Bacterial agents and sensitivity pattern of neonatal conjuctivitis in Aminu Kano Teaching Hospital

Introduction: In Africa alone, between 1000 – 4000 children are blinded annually by conjunctivitis. In view of the changing aetiological agentsdocumented in other parts of the world and evolving resistance of infective agents to therapeutic agents, the present study was designed to define the bacterial agents, their antibiotic sensitivity pattern seen in AKTH, Kano,Nigeria.Patients and methods: This was a cross sectional prospective study done over a period of 3 months. Consecutive neonates who satisfied the inclusion criteria were recruited until the sample size of 175 was obtained. samples were transported to the laboratory within one hour. Gram stainingand antibiotic sensitivity were determined using standard technique.Results: The mean age at presentation was 5.7 ± 4.6days. Results ofthe eye swabs showed that 97 (55.4%) were bacteriologically positive while 78 (44.6%) yielded no growth. Staphylococcus aureus was the most frequently isolated organism and was most sensitive to ceftriaxone, (73.1%). Escherichia coli was most sensitive to gentamicin (78.3%). Pseudomonas aeruginosa and Neisseria gonorrhea showed 100% sensitivity to ceftazidime and ceftriaxone respectively.Conclusion: Staphylococcus aureus is the commonest bacterial agent responsible for neonatal conjunctivitis. Staphylococcus aureus was most sensitive to ceftriaxone.Key words: Conjuctivitis, Neonates, Bacteria, Sensitivity

An Investigation of Users’ Acceptance and Satisfaction of E-Banking System as a Panacea towards a Cashless Economy in Nigeria

The benefits of e-banking have been established as being numerous and its success has been argued by many researchers to depend partly on the quality of the banking services but more especially on customer preferences and satisfaction. Surprisingly, as numerous as these e-banking benefits are, very long queues could still be seen in many Nigerian banks for the consumption of the traditional banking services of fund transfer, cash deposits and cash withdrawals. However, to prove the success of e- banking in Nigeria, users’ acceptance and satisfaction of the system need to be validated. Many research works had been conducted using the Technology Acceptance Model (TAM), an information system theory that models how users come to accept and use a technology, to pzredict and explain users’ acceptance of e-banking. TAM poses two theoretical constructs; perceived usefulness (PU) and perceived ease of use (PEOU) as fundamental determinants of user’s acceptance of an information system. This research work examines the factors that may influence users’ acceptance and satisfaction of e- banking in Nigeria by adding the impact of perceived credibility (PC) and trust to the TAM constructs (PU and PEOU) with four other external variables (convenience, quality of technology, service quality and system accessibility) in extending its validity on examining user’s acceptance and satisfaction of e-banking system in Nigeria as a panacea towards operating a cashless economy. The result of the hypothesis testing using Pearson chi square is consistent with previous studies which showed that there is a significant relationship in the predicted direction on intention to use information system (IS)

MAXIMUM PHISH BAIT: TOWARDS FEATURE BASED DETECTION OF PHISING USING MAXIMUM ENTROPY CLASSIFICATION TECHNIQUE

Several antiphishing methods have been employed with the primary task of automatically apprehending and ruling out or preventing phishing e-mail from users’ mail stream. Phishing attacks pose great threat to internet users and the extent can be enormous if unchecked. Two major category techniques that have been shown to be useful for classifying e-mail messages automatically include the rule based method which classifies email by using a set of heuristic rules and the statistical based approach which model e-mails statistically usually under a machine learning framework. The statistical based methods have been found in literature to outperform the rule based method. This study proposes the use of the Maximum Entropy Model, a generative model and show how it can be used in antiphishing tasks. The model based feature proposed by Bergholz et al (2008) will also be adopted. This has been found to outperform basic features proposed in previous studies. An experimental comparison of our approach with other generative and non-generative classifiers is also proposed. This approach is expected to perform comparably better than others method especially in the elimination of false positives

A maximum entropy classification scheme for phishing detection using parsimonious features

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM)

A Computation Investigation of the Impact of Convex Hull subtour on the Nearest Neighbour Heuristic

This study investigated the computational effect of a Convex Hull subtour on the Nearest Neighbour Heuristic. Convex hull subtour has been shown to theoretically degrade the worst-case performances of some insertion heuristics from twice optimal to thrice optimal, although other empirical studies have shown that the introduction of the convex hull as a subtour is expected to minimize the occurrences of outliers, thereby potentially improving the solution quality. This study was therefore conceived to investigate the empirical effect of a convex-hull-based initial tour on the Nearest Neighbour Heuristic vis-a-vis the traditional use of a single node as the initial tour. The resulting hybrid Convex Hull-Nearest Neighbour Heuristic (CH-NN) was used to solve the Travelling Salesman Problem. The technique was experimented using publicly available testbeds from TSPLIB. The performance of CH-NN vis-à-vis that of the traditional Nearest Neighbour solution showed empirically that Convex Hull can potentially improve the solution quality of tour construction techniques

