Empowering Cybersecurity: A Deep Dive into AI-Driven Security Intelligence Modelling
Smart Incubator for Empowering Poultry Farmers by Maximizing Egg Hatching Success
Early Disease Detection of Black Gram Plant Leaf using Cloud Computing Based YOLO V8 Model
Data Retrieval in Cancer Documents using Various Weighting Schemes
Securing Healthcare Data in Smart Cities: A Machine Learning Approach
This paper is on usability evaluation of web search engines using navigational query model examples from library and information services. The paper adapted laboratory experimental design in which a study population of five web search engines, namely: Ask.com, Bing, Excite, Google, and Yahoo! web search engines, were selected for the study, and twenty one master degree students from the Department of Library and Information Technology, Federal University of Technology Minna, Nigeria were recruited and trained to perform usability evaluation of the selected web search engines by submitting queries drawn from library and information services to the various web search engines and assessing/judging their performances based on the relevance of the output results. The instrument used for data collection was an observation template/form used to record the observations made by the participants. The instrument contains five queries each of which has six usability effectiveness constructs/statements developed. The study revealed that there is no significant difference in usability effectiveness between web search engines using navigational query model, i.e, overall the web search engines performed well, but at different levels of performances. Specifically, Google has the highest performance on usability effectiveness using navigational query model, as reflected in its mean score of 3.59. Yahoo! ranked second with a mean score of 3.50, the third is Ask.com which has the mean score of 3.29, followed by Bing with a mean score of 3.01, and lastly Excite which has a mean score of 2.73. Hence, it was recommended to Excite web search engine's designers to improve on their navigational query model for better performance and enhance their users' experience. This study will provide deeper understanding to search engine developers on the significance of usability performances of web search engines using navigational query models, so that they can put these into consideration when designing a web search engine.
Research on website usability evaluation have been growing in recent times as a result of increased internet penetration rate globally. In view of this, several efforts have been made to develop usability evaluation methods for different genre of websites. Due to its uniqueness, university websites which is a class of academic websites require further attention. This paper hence provides a brief survey of various usability evaluation methods used in university websites and with special focus on Multi Criteria Decision Making (MCDM) approaches. A total of thirty five scholarly articles were retrieved from online database from year 2000 to 2018. The review was classified based on usability evaluation methods, MCDM approaches, and automated tools. Results from the trend analysis shows that automated tools, heuristic evaluation, and user testing methods are the mostly used usability evaluation methods. MCDM approach usage in usability evaluation is also found to be on the increase as well. It is therefore recommended that more attention should be given to the usage of this methodology in the usability evaluation of this important genre of websites.
Tremendous growth in the availability of social media data and its collection and storage is moving towards the big data problems. Cloud-based data centres have adopted replication control mechanisms for the data distribution strategies to handle such huge volumes of data. These methods increase the query complexity and chance of the attacks. Parallel researches demonstrated that the large scale data demands in time complexity reduction and early attack detection. Most of the attacks depend on the network and application characteristics. Online personal information tracks deep insights about how people assemble numbers, rates, and attracted towards a particular substance or channel. This information of the Internet users can be gathered easily from their daily based activities over the Internet. However, the popular social media applications such as Twitter do not primarily signify the attacks or a chance of potential attack characteristics. With the added security concerns, it has been observed that the parallel research attempts have failed to justify the time complexity. Hence, this research proposes two novel algorithms as firstly, a secure distributed large volume data query algorithm using novel secure data discovery technique and secondly, a cognitive attack detection method using data characteristics analysis by deploying machine learning method. The final outcome of the research is to build a novel automated framework to detect the data centre clusters, which are potentially under attack.
An Educational Institution timetable is a tentative schedule of a number of lectures and classrooms where all constraints are met. Creation of timetables is typical and time taking process. The automated computer timetable generator can save a lot of valuable time of administrators, especially who contribute their efforts for creating and managing course timetables. Every Educational Institutions are having their individual timetabling system, proprietary software, which are available in the market that may meet the needs of every Educational Institution. Therefore the authors have developed automated customized timetable generating system, which fits to any Educational Institution timetabling problem. They found a lot of problems with infinite solution space. And identifying a best optimum solution for the problem with a minimum or less performance cost is hard and difficult task. Genetic algorithm though gave a beautiful solution through its fitness evolution strategy and its principle 'the survival of the fittest', still it is costlier in performance due to its bulky iterative process and it also risks the local optima. Thus in this paper, PBIL Algorithm (Population Based Incremental Learning Algorithm) is applied to frame the best possible timetable with even load distribution taken as the optimality criteria.
Opportunistic network (oppnets) is a promising paradigm, which allows communication to be set up between nodes even without infrastructure in a delay tolerant fashion. It has even become more pertinent with the proliferation of varieties of autonomous mobile devices. However, malicious devices pose potential security threats (packet dropping, eavesdropping, Denial of Service (DoS) attack, black hole attack, Identification (ID) spoofing, etc.) to the performance of oppnets, due to the inherent characteristics of such networks like ever changing network topology and lack of a definite communication path between nodes amongst others. Recent focus on mitigation mechanisms for security threats in oppnets is on trust management since various mechanisms have been used, but the trust-based methods appeared to meet up with the security requirement of the opportunistic network better. However, no work strictly present trust management extensively. This paper presents trust management together with trust related issues in oppnet (trust-based security threats, trust mechanism, and the trust management scheme as oppnet security solution). It is aimed at providing the reader a clear understanding of trust management (preferred solution) within a single literature.
