A Comparative Analysis of Data Mining Techniques on Breast Cancer Diagnosis Data using WEKA Toolbox

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors

House of Cards: developing KPIs for monitoring cybersecurity awareness (CSA)

Non-malicious insider threats continue to pose a significant concern to an organisation’s cybersecurity defence strategy, yet organisations still struggle to contain such insider threats. A critical pillar for doing so rests on the development and monitoring of Cybersecurity Awareness (CSA) programmes. CSA programmes need to be both prioritised and acknowledged as an important and crucial approach to the reduction of such threats. Although CSA programmes are developed on an ad-hoc basis by many organisations, the effectiveness of such programmes and how their entire lifecycle needs to be reviewed, monitored and managed needs to be further explored. In order to do so, this paper extracts a number of key performance indicators (KPIs) for monitoring CSA programmes. The paper relies on empirical data from an in-depth case study of University X in Saudi Arabia and sensitises the research approach by using Kirkpatrick’s four level model as a theoretical scaffold. Through the combined use of Kirkpatrick’s model that is recognised as a comprehensive model for evaluating the results of training and learning programmes and the empirical data from the case study, we offer a customised CSA-oriented model for managing cybersecurity awareness programmes, reflect on its associated KPIs, and consider broader information security management considerations

Adaptation based on learning style and knowledge level in e-learning systems

Although there have been numerous attempts to build and evaluate adaptive e-learning systems, they tend to be limited in scope, and suffer from a lack of carefully designed and controlled experimental evaluations of their effectiveness and usability. This thesis addresses these issues through the implementation of an adaptive e-learning system and its experimental validation. The design of an adaptive framework and the specific instantiation of its components into a configurable adaptive e-learning system are presented. The domain model of the system deals with computer security. The learner model incorporates the information perception dimension of the Felder-Silverman model of learning style and also knowledge level. The adaptation model generates personalised learning paths and offers adaptive guidance and recommendation. The thesis also provides an empirical evaluation through three controlled experiments to investigate the effect of different forms of adaptation. Rigorous experimental design, careful investigation and precise reporting of results are taken into account in all the three experiments. The findings indicate that matching the sequence of learning objects to the information perception learning style yields significantly better learning outcome and learner satisfaction than non-matching sequences. They also indicate that adaptation based on the combination of the information perception learning style and knowledge level yields significantly better learning outcome (both in the short- and long-term) and learner satisfaction than adaptation based on either of these learner characteristics alone; this combination is also marked by a significantly higher level of perceived usability compared to a non-adaptive version of the e-learning system

The importance of the necessary requirements for the Production of knowledge as input to build a competitive advantage in the University of Hail at K.S.A

The study aims to see the degree of importance of necessary requirements for the production of knowledge to build a competitive advantage in the University of Hail in K.S.A., and to see if there are significant differences between the average responses of sample individuals The study around identify the necessary requirements for the production of knowledge to build a competitive advantage in the University of Hail to (gender, years of experience in the practice of administrative work and professional rank). The researcher used the descriptive method, and used a questionnaire to collect data from a random sample. In the target community (67.5%) were sampled. The study found the following results: The necessary requirements of knowledge production as input to build a competitive advantage in the University of Hail (regulatory requirements - human requirements - physical requirements) is very high degree. average (4.54). There are no statistically significant differences between the responses of the average members of the research sample about the necessary requirements of knowledge production as input to build a competitive advantage in the University of Hail. The study recommends the adoption of the university to the requirements contained in the study

The Effect of Real Exchange Rate on Current Account

This research studies the impact of real Gross Domestic Production (GDP) and real exchange rate on the Current Account position in Jordan, during the quarterly periods from 2008 to 2022. The study reviews the literature on the theoretical framework around the impact of real GDP and fluctuations of the real exchange rate on the current account status. This study describes the performance of the current account during the study period and applies the appropriate econometric tests to an economic model. The main results show a significant, positive effect of real GDP growth in improving the status and sustainability of the current account; output growth indicates an increase in production leading to higher levels of exports and lower levels of imports. The results did not show a significant effect of the real exchange rate on the current account position, which is largely attributed to the stabilization policy of exchange rate pursued by the Central Bank of Jordan since October 1995

