Classification of E-commerce Products Using RepTree and K-means Hybrid Approach

Midha, Neha; Singh, Vikram

doi:10.1007/978-981-10-6620-7_26

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…We chose J48 [28] and Logistic Model Tree (LMT) [29] from the tree-based family, Decision Table [30], JRip [31], and OneR [32] from rule-based family, IBK [33] and KStar [34] from lazy learner family, Bagging [35] and LogitBoost [36] from metaheuristic family, and Bayes Net [37] from Bayesian family. Rep Tree [38] and Regression are used as base classifiers for Bagging and LogitBoost algorithms.…”

Section: Classification Algorithmsmentioning

confidence: 99%

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Khan

Ahmad

Alsayed

et al. 2020

Scientific Programming

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With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.

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Section: Classification Algorithmsmentioning

confidence: 99%

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Khan

Ahmad

Alsayed

et al. 2020

Scientific Programming

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“…We have considered four well‐known data mining algorithms in this article. These four algorithms include MLP, naïve Bayes, SMO, and REPtree 54 . We used different random sizes of data.…”

Section: Case Studymentioning

confidence: 99%

Leveraging mist and fog for big data analytics in IoT environment

El‐Hasnony

Mostafa

Elhoseny

et al. 2020

Trans Emerging Tel Tech

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Internet of Things (IoT) emerged as one of the leading technological advancements of our days. IoT generates enormous quantities of valuable data that need on time processing, resulting in reliable, and accurate decisions based on the Internet of Things vision. The quality of the generated data is inadequate, incomplete, uncertain, and produced from multiple sources. Although cloud servers can analyze and store enormous data, they need a lot of time to send full‐size data for storage and analysis as well as the high overhead they have that not satisfactory in many applications. This article provides a systematic way to review the IoT environment according to big data analytics together with limitations and challenges. Moreover, a cloud‐fog‐mist combination for handling IoT data concerning centralized and distributed data mining is explained. A proposed hybrid real‐time remote patient monitoring framework introduced that consists of the integration among the mist, fog, and cloud for healthcare treatment, which remote‐monitors patients continuously. In addition, Reduced‐Error Pruning tree (REPtree), MultiLayer Perceptron, naïve Bayes, and Sequential Minimal Optimization algorithms have applied to “Gas sensors for home activity monitoring” dataset to demonstrate the feasibility of traditional data mining algorithms to IoT data. The results showed that the REPtree algorithm achieved better accuracy against others with accuracy ranged between 90.66% and 93.6% according to the size of the data used in the study. Still, for the time metric, naive Bayes outperformed them with the lowest time between 1 and 18 seconds for building the model.

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“…In the pre-pruning process, if the information gain due to division is not recognized, the expansion of the tree will be terminated [70]. XGBoost Extreme gradient boost (XGBoost) is an optimized distributed gradient boosting algorithm with high efficiency and flexibility.…”

Section: Liblinearmentioning

confidence: 99%

Exploiting EEG Signals and Audiovisual Feature Fusion for Video Emotion Recognition

et al. 2019

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External stimulation, mood swing, and physiological arousal are closely related and induced by each other. The exploration of internal relations between these three aspects is interesting and significant. Currently, video is the most popular multimedia stimuli that can express rich emotional semantics by its visual and auditory features. Apart from the video features, human electroencephalography (EEG) features can provide useful information for video emotion recognition, as they are the direct and instant authentic feedback on human perception with individuality. In this paper, we collected a total of 39 participants' EEG data induced by watching emotional video clips and built a fusion dataset of EEG and video features. Subsequently, the machine-learning algorithms, including Liblinear, REPTree, XGBoost, MultilayerPerceptron, RandomTree, and RBFNetwork were applied to obtain the optimal model for video emotion recognition based on a multi-modal dataset. We discovered that using the data fusion of all-band EEG power spectrum density features and video audio-visual features can achieve the best recognition results. The video emotion classification accuracy achieves 96.79% for valence (Positive/Negative) and 97.79% for arousal (High/Low).The study shows that this method can be a potential method of video emotion indexing for video information retrieval.

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Classification of E-commerce Products Using RepTree and K-means Hybrid Approach

Cited by 8 publications

References 6 publications

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Leveraging mist and fog for big data analytics in IoT environment

Exploiting EEG Signals and Audiovisual Feature Fusion for Video Emotion Recognition

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