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Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

This paper offers a summary of the latest studies on healthcare scheduling problems including patients’ admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients’ admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

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The manufacturing of a printed circuit board in the SMT assembly line goes through multiple phases of automatic handling. To ensure the quality of the board and reduce the number of defects, inspection tasks such as solder paste inspection and automatic optical inspection are conducted. The inspection tasks are carried out at various phases of the assembly line. The paper aims to answer the questions of how machine learning technology can contribute for better PCB fault detection in the assembly line and at which parts of the assembly line this technology has been applied. The paper discusses the PCB defect detection by using machine learning and other approaches. The current research shows that PCB defect detection using machine learning are miniscule. Early detection is still unexplored and experimented in the industry.

Clustering technique is one of the techniques which have been used to provide energy efficiency.. However Most of clustering schemes select the cluster head either randomly without considered important parameters or based on centralized approach by utilizing the base station which can affect the network scalability. In addition, single hop communication is used by CHs to forward their sensed data to the CH which lead to increases energy consumption of CHs in large scale network. Therefore in this paper clustering scheme is proposed based on distributed approach, different parameters are consider for cluster head selection and also for multi-hop communication. Results shows that the proposed scheme have better performance in term of energy consumption and number of alive sensor nodes IntroductionThe design and advance in sensor technology such as low power CMOS technology, microprocessors and low power radio frequency (RF) have made it feasible to develop cheap tiny sensors with wireless network. These Sensors will be utilized for monitoring different environments for example, battle fields, tracking objects and construction distortion detection. One of the most critical issues in WSN is power source as powered by limited small batteries that can't be replaced or recharged. So having power efficient plan for delivering packets to BS is so important due to maximizing life time of network. One of the techniques used to reduce energy consumption and results in prolong network lifetime of WSN is to partition the network into clusters [1]. Clustering technique has a lot of advantages in comparison with flat routing protocols in WSNs, since it adds more scalability, less load, reduce energy consumption. Scalability since only CHs nodes are responsible for data dissemination thus the size of routing table is reduced at the individual sensor node. Also, sensor node can generate redundant data therefore in Clustering algorithm CH node aggregation the received data from its member's nodes using data aggregation method, which in turn help to reduce redundant data and thus reduce the size of the data packets thus energy is saved [2,3]. In clustering schemes, there are

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