Recommendation System for Criminal Behavioral Analysis on Social Network using Genetic Weighted K-Means Clustering

Soundarya, V.

doi:10.17706/jcp.12.3.212-220

Cited by 15 publications

(2 citation statements)

References 17 publications

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“…Prior studies found that the influence of several factors, such as social, demographic, and economic, significantly impacts crime occurrence (Ranson, 2014;Soundarya et al, 2017;Stansfield et al, 2017). It has been observed that multivariate analysis in crime forecasting is beneficial in improving forecasting performance capabilities.…”

Section: Literature Study On Feature Selection In Crime Forecastingmentioning

confidence: 99%

Hybrid Neighbourhood Component Analysis with Gradient Tree Boosting for Feature Selection in Forecasting Crime Rate

Khairuddin

Alwee

Haron

2023

Journal of Information and Communication Technology

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Crime forecasting is beneficial as it provides valuable information to the government and authorities in planning an efficient crimeprevention measure. Most criminology studies found that influence from several factors, such as social, demographic, and economicfactors, significantly affects crime occurrence. Therefore, most criminology experts and researchers study and observe the effectof factors on criminal activities as it provides relevant insight into possible future crime trends. Based on the literature review, theapplications of proper analysis in identifying significant factors that influence crime are scarce and limited. Therefore, this study proposed a hybrid model that integrates Neighbourhood Component Analysis (NCA) with Gradient Tree Boosting (GTB) in modelling the United States (US) crime rate data. NCA is a feature selection technique used in this study to identify the significant factors influencing crime rate. Once the significant factors were identified, an artificial intelligence technique, i.e., GTB, was implemented in modelling the crime data, where the crime rate value was predicted. The performance of the proposed model was compared with other existing models using quantitative measurement error analysis. Based on the result, the proposed NCA-GTB model outperformed other crime models in predicting the crime rate. As proven by the experimental result, the proposed model produced the smallest quantitative measurement error in the case study.

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Section: Literature Study On Feature Selection In Crime Forecastingmentioning

confidence: 99%

Hybrid Neighbourhood Component Analysis with Gradient Tree Boosting for Feature Selection in Forecasting Crime Rate

Khairuddin

Alwee

Haron

2023

Journal of Information and Communication Technology

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“…Anjomshoa et al [21] proposed a scheme which is used for identification of social behavior using machine learning. In this scheme an intelligent system makes which enable to identify user continuously on social network.…”

Section: Related Workmentioning

confidence: 99%

The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review

Desai¹,

Jhaveri²

2018

IJCA

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Rapid developments in hardware and software based connected devices with communication technologies introduce Internet-of-things (IoT). In recent year, IoT gained enormous popularity and used vastly in a variety of applications. IoT represents the new forms of communication such as between human and things, and between two things. At present era IoT concepts are highly influence by creating a new dimension in the internet world. Intelligent processing and analysis of big data is the key to developing smart IoT applications. Such applications are logistic, transportation, agriculture, healthcare, and environment. Increasing the use of internet and external factor of environment are caused dynamic change in IoT system. The use of smart environments in the delivery of a pervasive care is the research topic that has witnessed increasing interest in recent years. These smart environments aim to deliver pervasive care through ubiquitous sensing by monitoring the occupant's activities. In order to provide smarter environment, their need to be implement IoT with machine learning. In recent year, machine learning technique have been used widely because of its technologies such that identification, extraction, classification, regression and forecasting. Machine learning exploring historical data from camera and sensors and perform techniques which improve the lifespan of network. In this paper, we build survey on existing research work carried out for various applications of machine learning to IoT. We summarize techniques and tools for IoT and also their benefits and limitations. Using a summarized data we also look for different future challenges. The aim of this paper is to survey different IoT technologies to assist the people to live in a smart environment.

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Active Learning and Deep Learning for the Cold-Start Problem in Recommendation System: A Comparative Study

Alabdulrahman

Viktor

Paquet

2020

Communications in Computer and Information Science

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Recommendation System for Criminal Behavioral Analysis on Social Network using Genetic Weighted K-Means Clustering

Cited by 15 publications

References 17 publications

Hybrid Neighbourhood Component Analysis with Gradient Tree Boosting for Feature Selection in Forecasting Crime Rate

Hybrid Neighbourhood Component Analysis with Gradient Tree Boosting for Feature Selection in Forecasting Crime Rate

The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review

Active Learning and Deep Learning for the Cold-Start Problem in Recommendation System: A Comparative Study

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