Emotion and Adaptation

https://www.healio.com/psychiatry/journals/psycann/1976-12-6-12/{806b8887-c02e-40c8-a228-57f1cdb2bdba}/the-stress-of-life.pdf

The stress of life

LEARNING The objectives of BIOS 781 are to present: OBJECTIVES: 1. basic population and quantitative genetic principles, including classical genetics, chromosomal theory of inheritance, and meiotic recombination 2. an exposure to QTL mapping methods of complex quantitative traits and linkage methods to detect co-segregation with disease 3. methods for assessing marker-disease linkage disequilibrium, including case-control approaches 4. methods for genome-wide association and stratification control.

Genetic Data Analysis

https://www.bmj.com/content/bmj/1/6059/515.3.full.pdf

Handbook of Enzyme Electrophoresis in Human Genetics

https://romisatriawahono.net/lecture/rm/survey/softcomputing/Manjarres%20-%20Harmony%20Search%20Algorithm%20-%202013.pdf

Survey A survey on applications of the harmony search algorithm

Enterprise resource planning (ERP) is a broad set of activities that is supported by multimodule application software that helps manufacturers or other businesses manage their activities. ERP is more than a software package; it includes the efforts and activities to integrate internal and external management information, business process automation and reengineering, and organisation structure streamlining. Thus, ERP eventually leads to an efficient and competitive business. Despite the prominence of ERP systems, approximately three-quarters of ERP projects are deemed as failures. Most failures are attributed to the misfit between pre-loaded business models in the ERP system and business requirements in the real world. This paper introduces a framework to classify ERP misfits into logical categories that provide insights for solution derivation. Subsequently, the classification methods are applied to a case study. Practitioners can use the misfit classification method to derive corresponding actions as solutions for ERP misfits based on their nature or specificity. In addition, the theoretical contribution of the ERP misfit problem is explored to provide information for researchers to determine appropriate theories and concepts explaining this domain.

/pdf/a-framework-for-classifying-misfits-between-enterprise-3vdzt5zyc7.pdf

A framework for classifying misfits between enterprise resource planning (erp) systems and business strategies

Dynamic crowd evacuation approach for the emergency route planning problem: Application to case studies

Credit card fraud is a serious and growing problem. It is becoming more challenging with an issues of highly imbalanced class. In the field of data mining, prediction or typically known as data classification problem involved detecting events. Uncommon events are hard to identify on account of their inconsistency and casualness, however, misclassifying rare events can result in heavy costs. Thus, to overcome this issues, it is suggested by few researches to overcome at the stage of pre-processing itself. One of the pre-processing methods available is sampling methods. In sampling methods, oversampling and undersampling is the most widely used techniques in imbalanced data. This paper try to investigate the performance of classification model when combining the method of oversamplings and undersampling in detecting the fraud cases from the fraud detection dataset. Few oversampling techniques is selected to combine with random undersampling techniques to undergo the process and the performance of the model is evaluate using random forest classifier and performance measure selected that is suitable for imbalanced class problem. The results obtained then compared with previous literature. From the results, the combination of oversampling and undersampling techniques gives a better precision, recall and F1-Measure value in average of 0.80%.

Combining oversampling and undersampling techniques for imbalanced classification: A comparative study using credit card fraudulent transaction dataset

Web visual aesthetics and its usability play very important roles in determining the success of the Website. It is difficult to decide which aspect needs to be given more attention when it comes to designing a Website as aesthetics is focusing on the ‘look’ and ‘feel’ while usability is stressing on its functionalities. There is no perfect answer which aspects of the Web design are more important than the others. In addition, it is also difficult to have a balance of these two seemingly contrary objectives of Web design. This research focuses on how to balance the aesthetics design and the usability of Web design. This study also investigates methods to evaluate Web page's usability and aesthetic appeal, the role of these two aspects play in the Website design and also the relationship between these two aspects. The results of study provide a tangible concept on how the Web usability and the Web aesthetics design affect each other and methods to balance between the Web usability and aesthetics design. The contribution of this paper is to provide guiding principle to the Website developer in order to develop a Website which is wellbalance in usability as well as its aesthetics aspect.

Balancing between usability and aesthetics of Web design

Feature selection aims to confiscate inappropriate features and yet improve classification performance. These aims are conflicting with one another, and a choice must be made in the presence of the trade-off between them. Numerous researches deal with feature selection problem but, they are mostly single-objective based. Nowadays, multi-objective optimisation approaches are becoming the most suitable approaches to deal with feature selection problems. They can easily create a balance between selected features and classification accuracy or error rate. Evolutionary computation techniques have been applied for multi-objective feature selection. Cuckoo optimisation algorithm is among the most popular technique that is exceptional in solving the problems of feature selection. Based on the binary cuckoo optimisation algorithm, two different multi-objective filter-based feature selection frameworks are presented with the idea of nondominated sorting genetic algorithms NSGAIII (BCNSG3) along with NSGAII (BCNSG2). Thus, four multi-objective filter-based feature selection approaches are proposed by employing mutual information along with gain ratio based-entropy as the respective filter evaluation measures in all the proposed frameworks. The results obtained are examined and analysed against the existing methods and single objective scheme on fourteen (14) datasets of varying degree of difficulties. The outcome of the experiments displays that the proposed multi-objective algorithms successfully derive a set of nondominated solutions that used the least feature size and attained the best error rate than using full-length features. In general, BCNSG2 obtained the best results compared to the existing methods and single-objective algorithm, whereas BCNSG3 outdoes all other approaches.

/pdf/filter-based-multi-objective-feature-selection-using-nsga-1d2zgipesz.pdf

Umi Kalsom Yusof

Papers

A framework for classifying misfits between enterprise resource planning (erp) systems and business strategies

Dynamic crowd evacuation approach for the emergency route planning problem: Application to case studies

Combining oversampling and undersampling techniques for imbalanced classification: A comparative study using credit card fraudulent transaction dataset

Balancing between usability and aesthetics of Web design

Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm