Efficient transport and economic development: A transport survey analysis

Efficient transport and economic growth in a city or country are inter-related. The overall focus of the survey conducted was to find the travel habits of the residents in the city of Kuching (Malaysia), so as to weigh the prospects of economic development in future. The three objectives were to evaluate the efficiency of the local bus transportation system, to confirm on the usage of car as the preferred mode of transport, and to identify areas of improvement to the system as well as analyzing the need for an alternative mode(s) of transportation. The quantitative and qualitative analysis is done on data to find the relationships between various variables measured. Car has been confirmed to be the popular mode of transport across the age groups, across the income groups and across the professions, whereas the bus transport was really not significant. The study identified the important characteristics and priorities in the travel behavior of Kuching residents. The results of the study will be significant in the planning of new economic developments that encourages the use of public transportation in Kuching city

A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques

Phishing Web Page Detection using Optimised Machine Learning

Phishing is a type of social engineering attack that can affect any company or anyone. This paper explores the effect that different features and optimisation techniques have on the accuracy of intelligent phishing detection using machine learning algorithms. This paper explores both hyperparameter optimisation as well as feature selection optimisation. For hyperparameter tuning, both TPE (Tree-structured Parzen Estimator) and GA (Genetic Algorithm) were tested, with the best option being model dependent. For feature selection, GA, MFO (Moth Flame Optimisation) and PSO (Particle Swarm Optimisation) were used with PSO working best with a Random Forest model. This work used URL (Uniform Resource Locator), DOM (Document Object Model) structure, page rank and page information related features. This research found that the best combination was Random Forest using PSO for feature selection and TPE for hyperparameter optimisation, giving an accuracy of 99.33%

Impact on Student Learning From Traditional Continuous Assessment and an E-Assessment Proposal

Is learning for assessment an inevitable outcome of assessment for learning? We plan to investigate on this – by showing the effects of traditional continuous assessment modes we have implemented, and inviting student opinions on an e-assessment proposal which was virtually tested in a university setup. Student’s perceptions are checked in the case of traditional continuous assessment techniques vs. non continuous assessments and the effect tallied with the coursework marks obtained for two groups of students. Also classroom assessment vs. e-assessment options were posed to students who were exposed to the proposed e-assessment option and comments invited. In each case, the reasoning behind the choice of assessment and associated learning strategies are probed into. The e-assessment is proposed to implement continuous assessment especially for large classes and also as a medium to invoke a positive learning approach through the feedback mechanism available on the e-assessment tool. A simple algorithm is also proposed for essay e-assessment scoring

Synthesis, characterization and antimicrobial studies of novel Schiff bases and their complexes

538-544Novel Schiff bases, Furan-2-carboxylic acid pyridin-4-ylmethyleneamide , and Thiophene-2-carboxylic acid 1H-indol-2-ylmethyleneamide and their mononuclear Ni(II) and Cu(II) complexes have been synthesized and characterized by elemental analysis, molar conductance, UV-visible, FT-IR, 1H NMR and EPR spectroscopy. The complexes are non-electrolytes as evidenced from the molar conductance vaules. The ligands and their complexes have been tested for their antimicrobial activity against one gram positive bacteria, Bacillus subtilis, gram negative bacteria, Escherichia coli and fungi Candida albicans. It is found that metal complexes exhibited more activity than the free ligand

Synthesis, characterization and antimicrobial studies of novel schiff bases and their complexes

Novel Schiff bases, Furan-2-carboxylic acid pyridin-4-ylmethyleneamide (L1), and Thiophene-2-carboxylic acid 1H-indol-2-ylmethyleneamide (L­2) and their mononuclear Ni(II) and Cu(II) complexes have been synthesized and characterized by elemental analysis, molar conductance, UV-Vis, FT-IR, 1H NMR and EPR spectroscopy. The complexes are non-electrolytes. The ligands and their complexes have been tested for their antimicrobial activity against one Gram positive bacteria, Bacillus subtilis, one Gram negative bacteria, Escherichia coli and fungi Candida albicans. It was found that metal complexes exhibited more activity than the free ligand

Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10Gbps worked quite well

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This article aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. An extensive research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed

Optimal conditions for production of extracellular alkaline protease from a newly isolated Bacillus subtilis strain AKRS3

A new strain of alkaline protease producing Bacillus bacterium was isolated from an alkaline soil sample and was characterized. The bacterium was identified as Bacillus subtilis based on 16S rRNA sequencing and phylogenetic tree analysis. The organism was coded as B. subtilis AKRS3. The growth curve of the organism was elucidated by culturing in a basal medium in shake flasks under ambient shaking conditions. The optimal conditions for alkaline protease production were studied by following the monofactorial methodology. Enzyme yield was found to be optimum close to 24 hours, which coincided with the commencement of the stationary phase of the bacterium. A pH of 9.0, temperature of 37o C and agitation speed of 125 rpm were also identified to be optimum for enhanced enzyme yields in shake flasks. An inoculum of 4% v/v with 24 hrs of age was also identified to be ideal for inoculation. Xylose at 2 g/l concentration and beef extract at 1.5 g/l concentration were preferred by the organism for optimum enzyme productivity in the broth. The organism displayed an NaCl tolerance of upto 1 % as optimum. Fe2+ salts at 0.01 % concentration was the preferred metal component in the medium