address (home test and care-of test) that results in higher handover delays and signalling overhead. This paper presents an enhanced procedure, time-based one-time password Route Optimization (TOTP-RO), for Mobile IPv6 Route Optimization that uses the concepts of shared secret Token, time based one-time password (TOTP) along with verification of the mobile node via direct communication and maintaining the status of correspondent node's compatibility. The TOTP-RO was implemented in network simulator (NS-2) and an analytical analysis was also made. Analysis showed that TOTP-RO has lower handover delays, packet loss, and signalling overhead with an increased level of security as compared to the standard Mobile IPv6's Return-Routability-based Route Optimization (RR-RO).

Matched MeSH terms: Information Storage and Retrieval/methods*
Fulltext Effect of temporal relationships in associative rule mining for web log data

Khairudin NM, Mustapha A, Ahmad MH

ScientificWorldJournal, 2014;2014:813983.
PMID: 24587757 DOI: 10.1155/2014/813983

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.

Matched MeSH terms: Information Storage and Retrieval/methods*
Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index

Wang Z, Zhang F, Zhang X, Chan NW, Kung HT, Ariken M, et al.

Sci Total Environ, 2021 Feb 12;775:145807.
PMID: 33618298 DOI: 10.1016/j.scitotenv.2021.145807

Soil salinization is an extremely serious land degradation problem in arid and semi-arid regions that hinders the sustainable development of agriculture and food security. Information and research on soil salinity using remote sensing (RS) technology provide a quick and accurate assessment and solutions to address this problem. This study aims to compare the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction and exploration of the potential application of derivatives to RS prediction of salinized soils. It explores the ability of derivatives to be used in the Landsat-8 OLI and Sentinel-2A MSI multispectral data, and it was used as a data source as well as to address the adaptability of salinity prediction on a regional scale. The two-dimensional (2D) and three-dimensional (3D) optimal spectral indices are used to screen the bands that are most sensitive to soil salinity (0-10 cm), and RS data and topographic factors are combined with machine learning to construct a comprehensive soil salinity estimation model based on gray correlation analysis. The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. This study compared the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction in finding the potential application of derivatives to RS prediction of salinized soils, with the results providing some theoretical basis and technical guidance for salinized soil prediction and environmental management planning.

Matched MeSH terms: Information Storage and Retrieval
Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.

Sci Total Environ, 2019 May 01;663:1-15.
PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.

Matched MeSH terms: Information Storage and Retrieval
Fulltext A study of clinical questions in primary care

Fozi K, Teng CL, Krishnan R, Shajahan Y

Med J Malaysia, 2000 Dec;55(4):486-92.
PMID: 11221162

This is a prospective study of clinical questions generated in primary care consultations and a comparison of two approaches to answering those clinical questions. Twenty-one doctors in a university-based primary care clinic submitted 78 clinical questions arising from patient consultations during 24 clinic days (0.01 question per patient encounter). These doctors subsequently found answers to 40% of their questions but were satisfied with only 67% of these answers. The investigators were able to provide answers for 95% of the questions asked and the doctors rated these answers as satisfactory in 86% of instances. Answers obtained by investigators had significantly higher satisfaction score than those obtained by doctors' search (p = 0.002). The two main findings of this study are (1) almost all questions arising in clinic setting could be answered by intensive search; (2) answers found by intensive searches were judged to be more satisfactory than those found routinely by doctors. Provision of an information retrieval service in addition to training in the searching and appraisal of medical literature are possible solutions to the information needs of busy clinicians.

