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

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

Enhanced Dataset of Digitized Screen-film Mammograms of African Descent

This dataset presents the enhanced version of digitized Screen-film Mammograms of African Descent. It contains mamographic images of 78 African cancer patient

A Neuro-Fuzzy Based System for the Classification of Cells as Cancerous or Non-Cancerous

Objectives: In this study, we developed a neuro-fuzzy based system for classification of cancerous and non-cancerous lung cells. Methods: Images were pre-processed using median filter algorithm, segmented using marker-controlled watershed algorithm, and were extracted using gray-level co-occurrence matrix. A hybridized diagnosis system that made use of neural network and fuzzy logic for classification of lung cells into cancerous and non-cancerous cells is modelled. Computed tomography (CT) scan image dataset of the lung was downloaded from The Cancer Imaging Archive dataset. Neural network performed the training and classification of the lung cells with back-propagation algorithm, while the cancerous cells were passed into fuzzy inference system to determine the lung cancer stage. Results: Our system was able to successfully classify the imported CT scan images into normal or abnormal with considerably high accuracy of 70% and precision of 89%. This system can support physicians in decision making when diagnosing cance

Triple Watermarking Scheme for Digital Images

Digital watermarking of text, image, and video data has become an indispensable strategy for the authentication, validation, and ultimate protection of digital content. This study aims to develop a resilient, imperceptible watermarking scheme for digital images. A triple transform domain watermarking scheme based on stationary wavelet transform (SWT), discrete cosine transform (DCT), and singular value decomposition (SVD) is presented as a comprehensive, adaptable, and resilient solution for safeguarding digital content in a dynamic digital environment. The proposed scheme leverages the unique strengths of SWT, DCT, and SVD. SWT was used to decompose the image into various frequency bands, whereas DCT was applied to these high-frequency components, enhancing the security of the watermark. SVD was then used to decompose the image matrix into singular values, providing further robustness. The watermarking algorithm was designed to be both imperceptible and robust against various attacks. The imperceptibility and robustness of the scheme were evaluated on three publicly available datasets using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), and bit error ratio (BER). The results indicate that the SWT-DCT-SVD watermarking scheme is both robust and imperceptible and compares relatively well with state-of-the-art schemes

Development of an Image Encryption Algorithm using Latin Square Matrix and Logistics Map

The goal of this study was to develop a robust image cryptographic scheme based on Latin Square Matrix and Logistics Map, capable of effectively securing sensitive data. Logistics mapping is a comparatively strong chaos system which enciphers with an unpredictability that significantly reduces the chance of deciphering. Additionally, the Latin square matrix stands out for its uniform histogram distribution, thereby bolstering its encryption's potency. The consequent integration of these algorithms in this study was therefore grounded in the scientific rationale of establishing a strong and resilient cypher technique. The study provides a new chaos-based method and extends the application of the probabilistic approach to the domain of symmetric key image encryption. Permutation and substitution approaches of image encryption were deployed to address the issue of images volume and differing sizes. The issue of misplaced pixel positions in the image was also adequately addressed, making it an effective method for image encryption. The hybrid technique was simulated on image data and evaluated to gauge its performance. Results showed that the algorithm was able to securely protect image data and the private information associated with them, while also making it very difficult for unauthorized users to decrypt the information. The average encryption time of 184(μs) on seven (7) images showed that it could to be deployed for real-time systems. The proposed method obtained an average entropy of 7.9398 with key space of 1.17x1077 and an average avalanche effect (%) of 49.9823 confirming the security and resilience of the developed method

LF-ViT: Development of a Virtual Reality Guided Tour Mobile App of Landmark University Teaching and Research Farm

In this work, we designed and developed a Virtual Reality guided tour mobile app for Landmark University farms, LF-ViT. We were motivated by the need to circumvent the problem of bio-security caused by incessant visit to the farm by visitors, tourists or customers. The guided tour was implemented using the storytelling technique. Other technical details of the design and implementation process are discusse