Model Management for Cybernetic Decision Support Systems

Managers being human have limited information processing capacity and are subject to judgmental biases, inferential shortcomings, and ignorance of the rules for optimal information processing and decision-making. Decision aids in the form of human computer information processing systems such as decision support systems, are sometimes employed to assist and support human managers in various tasks. These decision aids unfortunately do not fulfill the requirements and expectations of managers in arriving at the desired solution. This is due to the fact that the tools provided are not suitable for the managers, as they do not put sufficient emphasis on the human aspects of decision-making. Most recent models for decision support systems presented by researchers in model management are based on Operational Research or Artificial Intelligence, and are not adequate. They are based on a simplistic question-answer environment which does not reflect the real-life situation. They also do not put enough emphasis on the intelligent, deciding and reasoning side of the model. Furthermore, the models have little capacity to learn and adapt to new environments and needs. Thus, the proposal in this thesis is for a new system called Model Manager System (MOMS) that incorporates Artificial Intelligence and a Cybernetic Approach with the actual Decision-Making Environment. In order to design the proposed decision model system, various areas are explored - such as the decision-making process, managerial behaviour, human information system, and available decision aids - where various elements related to human decision making are considered. As Cybernetic tools are used in designing the model, human aspects are emphasised greatly, especially in the Information Processing techniques such as intelligence, control, coordination, monitoring, and implementation. The designed system tries to mimic Human Information Processing wherever possible

Combination of GREEN and SHRed AQM for short-lived traffic

The majority of traffic flows dominate Internet traffic is Web interactions where they are short-lived HTTP connections handled by TCP.Short-lived traffic is more sensitive to delay and has small congestion windows cwnds.This paper introduces a new active queue management (AQM) algorithms based on combination of GREEN algorithm and SHRED, to tackle issues on Short-lived flows.Active Queue Management (AQM) refers to a method to enhance congestion control, and to achieve trade off between link utilization and delay. Several example of AQM model is Random Early Detection (RED), Blue and GREEN (Generalized Random Early Evasion Network).RED has the potential to overcome some of the problems such as synchronization of TCP flows. To evaluate the performance of new algorithm, network simulation has been done using NS-2 simulation.This study provides a series of NS-2 experiments to investigate the behavior of new algorithm.The results show improvement on short-lived traffic

Artificial intelligence in decision-making

The technology of Artificial Intelligence(AI) is expanding rapidly currently. Decision-making is one of the area where the application of AI is gaining momentum. In this article, the role of AI in decision-making is discussed. This role is particularly given emphasis. This is due to its ability to handle information regarding decision-making as well as solutions for a specific problem. The discussion also focus on the Expert System component for handling the reasoning of a suggested solution

Soft modelling for decision maker: a review

This paper discuss some of soft modelling used by decision maker to build Decision Support Systems Generator, such as mathematical or equadonal model, process model, formulae function etc. Mathematical model used in optimization, what-if, and goal-seeking mode. A process or functional model is used in the form of formal procedure to model human sense, perception and emotion

Analisis kemalangan jalanraya menggunakan model regrasi berganda

Multiple linear regression is a model that incorporates the variable with several variables in a linear relationship. This paper try to introduce the basic discussions of the state model. The discussion included a general linear regression model, assumptions, calculated parameters and others. The SAS/STAT package was used in the analysis for the problem solving

Regrasi linear mudah: kajian kes jumlah kemalangan jalan raya di Malaysia Barat

Simple linear regression is a model that incorporates the two variables in a linear relationship. This paper tried to introduce the basic discussions of the state model. The discussion included a general linear regression model, assumptions, calculated parameters and how the model was used in the prediction and assumption. The SAS/STAT package was used in the analysis for the problem solving

Pendekatan analisis penyampulan data menggunakan model analisis regresi di dalam proses perlantikan Professor Madya di Universiti Teknologi Malaysia

Analisis Penyampulan Data (APO) biasanya mcnggunakan model pengaturcaraan linear untuk mengukur kecekapan relatlf setlap cawangan (atau dikenali juga dengan panggilan unit) di dalam sesebuah organisasi. Selain daripada model tersebut, model analisis regresi juga boleh digunakan bag! melaksanakan analisis yang sama. Menggunakan konsep yang sama, APD juga boleh dikembangkan untuk menilai preastasi kakltangan dl dalam sesebuah jabatan. Dalam kertaskerja ini model regresi akan digunakan untuk mengira kecekapan pensyarah-pensyarah untuk dilantik ke jawatan Professor Madya. Kertaskerja ini memblncangkan teorl-teorl pengenalan regresi dan sebuah kes kajian bagi menerangkan bagalmana model regresi boleh digunakan untuk tujuan tersebut. Disamping menentukan kecekapan setlap pensyarah, model ini juga menerangkan mengapa seorang pensyarah Itu dlanggap lebih cekap dibandingkan dengan yang lain. Pakej komputer SAS/STAT digunakan di dalam analisis kes kajian

Requirements engineering process improvement: a review and research agenda

The Malaysian Software Engineering Conference (MySEC) is the leading regional conference on software engineering that aims to bring together researchers and practitioners from the academia, industry and government to advance state-of-the-art research and practice in software engineering