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Email has continued to be an integral part of our lives and as a means for successful communication on the internet. The problem of spam mails occupying a huge amount of space and bandwidth, and the weaknesses of spam filtering techniques which includes misclassification of genuine emails as spam (false positives) are a growing challenge to the internet world. This research work proposed the use of a metaheuristic optimization algorithm, the whale optimization algorithm (WOA), for the selection of salient features in the email corpus and rotation forest algorithm for classifying emails as spam and non-spam. The entire datasets were used, and the evaluation of the rotation forest algorithm was done before and after feature selection with WOA. The results obtained showed that the rotation forest algorithm after feature selection with WOA was able to classify the emails into spam and non-spam with a performance accuracy of 99.9% and a low FP rate of 0.0019. This shows that the proposed method had produced a remarkable improvement as compared with some previous methods.

Abstract-Mobile technology, over the years, has improved tremendously in sophistication and functionality. Today, there are mobile phones, known as smartphones, that can perform virtually most functions associated with personal computers. This has translated to increase in the adoption of mobile technology. Consequently, there has been an increase in the number of attacks against and with the aid of this technology. Mobile phones will often contain data that are needed as evidence in a court of law. And, therefore, the need to be able to acquire and present this data in an admissible form cannot be overemphasized. This requires the right forensic tools. This is the focus of this study. We evaluated the ability of four forensic tools to extract data, with emphasis on deleted data, from Android phones. Our results show that AccessData FTK Imager and EnCase performed better than MOBILedit Forensic and Oxygen Forensic Suite at acquiring deleted data. The conclusion is that, finding a forensic tool or toolkit that is virtually applicable across all mobile device platforms and operating systems is currently infeasible.

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The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiencyinsolvingmanyoptimizationproblemsandreal-worldapplications.TheLCAhasalsoshowngreat potentialsinsolvingnon-deterministicpolynomialtime(NP-complete)problems.Thissurveypresentsabriefsynopsisof theLCA literaturesinpeer-reviewedjournals,conferencesandbookchapters.Theseresearcharticlesarethen categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies. Big Data and Data Mining Search technique 37 , resource allocation 38 , resource management 39 , etc. Mobile Computing and Sensor Networks Resource allocation 40 , energy conservation problem 41 , resource distribution 42 , search technique 43 , etc. Engineering Design Toy problem 44,45 , chaotic sequences 46 , Cognitive engineering design problem 47 , structural design problem 48 , etc. Networks and Distributed (i.e. parallel, grid and cloud) Systems Routing problem 49,50 , assignment problem 40,51 , resource allocation 52 and resource distribution 53 , etc. Graph Colouring Assignment problem 54,55 . Resource Constrained Projects Resource allocation 56,57 , resource distribution 58 and scheduling 59 . Machine Learning Learning of classification rules 60,61 , Learning the Structure of Bayesian Networks 62 , learning rules in a fuzzy system 63 and quadratic assignment problem 64 . University Timetable Resources allocation 65 and scheduling 66,67 . Subset Problems Multiple knapsack problem 68-70 , maximum independent set problem 71-73 , maximum clique problem 74,75 and redundancy allocation problem 76,77 , etc. Other Applications Open shop 78 and group shop scheduling 79 , permutation flow shop problem 80 , set covering problem 81 , 2D-HP Protein Folding 82 , etc.

Abstract-The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called junk emails are unsolicited messages that are almost identical and sent to multiple recipients randomly. In this study, a performance analysis is done on some classification algorithms including: Bayesian Logistic Regression, Hidden Naï ve Bayes, Radial Basis Function (RBF) Network, Voted Perceptron, Lazy Bayesian Rule, Logit Boost, Rotation Forest, NNge, Logistic Model Tree, REP Tree, Naï ve Bayes, Multilayer Perceptron, Random Tree and J48. The performance of the algorithms were measured in terms of Accuracy, Precision, Recall, FMeasure, Root Mean Squared Error, Receiver Operator Characteristics Area and Root Relative Squared Error using WEKA data mining tool. To have a balanced view on the classification algorithms' performance, no feature selection or performance boosting method was employed. The research showed that a number of classification algorithms exist that if properly explored through feature selection means will yield more accurate results for email classification. Rotation Forest is found to be the classifier that gives the best accuracy of 94.2%. Though none of the algorithms did not achieve 100% accuracy in sorting spam emails, Rotation Forest has shown a near degree to achieving most accurate result.

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