Pedestrian Detection using Triple Laser Range Finders

Rahman, Abdul Hadi Abd; Ariffin, Khairul Akram Zainol; Sani, Nor Samsiah; Zamzuri, Hairi

doi:10.11591/ijece.v7i6.pp3037-3045

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Certain types of data require encryption due to prevent attacks such as odometry, action controller and perception data. There are various types of data such as strings, images and point cloud data [1], [2], [3]. Robotic Operating System (ROS) provides a reliable platform for robot application but is vulnerable to cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Peer Robot Communications using CryptoROS

Rahman

Sulaiman

Sani

et al. 2019

IJACSA

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The demand of cloud robotics makes data encryption essential for peer robot communications. Certain types of data such as odometry, action controller and perception data need to be secured to prevent attacks. However, the introduction of data encryption caused increment of overhead for data stream communication. This paper presents an evaluation of CryptoROS architecture on Robot Operating System (ROS) which focused on peer-to-peer conversations between nodes with confidentiality and integrity violation. OpenSSL is used to create a private key and generate a Certificate Signing Request (CSR) that contains public key and a signature. The CSR is submitted to a Certificate Authority (CA) to chain the root CA certificate and encryption of RSA private key with AES-256 and a passphrase. The protected private key are securely backed up, transported, and stored. Experiments were carried out multiple times with and without the proposed protocol intervention to assess the performance impact of the Manager. The results for different number of messages transmitted each time increased from 100, 250 to 500 with performance impact 1.7%, 0.5% and 0.2%, respectively. It is concluded that CryptoROS capable of protecting messages and service requests from unauthorized intentional alteration with authenticity verification in all components.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Peer Robot Communications using CryptoROS

Rahman

Sulaiman

Sani

et al. 2019

IJACSA

Self Cite

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“…Supervised learning is related to a scenario in which the "experience", otherwise referred to as a training example, contains significant information (the labels) missing in the unseen "test examples" of which the learned expertise is to be applied. In unsupervised learning, there is no distinction between training and test data [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification

Sani¹,

Rahman²,

Bakar³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Malaysia citizens are categorised into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. Based on recent studies by the World Bank, Malaysia is expected to enter the high-income economy status no later than the year 2024. Thus, it is essential to clarify the B40 population through a predictive classification as a prerequisite towards developing a comprehensive action plan by the government. This paper is aimed at identifying the best machine learning models using Naive Bayes, Decision Tree and k-Nearest Neighbors algorithm for classifying the B40 population. Several data pre-processing task such as data cleaning, feature engineering, normalisation, feature selection: Correlation Attribute, Information Gain Attribute and Symmetrical Uncertainty Attribute and sampling methods using SMOTE has been conducted to the raw dataset to ensure the quality of the training data. Each classifier is then optimized using different tuning parameter with 10-Fold Cross Validation for achieving the optimal values before the performance of the three classifiers are compared to each other. For the experiments, a dataset from National Poverty Data Bank called eKasih obtained from the Society Wellbeing Department, Implementation Coordination Unit of Prime Minister's Department (ICU JPM), consisting of 99,546 households from 3 different states: Johor, Terengganu and Pahang are used to train each of the machine learning model. The experimental results using 10-Fold Cross-Validation method demonstrates that the overall performance of Decision Tree model outperformed the other models and the significance test specified the result is statistically significance.

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“…The importance of performing feature selection before modelling any data set can contribute towards improving modelling accuracy, less opportunity to make decisions based on noisy data and faster computational time to train models [4]. Furthermore, feature selection must not be confused with dimensionality reduction, where the latter involves encoding mechanisms used to reduce the data set size [5]. The methods for feature subset selection algorithms can be divided into filters, wrappers and embedded approaches [6].…”

Section: Introductionmentioning

confidence: 99%

Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection

Sani¹,

Shamsuddin²,

Sahran³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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The exponential growth of todays technologies has resulted in the growth of high-throughput data with respect to both dimensionality and sample size. Therefore, efficient and effective supervision of these data becomes increasing challenging and machine learning techniques were developed with regards to knowledge discovery and recognizing patterns from these data. This paper presents machine learning tool for preprocessing tasks and a comparative study of different classification techniques in which a machine learning tasks have been employed in an experimental set up using a data set archived from the UCI Machine Learning Repository website. The objective of this paper is to analyse the impact of refined feature selection on different classification algorithms to improve the prediction of classification accuracy for room occupancy. Subsets of the original features constructed by filter or information gain and wrapper techniques are compared in terms of the classification performance achieved with selected machine learning algorithms. Three feature selection algorithms are tested, specifically the Information Gain Attribute Evaluation (IGAE), Correlation Attribute Evaluation (CAE) and Wrapper Subset Evaluation (WSE) algorithms. Following a refined feature selection stage, three machine learning algorithms are then compared, consisting the Multi-Layer Perceptron (MLP), Logistic Model Trees (LMT) and Instance Based k (IBk). Based on the feature analysis, the WSE was found to be optimal in identifying relevant features. The application of feature selection is certainly intended to obtain a higher accuracy performance. The experimental results also demonstrate the effectiveness of Instance Based k compared to other ML classifiers in providing the highest performance rate of room occupancy prediction.

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Pedestrian Detection using Triple Laser Range Finders

Cited by 9 publications

References 13 publications

Evaluation of Peer Robot Communications using CryptoROS

Evaluation of Peer Robot Communications using CryptoROS

Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification

Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection

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