European Journal of Computer Science and Information Technology (EJCSIT)

EA Journals


Information Overload: A Conceptual Model (Published)

This age of massive production and usage of information ranging from online resources to print has constantly created the need to educate individuals on Information overload, which happens when one is saddled with the task of processing and accessing excessive information at work and in life generally. Information overload is the abundance of information with limited cognitive processing capacity to the receiver. Despite its widespread discussion, a universally accepted definition or explanation remains elusive due to the diverse terminology employed. This variation in terminology implies differing levels of information overload. There is a dire need to develop a variety of models that assist information designers in understanding, measuring, and determining when an individual becomes overloaded with information. Drawing on Dubin’s theory, which provides a systematic framework for conceptual model development, this study utilizes the initial stages of theory building to create a Conceptual Model of information overload and its Primary Components together with their Sub-components. This model serves as a foundation for generating testable hypotheses and operationalizing the concept of information overload for further empirical investigations.

Keywords: Information, Model, cognitive overload, information processing, overload

A Model for Malicious Website Detection Using Feed Forward Neural Network (Published)

Malicious websites are the most unsafe criminal exercises in cyberspace. Since a large number of users go online to access the services offered by the government and financial establishments, there has been a notable increase in malicious websites attacks for the past few years. This paper presents a model for Malicious website URL detection using Feed Forward Neural Network. The design methodology used here is Object-Oriented Analysis and Design. The model uses a Malicious website URLs dataset, which comprises 48,006 legitimate website URLs and 48,006 Malicious website URLs making 98,012 website URLs. The dataset was pre-processed by removing all duplicate and Nan values, therefore making it fit for better training performance. The dataset was segmented into X_train and y_train, X_test and y_test which holds 60% training data and 40% testing data. The X_train contains the dataset of malicious and benign websites, while the y_train holds the label which indicates if the dataset is malicious or not. For the testing dataset the X_test contains both the malicious and non-malicious websites, while the y_test holds label which indicates if the dataset is malicious or not. The model was trained using Feed Forward Neural Network, which had an optimal accuracy 97%.

Keywords: Model, Neural network, detection, feed, malicious website


The context of Understanding has continued to be a major attraction to researchers in Natural Language Processing. This is built on the theory that language can be used effectively if it is understood and can be analyzed and as such, most Natural Language Processing research tend towards the belief that the human brain has a section dedicated for language analysis and understanding therefore, human ambiguity which, remains the major difference between natural and computer languages, can be modeled using appropriate man machine modeling tools since programming languages are designed to be unambiguous, that is, they can be defined by a grammar that produces a unique parse for each sentence in the language. The paper evaluates the classification process for a Natural language ‘the Yoruba language’ and presents a new method by which the language can be transformed into a computer understandable language using its morphological identification framework. Result shows that the approach is admissibly in line with known benchmarks. The paper recommends that non tonal language can also be experimented using the defined approach.

Keywords: Model, Morphology, Natural language, Tonal Language, Yoruba

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