AI Techniques for Combating Electronic Crimes and Enhancing Cybersecurity: Kuwaits Security Services as a Model

The research aimed to examine the security agencies use of artificial intelligence (AI) techniques in combating cybercrime and its reflection on enhancing cyber security. The study used the survey method in its descriptive and analytical levels. The interview tool was used to collect information from the research sample, the size of which was 12 items. The research found a set of results where the respondents declared the presence of an anti- cybercrime team comprised of specialists in police sciences, engineering, information systems, and network engineering. They added that using AI enables specialists in security agencies to benefit from its enormous potential in analyzing data, tracking cybercrime perpetrators through social networking sites, managing, and using information, following up on complaints, publications, and other messages, preparing security reports, and submitting them to the competent authorities, completing many general daily tasks and monitoring all the information that may affect the public opinion. The study recommended setting up industries related to AI technology to produce smart knowledge and unifying Arab capabilities in the information technology and communication field to protect Arab national security by combating foreign technical intrusions and virtual hegemony, representing the most advanced form of futuristic weapons

Data backup and recovery with a minimum replica plan in a multi-cloud environment

Cloud computing has become a desirable choice to store and share large amounts of data among several users. The two main concerns with cloud storage are data recovery and cost of storage. This article discusses the issue of data recovery in case of a disaster in a multi-cloud environment. This research proposes a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensuring high data reliability during disasters. This approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) aims at reducing the number of replicationsin the cloud without compromising the data reliability. PDRPMR means preventive action checking of the availability of replicas and monitoring of denial ofservice attacksto maintain data reliability. Several experiments were conducted to evaluate the effectiveness of PDRPMR and the results demonstrated that the storage space used one-third to two-thirds compared to typical 3-replicasreplication strategies

Kuwaiti EFL Students’ Perceptions of the Effectiveness of the Remedial English Course 099 at the College of Technological Studies

The study aims to evaluate the English remedial course 099 taught in the College of Technological Studies (PAAET) as part of the English program which disseminates English Language Skills to EFL students studying at this college. This study is expected to provide sufficient information to policymakers and educators involved with this program at all levels, with the intention to help them evaluate this course and make useful decisions to improve English Language Teaching in order to combat the deficiency in the English language suffered by college students in Kuwait. A number of 155 students participated in a questionnaire of 15 statements divided into four areas: reading, grammar, writing, and speaking skills. The findings of the study showed that most EFL students benefited from the English course 099, and their language skills were improved. However, there were some drawbacks and weaknesses of the program in terms of learners’ assessments and follow up. The significance of the study arises from the fact that it would enable decision-makers and course evaluators to pinpoint the strengths and weaknesses of the course and hence find ways to improve it

Disaster recovery in single-cloud and multi-cloud environments: Issues and challenges

Information Technology (IT) data services provided by cloud providers (CPs) face significant challenges in maintaining services and their continuity during a disaster. The primary concern for data recovery (DR) in the cloud is finding ways to ensure that the process of data backup and recovery is effective in providing high data availability, flexibility, and reliability at a reasonable cost. Numerous data backup solutions have been designed for a single-cloud architecture; however, making a single copy of data may not be sufficient because damage to data may cause irrecoverable loss during a disaster. Other solutions have involved multiple replications on more than one remote cloud provider (Multi-Cloud). Most suggested solutions have proposed obtaining a high level of reliability by producing at least three replicas of the data and either storing all replicas at a single location or distributing them over numerous remote locations. The drawbacks to this approach are high costs, large storage space consumption and (especially in the case of data-intensive cloud-based applications) increased network traffic. In this paper, we discuss the issues raised by DR for both Single-Cloud and MultiCloud environments. We also examine previous studies concerning cloud-based DR to highlight issues that researchers of cloud-based DR have considered to be most important