Wayne Goodridge

Heuristic constraint-path routing decision system

Heuristic QoS algorithms under strict constraints perform poorly in terms of finding a path that is suitable for a users QoS needs-the multiple constraint path problem (MCP). Exact QoS algorithms, on the other hand, guarantee that a path satisfying user needs would be found and offer a more realistic approach for solving the MCP problem in view of the fact that the NP-complete character of graphs are not common in real networks. This fact has driven approaches like the SAMCRA and A*prune algorithms. However, these algorithms still have very high running times relative to heuristic approaches. When QoS routing algorithms are used in online traffic engineering (TE) environments it may be necessary to route thousands of traffic flows each minute. Exact algorithms simply cannot work in such environments. We propose a heuristic algorithm that is suitable for working in an online TE environment. Simulations show that this algorithm produce high success rates in terms of finding suitable constraint paths for user flows while at the same time having execution times comparable to another heuristic based algorithms.

Intelligent diagnosis of diseases in plants using a hybrid Multi-Criteria decision making technique

An Expert System that can intelligently diagnose diseases in plants is proposed.A model for framing diseases and their pathological characteristics is presented.The disease diagnosis method uses Multi-Criteria Decision Making techniques.The AgriDiagnose system consists of a Pathology tool and a mobile app.Experimental results show an accuracy of over 95%. This paper describes an Expert System that can intelligently diagnose diseases in plants. The system is dialog-based and uses a Multi-Criteria Decision Making technique that is a hybrid of Analytic Hierarchy Process and Sensitive Simple Additive Weighting. The paper describes an approach for disease modeling that uses a set of characteristics which are weighted for each disease using two types of weights: Relative Weights and Scales. The diagnostic process involves calculating the utility value for each disease based on the utility values of its characteristics. Experimental results show an accuracy of over 95%. The system implemented is called AgriDiagnose and it consists of a web-based pathology tool to model the diseases and a mobile app for farmers to interact with the system for disease diagnosis in the field.

Traffic driven multiple constraint-optimisation for QoS routing

The core of any QoS routing algorithm designed to solve the multi-constrained optimal path problem, is a length function that is used to find the optimal route across the network. User applications require diverse optimisation requirements but typical QoS-based algorithms have fixed length functions and do not offer a flexible optimisation framework. In this paper, we present a multiple-constraint-optimisation algorithm that adapts to user traffic optimisation needs without requiring a change to core logic or the length function. This routing paradigm searches for feasible paths satisfying multiple QoS requirements and implements a routing decision support system (RDSS) that separates the constraint path finding mechanism from the optimisation mechanism. Thereby the algorithm can optimise for any type or combination of metrics. Simulations compare the performance of the RDSS algorithm with other QoS algorithms and demonstrate the feasibility of the proposed approach in finding pareto optimal paths especially under strict constraints.

The Case-Based Neural Network Model and Its Use in Medical Expert Systems

A theoretical model called the Case-Based Neural Network Model is introduced that captures selected patient cases into a data structure which incorporates the fundamental components of expert systems. This data structure is made up of a discrete pattern associative neural network of frames. The Case-Based Neural Network Model is implemented as a computer system called MED2000. This system generates appropriate questions and suggestions based on a notion of diagnosis developed by its neural network and knowledge base. When tested by medical experts, the system was found to be accurate and reproducible.

A reversible steganographic scheme for VQ indices based on locally adaptive coding

Achieving a high embedding capacity and low compression rate with a reversible data hiding method in the vector quantization (VQ) compressed domain is a technically challenging problem. This paper proposes a novel reversible steganographic scheme for VQ compressed images based on a locally adaptive data compression method. The proposed method embeds n secret bits into one VQ index of an index table in Hilbert-curve scan order. The experimental results show that the proposed method can achieve the different average embedding rates of 0.99, 1.68, 2.28, and 3.04 bit per index (bpi) and average compression rates of 0.45, 0.46, 0.5, and 0.56 bit per pixel (bpp) for n=1, 2, 3, and 4, respectively. These results indicate that the proposed scheme is superior to Chang et al.s scheme 1 [19], Yang and Lins scheme [21], and Chang et al.s scheme 2 [24].

Comparing a novel QoS routing algorithm to standard pruning techniques used in QoS routing algorithms

The problem of finding QoS paths involving several combinations of network metrics is NP-complete. This motivates the use of heuristic approaches for finding feasible QoS paths. Many constraint based routing algorithms find QoS paths by first pruning resources that do not satisfy the requirements of the traffic flow and then running a shortest path algorithm on the residual graph. This approach results in a QoS path that biases the first metric used in the search process. In addition, it can be shown that this approach may not always find the optimal path. Our research introduces a QoS routing algorithm that is based on a decision support system that is used to compute QoS paths. We demonstrate the feasibility of this approach by comparing it to standard pruning techniques.

Multiple Metric QoS Routing in Differentiated Services Networks Using Preference Functions Measurement Concepts

QoS routing protocols involving several combinations of network metrics can be difficult to solve in polynomial time. Our research introduces a novel approach that allows bandwidth brokers to perform QoS management in Diffserv Domains with any number and combination of network metrics with algorithm complexity of O(m(n + 1)) where m is the number of paths between two edge devices in the network and n is the number of metrics involved.

Integrating Two Artificial Intelligence Theories in a Medical Diagnosis Application

Reasoning Systems (Inference Mechanisms) and Neural Networks are two major areas of Artificial Intelligence (AI). The use of case-based reasoning in Artificial Intelligence systems is well known. Similarly, the AI literature is replete with papers on neural networks. However, there is relatively little research in which the theories of case-based reasoning and neural networks are combined. In this paper we integrate the two theories and show how the resulting model is used in a medical diagnosis application. An implementation of our model provides a valuable prototype for medical experts and medical students alike.