Study site: Primary Care Clinic,
University Hospital Kuala Lumpur i

Matched MeSH terms: Information Storage and Retrieval/standards*
Guidelines on retention of pathology records and materials, Part I (version 1/2005)

College of Pathologists, Academy of Medicine of Malaysia, Ministry of Health Malaysia

Malays J Pathol, 2005 Jun;27(1):51-6.
PMID: 16676694
Matched MeSH terms: Information Storage and Retrieval/standards*
Leveraging intelligent agents for knowledge discovery from heterogeneous healthcare data repositories

Zaidi SZ, Abidi SS, Manickam S

Stud Health Technol Inform, 2002;90:335-40.
PMID: 15460713

This paper presents a case for an intelligent agent based framework for knowledge discovery in a distributed healthcare environment comprising multiple heterogeneous healthcare data repositories. Data-mediated knowledge discovery, especially from multiple heterogeneous data resources, is a tedious process and imposes significant operational constraints on end-users. We demonstrate that autonomous, reactive and proactive intelligent agents provide an opportunity to generate end-user oriented, packaged, value-added decision-support/strategic planning services for healthcare professionals, manages and policy makers, without the need for a priori technical knowledge. Since effective healthcare is grounded in good communication, experience sharing, continuous learning and proactive actions, we use intelligent agents to implement an Agent based Data Mining Infostructure that provides a suite of healthcare-oriented decision-support/strategic planning services.

Matched MeSH terms: Information Storage and Retrieval/methods*
Applying data mining in healthcare: an info-structure for delivering 'data-driven' strategic services

Abidi SS

Stud Health Technol Inform, 1999;68:453-6.
PMID: 10724926

Presently, there is a growing demand from the healthcare community to leverage upon and transform the vast quantities of healthcare data into value-added, 'decision-quality' knowledge, vis-à-vis, strategic knowledge services oriented towards healthcare management and planning. To meet this end, we present a Strategic Knowledge Services Info-structure that leverages on existing healthcare knowledge/data bases to derive decision-quality knowledge-knowledge that is extracted from healthcare data through services akin to knowledge discovery in databases and data mining.

Matched MeSH terms: Information Storage and Retrieval/statistics & numerical data*
Polarity dependent photoisomerization of ether substituted azodyes: Synthesis and photoswitching behavior

Gan SM, Pearl ZF, Yuvaraj AR, Lutfor MR, Gurumurthy H

Spectrochim Acta A Mol Biomol Spectrosc, 2015 Oct 5;149:875-80.
PMID: 26004096 DOI: 10.1016/j.saa.2015.05.027

Two new ether substituted azodyes were synthesized and characterized by different spectral analysis such as (1)H NMR, (13)C NMR, FTIR and UV/Vis. Synthesized compounds were used to study the photoisomerization phenomenon by using UV-Vis spectro-photometer. Interesting polarity dependent effect is observed for the first time on these materials. Trans-cis (E-Z) and cis-trans (Z-E) conversion occurred within 41 s and 445 min, respectively for both the compounds in solutions. Polarizing optical microscopy studies revealed that there is no liquid crystal phase for both the compounds. The dramatic variation in the optical property is speculated to be the polarity of the chemical species. These derivatives are useful to fabricate optical data storage devices.

Matched MeSH terms: Information Storage and Retrieval
Fulltext Getting to know journal bibliographic databases

Ng KH, Peh WC

Singapore Med J, 2010 Oct;51(10):757-60; quiz 761.
PMID: 21103809

A bibliographic database is an organised digital collection of references to published literature. A bibliographic database may be general in scope or may cover a specific academic discipline. There are many types of medical and general bibliographic databases. They cover biomedical and scientific literature, morbidity and mortality statistics, therapeutic regimens, medical records, images and reviews of evidence-based medicine. Getting to know these databases will help researchers and authors to enhance their writing and publishing endeavours.

Matched MeSH terms: Information Storage and Retrieval/methods*
Fulltext Effectiveness of the use of internet search by third year medical students to establish a clinical diagnosis

Nalliah S, Chan SL, Ong CL, Suthan TH, Tan KC, She VN, et al.

Singapore Med J, 2010 Apr;51(4):332-8.
PMID: 20505913

Internet search has been the main source for information and data mining in medical research. Its use by medical students has immensely contributed to learning activities. The main aim of the study was to determine the effectiveness of internet use by medical students during their initial years of clinical instruction in order to establish a diagnosis after being provided with the history and physical findings of a clinical problem.

Matched MeSH terms: Information Storage and Retrieval

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