Performance Evaluation of Convex Hull Node-Based Heuristics for Solving the Travelling Salesman Problem

This experimental study investigated the effect of Convex Hull on Nodebased Heuristics. This was motivated by the assertion in the literature that starting some insertion tours with a convex hull theoretically degrades their worst case from twice optimal to thrice optimal. The Node-based techniques considered were Nearest Neighbour Heuristic (NNH) and Nearest Insertion Heuristic (NIH). The derived heuristics with Convex Hull were referred to in this study as Convex Hull Nearest Neighbour (CHNN) and Convex Hull Nearest Insertion (CHNI), respectively. The techniques were experimented on eleven benchmark instances from TSPLIB using Python Programming Language. Experimental results showed that the performances of both the Nearest Neighbour and Nearest Insertion were enhanced in terms of Computational speed and solution qualit

mPD-APP: a mobile-enabled plant diseases diagnosis application using convolutional neural network toward the attainment of a food secure world

The devastating effect of plant disease infestation on crop production poses a significant threat to the attainment of the United Nations' Sustainable Development Goal 2 (SDG2) of food security, especially in Sub-Saharan Africa. This has been further exacerbated by the lack of effective and accessible plant disease detection technologies. Farmers' inability to quickly and accurately diagnose plant diseases leads to crop destruction and reduced productivity. The diverse range of existing plant diseases further complicates detection for farmers without the right technologies, hindering efforts to combat food insecurity in the region. This study presents a web-based plant diagnosis application, referred to as mobile-enabled Plant Diagnosis-Application (mPD-App). First, a publicly available image dataset, containing a diverse range of plant diseases, was acquired from Kaggle for the purpose of training the detection system. The image dataset was, then, made to undergo the preprocessing stage which included processes such as image-to-array conversion, image reshaping, and data augmentation. The training phase leverages the vast computational ability of the convolutional neural network (CNN) to effectively classify image datasets. The CNN model architecture featured six convolutional layers (including the fully connected layer) with phases, such as normalization layer, rectified linear unit (RELU), max pooling layer, and dropout layer. The training process was carefully managed to prevent underfitting and overfitting of the model, ensuring accurate predictions. The mPD-App demonstrated excellent performance in diagnosing plant diseases, achieving an overall accuracy of 93.91%. The model was able to classify 14 different types of plant diseases with high precision and recall values. The ROC curve showed a promising area under the curve (AUC) value of 0.946, indicating the model's reliability in detecting diseases. The web-based mPD-App offers a valuable tool for farmers and agricultural stakeholders in Sub-Saharan Africa, to detect and diagnose plant diseases effectively and efficiently. To further improve the application's performance, ongoing efforts should focus on expanding the dataset and refining the model's architecture. Agricultural authorities and policymakers should consider promoting and integrating such technologies into existing agricultural extension services to maximize their impact and benefit the farming community

A Maximum Entropy Classification Scheme for Phishing Detection using Parsimonous Features

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM

Two cultures, one identity: formulations of Australian Isma'ili Muslim identity

The Shi'a Imami Nizari Isma'ili Muslims have often been considered the "poster child" for pluralistic integration (Cayo 2008). This ethos has been inculcated within members of the community, with its adherents seeing themselves as a diverse and multi-ethnic collective. Nevertheless, despite this purported pluralism, social research on the Isma'ilis has primarily focused on the diasporic and post-diasporic migrant communities of South Asian descent, the 'first and second-generation immigrants,' in the Euro-American context (Mukadam and Mawani 2006, 2009; Nanji 1983, 1986). The experiences of co-religionists in other contexts have often been neglected. This study examines how members of the self-described geographically and socially isolated Isma'ili community in Australia construct their identity vis-à-vis the larger, global, Isma'ili community, and how they have responded to the potential of identity threat given the arrival of another group of Isma’ilis with a differing migratory history integrating into the extant community. Using the approach of identity process theory, this study examines how salient features of identity are constructed amongst the Australian Isma'ilis, how religion and identity take on multiple meanings within the Australian Isma'ili context, and, finally, sheds light on the self-sufficiency of this community despite geographic and social isolation