The improvement of standard operating procedures (SOP) and the process of grading the fresh palm fruit (BTS) at Kilang Kelapa Sawit Risda Ulu Keratong

Industrial Engineering and Quality Management is a branch of engineering subject that giving people knowledge about management in the industry. An industrial visit had been done due to this subject needed. The purpose of the visit is to identify an improvement that needed for the industry to make it more efficient and produce a good quality product and also managing the employee behaviour while at work. Industrial Engineering works to eliminate waste of time, money, materials, person-hours, machine time, energy, and other resources that do not generate value[1]. Industrial engineering is concerned with the development, improvement, and implementation of an integrated system of people, money, knowledge, information, equipment, energy, materials, analysis and synthesis. From all of the topic concern in industrial engineering then should be applied to the industry. In the factory sometimes have a bit of issue that they not notice out so there the function of Industrial Engineering will work. So from the industrial visit, there are some improvements that can be made such as grading system and standard of procedure. The old grading system of oil palm was not accurate enough, thus causing an error and producing poor oil quality. As the oil has less their grade so it will reduce the income of the factory because the buyer of the oil doesn’t take the responsibility due to the less of oil quality

SIEM Network Behaviour Monitoring Framework using Deep Learning Approach for Campus Network Infrastructure

One major problem faced by network users is an attack on the security of the network especially if the network is vulnerable due to poor security policies. Network security is largely an exercise to protect not only the network itself but most importantly, the data. This exercise involves hardware and software technology. Secure and effective access management falls under the purview of network security. It focuses on threats both internally and externally, intending to protect and stop the threats from entering or spreading into the network. A specialized collection of physical devices, such as routers, firewalls, and anti-malware tools, is required to address and ensure a secure network. Almost all agencies and businesses employ highly qualified information security analysts to execute security policies and validate the policies’ effectiveness on regular basis. This research paper presents a significant and flexible way of providing centralized log analysis between network devices. Moreover, this paper proposes a novel method for compiling and displaying all potential threats and alert information in a single dashboard using a deep learning approach for campus network infrastructure

Pixel Value Graphical Password Scheme: K-Means as Graphical Password Fault Tolerance

Pixel value access control (PVAC) was introduced to deliver a secure and simple graphical password method where it requires users to load their image as their password. PVAC extracts the image to obtain a three-octet 8-bits Red-Green-Blue (RGB) value as its password to authenticate a user. The pixel value must be matched with the record stored in the database or otherwise, the user is failed to authenticate. However, users which prefer to store images on cloud storage would unintentionally alter and as well as the pixel value due to media compression and caused faulty pixels. Thus, the K-Means clustering algorithm is adapted to fix the issue where the faulty pixel value would be recognized as having the same pixel value cluster as the original. However, most of K-Means algorithm works were mainly developed for content-based image retrieval (CBIR) which having opposite characteristics from PVAC. Thus, this study was aimed to investigate the crucial criteria of PVAC and its compatibility with the K-Means algorithm for the problem. The theoretical analysis is used for this study where the suitable characteristics of K-Means are analyze based on PVAC requirements. The compliance analysis might become a referencing work for digital image clustering techniques adaptation on security system such as image filtering, image recognition, and object detection since most of image clustering works was focused on less sensitive image retrieval

Comparative statistics of the MGE13 P_IntS scores between P_IntS(posts), P_IntS(likes) and P_IntS(31).

<p>Comparative statistics of the MGE13 P_IntS scores between P_IntS(posts), P_IntS(likes) and P_IntS(31).</p

MGE13 P_IntS(posts) chart.

<p>P_IntS(posts) chart for MGE13. The y-axis shows the IntS scores and the x-axis indicates the day of the MGE13 campaign. The lines are coloured specific to each of the 47 MGE13 candidates, and drawn based on the accumulated P_IntS scores, while using as the baseline probability. The movement of the lines across the chart illustrates the strength of the passive interactions that occurred during the MGE13 campaigning period.</p

Total <i>Posts</i> and <i>Likes</i> of the MGE13 FP data.

<p>Number of posts (blue bar) and the acquired likes (green bar) recorded on the FP of 51 candidates during the MGE13 campaign. The y-axis shows the log of posts and likes of each candidate as indicated on the x-axis. The purple line indicates the average number of likes received, while the red line indicates the average number of posts published. The graph also shows the total number of likes gained by Najib Razak (<i>najib</i>) and Anwar Ibrahim (<i>anwar</i>) over the 33 days of campaigning.</p

MGE13 daily <i>Posts</i> and <i>Likes</i>.

<p>Line graph of the number of posts (blue line) with the acquired number of likes (green line) for every day over the period of the MGE13 campaign.</p

Sample of baseline probability, for MGE13.

<p>Sample of baseline probability, for MGE13.</p

MGE13 Linear Regression (Ln).

<p>Scatterplot with Linear Regression Line of Ln Likes () vs Ln Posts () for MGE13 data.</p