The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks

This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi

An investigation on benefits and future expectation of Industrialised Building System (IBS) implementation in construction practices

Industrialised Building System (IBS) is well known in many developing countries due to the benefits that can be derived from its applications in construction projects. However, the low percentage of IBS usage may be due to lack of awareness and knowledge about IBS among many professionals. There may be factors that contribute to a lack of interest from the client towards IBS. The aim of this study is to improve the application of IBS particularly in private construction projects in Malaysia by determining the benefits and expectation on application of IBS in private construction projects. This study adopts a quantitative method using questionnaires that were sent to 35 construction firms as a sampling frame. Finally, the finding of this study hopefully could assist professional parties in construction industry in providing a better ground knowledge for improving decisions making to achieve the success of IBS construction projects implementation and also this study will achieved the project objectives in terms of predetermined objectives that are mostly within the time, specified budget and standard qualit

Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified

Child labour: the case study in Bangladesh

Child labour involves of person that age below than 17 years old. Child labour often happen in poor countries such as Bangladesh. In Bangladesh, the issue of child labour might be the biggest issue. Bangladesh come up with Bangladesh Labour Act (BLA) that did not allow any person age below from fourteen years old to work (Nawshin et al, 2019). One of the aim or purpose of this act is to prevent teen workers in order to get the proper payment of any work. This is because when organization use child labour, they might be paid at lower rate because children usually do not have much responsible in their family compared to teen workers. This indirectly cause an economic matter in a family

Engineering and Humanities Students' Strategies for Vocabulary Acquisition: An Iranian Experience

The present study set out to investigate the differences between EAP (English for Academic Purposes) students of Humanities and Engineering in terms of vocabulary strategy choice and use. One hundred and five undergraduate Iranian students (39 students from Engineering Faculty and 66 from Humanities Faculty) studying at Bu-Ali Sina University Hamedan, during the academic year of 2011–2012 participated in this study. For data collection purposes, a pilot-tested factor-analyzed five-point Likert-scale vocabulary learning strategies questionnaire (VLSQ) containing 45 statements was adopted. The results of independent samples t-test indicated that, overall, the two groups were not significantly different in the choice and use of vocabulary learning strategies. However, running Chi square analyses, significant differences were found in individual strategy use in 6 out of 45 strategies. That is, while Humanities students used more superficial and straightforward strategies like repetition strategy and seeking help from others, the Engineering students preferred much deeper, thought-provoking and sophisticated strategies like using a monolingual dictionary and learning vocabulary through collocations and coordinates. Further, the most and the least frequently used vocabulary learning strategies by the two groups were specified, out of which only two strategies in each category were commonly shared by both groups. The possible reasons why the results have turned out to be so as well as the implications of the study are discussed in details in the paper

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.Comment: Accepted at IJCNN 201

Transfer learning for time series classification

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike for image recognition problems, transfer learning techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved if the model is fine-tuned from a pre-trained neural network instead of training it from scratch. In this paper, we fill this gap by investigating how to transfer deep CNNs for the TSC task. To evaluate the potential of transfer learning, we performed extensive experiments using the UCR archive which is the largest publicly available TSC benchmark containing 85 datasets. For each dataset in the archive, we pre-trained a model and then fine-tuned it on the other datasets resulting in 7140 different deep neural networks. These experiments revealed that transfer learning can improve or degrade the model's predictions depending on the dataset used for transfer. Therefore, in an effort to predict the best source dataset for a given target dataset, we propose a new method relying on Dynamic Time Warping to measure inter-datasets similarities. We describe how our method can guide the transfer to choose the best source dataset leading to an improvement in accuracy on 71 out of 85 datasets.Comment: Accepted at IEEE International Conference on Big Data 201

Performance Evaluation Of Qos In Wimax Network

OPNET Modeler is used to simulate the architecture and to calculate the performance criteria (i.e. throughput, delay and data dropped) that slightly concerned in network estimation. It is concluded that our models shorten the time quite a bit for obtaining the performance measures of an end-to-end delay as well as throughput can be used as an effective tool for this purpose

Issue of hiring a criminal

Hiring a criminal. Criminal refers to a person who has committed to the crime. In some other words, there is a crime that called as felony. Felony is a crime that classified as the most serious type of offenses such as fraud, physical harm or large scale of theft. Thus, hiring a criminal is defined as company wanted to hire a person who has criminal records background as an employee. Nowadays, criminal history is quite common in the country like USA, which has over 6.6 million people been under correctional supervision such as jail, prison and parole. According to Kurlychek, Bushway, & Denver (2019), employers were asked questions regarding to the criminal history and use various methods and sources to collect the criminal background information. In contrast, some companies would prefer to hire people who are nominated and found that prison record of felony convictions reduced the employer’s motivation to hire an employee (Griffith & Young, 2017). Thus, employers are making decision based on the criminal history and checks for the record to make the hiring decisions (Young & Ryan, 2019) even though the connection between the criminal records and the employment is still at the infancy stage (Griffith, Rade, & Anazodo, 2019). In recent years, the policy attention is focus on the employment for the people who has criminal background (Agan & Starr, 2017). Consequently, “Ban the Box” policies has created to revise when and how the criminal histories were disclosed to move forward to the fair chance of employment selection process (Griffith & Young, 2017) to prevent the inequalities of economics and racial problems (Agan & Starr, 2017)