School-wide publications
Permanent URI for this collectionhttps://dspace.njala.edu.sl/handle/20.500.144402/182
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Item Review of Knowledge Management in Optical Networks, Lambda Architecture using Database Technologies in Cloud Settings(International Journal of Scientific and Research Publications, 2021-08-24) Fofanah, Abdul JosephIn this paper, we present concepts, theories, and overview of knowledge management in an autonomous optical networks and Lamba Architecture in cloud related environment.This study presents some illustrative cases that has been used to illustrate the potential application of KM architecture and to evaluate the various policies for the knowledge sharing and integration algorithm.Here the knowledge is used at the optical transponder system level, sharing, and integration implemented at the node level and supervising and data analytics (SDA) controller level.The KM process has been evaluated by integrating on a metro-network situation in terms of model error convergence time and the data shared among agents.Indeed, the propagation and reinforcement actions illustrated by similar convergence time than data-based policies at various phases of the network learning process without compromising the convergence accuracy of the model prediction.The Lambda Architecture is the new model for Big Data and database research focus, that helps in data processing with a balance on throughput, latency, and fault-tolerance.To provide a complete solution and better accuracy, low latency, and high throughput, there exists no single tool.This introduced the idea to use a set of tools and methods to build a comprehensive Big Data approach.Although this paper does not provide a developed and working tool, however, provides an outline and the methods used by researchers to overcome some of the shortcomings of Lambda Architecture.The Lambda Architecture defines a set of layers to fit in a set of tools and methods rightly for constructing a comprehensive Big Data scheme: Speed Layer, Serving Layer, Batch Layer.Each layer satisfies a set of features and builds upon the functionality delivered by the layers beneath it.The Batch Layer is the place where the master dataset is warehoused, which is an unchangeable and add-only set of raw data.Also, the batch layer computes before the results using a distributed processing system like Hadoop, Apache Spark that can manage large amounts of data.The Speed Layer encapsulates new data coming in real-time and processes it.The Serving Layer comprises a parallel processing query steam engine, that takes results from both Batch and Speed Layers and responds to questions and requests in real-time with low latency.Stack Overflow is a Question-and-Answer forum with an enormous user community, millions of posts with rapid growth over the years.Item Adaptive Neuro-Fuzzy Inference System with Non-Linear Regression Model for Online Learning Framework(RELX Group (Netherlands), 2021-10-01) Fofanah, Abdul JosephIn this paper, we present a combined hybrid of an adaptive neuro-fuzzy inference system with an adaptive online learning model (ANFISAOLM) using a non-linear regression machine learning technique to construct the Neural Network. We proposed ANFIS-AOLM for learning by modeling and controlling imprecise defined, uncertainty system with a significant role of the neuro-fuzzy method. The various simulation exercise was conducted with define input and output values, define rules and the dataset was created and trained the Neural Network. Exploration of the dataset was tested for ANFIS-I using “Grid Partitioning”; ANFIS-II using “Subtractive Clustering”; and ANFIS-III using “Fuzzy Clustering Means”. The study was conducted in order to explore the effectiveness of the combined hybrid learning opportunities using ANFIS and Non-linear Regression models. The study was experimented using MATLAB programming language for the adaptation of the online learning framework with learner’s capability to learn from the existing information and adapt accordingly in the learning environment. The results obtained from the analysis shows that with a membership function of 20 and 25 an average of 97.7% accuracy was achieved. However, as the number of membership functions increases the better the performance of the ANFIS and the higher the computational response time. The non-linear regression model also indicated that the relationship of the ANFIS system and the proposed technique is adequate for learner’s adaptation and the decision made through learning mechanism.Item Experimental Exploration of Evolutionary Algorithms and their Applications in Complex Problems: Genetic Algorithm and Particle Swarm Optimization Algorithm(2023-04-18) Fofanah, Abdul JosephThe primary aim of this study was to experiment and compare the empirical evidence of evolutionary algorithms and their applications using the Genetic Algorithm (GA) and Particle Swamp Optimization (PSO) algorithm. The objectives of the study included reviewing recent scholarly scientific research papers and practices, and algorithms with existing genetic algorithm techniques, identifying the need for lessons learned and leveraging best health practices garnered from the existing problems of optimization, reviewing and identifying the technological tools used in the healthcare domain, algorithms, and the effectiveness of GA and PSO techniques and to develop and analyse GA algorithms using datasets to predict and determine which of the two algorithms performs better. Evolutionary algorithms which are normally viewed as components of both evolutionary computing and bio-inspired computing use mechanisms inspired by nature and are indeed capable of solving problems through processes that emulate the behaviours of living organisms. Genetic algorithms (GAs), evolution strategies (ESs), differential evolution (DE), and particle swarm optimization (PSO) are typical examples. Therefore, evolutionary algorithms are typically used to provide good, estimated solutions to problems that cannot be solved easily using other techniques, and as such numerous optimization problems fall into this category. This work aims to provide a systematic evaluation of the literature on contemporary PSO methods for knowledge discovery in the area of illness detection. Although the major objective is to present recommendations for future enhancement and development in this field, the systematic analysis reveals the possible study topics of PSO techniques as well as the research gaps. The essential ideas, theoretical underpinnings, and traditional application domains are covered in this study. It is fervently anticipated that the findings will help researchers to thoroughly examine PSO algorithms for disease diagnosis. According to the current state of PSO strategies in healthcare, several difficulties that can be taken on to advance the sector are mentioned.Item ST-FLAM: Evaluating Performance of Deep Learning Models on Mobility Patterns for EVD Forecasting based on Spatio-Temporal Feature Learning(ACM, 2024-12-20) Fofanah, Abdul Joseph