A review of supervised and unsupervised machine learning techniques for suspicious behavior recognition in intelligent surveillance system

Verma, Kamal Kant; Singh, Brij Mohan; Dixit, Amit Rai

doi:10.1007/s41870-019-00364-0

Cited by 50 publications

(18 citation statements)

References 60 publications

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“…But the FPR was not sufficient. A supervised and unsupervised ML methodology was introduced in [24] for SB detection. However, the accuracy of suspicious detection was not enhanced.…”

Section: Related Workmentioning

confidence: 99%

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

Gnanakumari¹,

Vijayalakshmi²

2023

Intelligent Automation &Amp; Soft Computing

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In social data analytics, Virtual Community (VC) detection is a primary challenge in discovering user relationships and enhancing social recommendations. VC formation is used for personal interaction between communities. But the usual methods didn't find the Suspicious Behaviour (SB) needed to make a VC. The Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking (GJSBS-RDNNCR) Model addresses these issues. The GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks (SN). In the GJSBS-RDNNCR model, the SN is given as an input at the input layer. After that, the User's Behaviors (UB) are extracted in the first Hidden Layer (HL), and the Generalized Jaccard Similarity coefficient calculates the similarity value at the second HL based on the SB. In the third HL, the similarity values are examined, and SB tendency is classified using the Activation Function (AF) in the Output Layer (OL). Finally, the ranking process is performed with classified users in SN and their SB. Results analysis is performed with metrics such as Classification Accuracy (CA), Time Complexity (TC), and False Positive Rate (FPR). The experimental setup considers 250 tweet users from the dataset to identify the SBs of users.

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“…But the FPR was not sufficient. A supervised and unsupervised ML methodology was introduced in [24] for SB detection. However, the accuracy of suspicious detection was not enhanced.…”

Section: Related Workmentioning

confidence: 99%

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

Gnanakumari¹,

Vijayalakshmi²

2023

Intelligent Automation &Amp; Soft Computing

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“…Clustering: it is a machine learning scheme used to split power consumption data into various clusters and hence helps in classifying them into normal or abnormal in unlabelled datasets (even with many dimensions). This anomaly detection strategy has attracted a lot of interest in different research topics for its simplicity, such as intrusion detection in networks [23], Internet of things (IoT) [24], sensor networks [25], suspicious behavior detection in video surveillance [26], anomalous transaction detection in banking systems [27] and suspicious account detection in online social networks [28]. In addition clustering has the capability for learning and detecting anomalies from the power consumption time-series without explicit descriptions [29].…”

Section: Unsupervised Detection (U)mentioning

confidence: 99%

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Himeur,

Ghanem,

Alsalemi

et al. 2020

Preprint

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Enormous amounts of data are being produced everyday by submeters and smart sensors installed in different kinds of buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem to become widespread, costly and time-consuming issue. Moreover, this will help in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. Specifically, a comprehensive survey is introduced, in which a novel taxonomy is introduced to classify existing algorithms based on different factors adopted in their implementation, such as the machine learning algorithm, feature extraction approach, detection level, computing platform, application scenario and privacy preservation. To the best of the authors' knowledge, this is the first review article that discusses the anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumptions, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, and (iv) platforms for reproducibility. Following, insights about current research trends that anomaly detection technology needs to target for widespreading its application and facilitate its implementation are described before deriving a set of challenging future directions attracting significant research and development attention.

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“…Particle swarm is a comprehensive nature call based optimization policy that works on concept of swarms in search of adaptation. PSO is very simple approach to solve NP complete problems; here variables adjust their values closer to closest member to any target at any particular moment [27]. In search of hidden food source circling a flock of swarm, one will chips food that is closest to it and other flocks swing nearby.…”

Section: Problem Formulationmentioning

confidence: 99%

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

Tomar,

Pant,

Tripathi

et al. 2019

IJEAT

Self Cite

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A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful nature inspired load balancing mechanism is proposed in this paper which optimized makespan and throughput in environment of varying cloudlets and virtual machines results as compared to other conventional approaches. Proposed (EPSO) algorithm is with four scheduling policies namely FCFS, Round Robin (RR) and Shortest Job First (SJF) and get near twice good throughput percentage and minimized makespan in two different environments. Author used Cloud sim toolkit and some Open Source cloud packages to simulate the results of various scheduling components. Experimental results of various components are tested and simulated on java based CloudSim toolkit framework.

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A review of supervised and unsupervised machine learning techniques for suspicious behavior recognition in intelligent surveillance system

Cited by 50 publications

References 60 publications

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

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