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

Lightweight Deep Learning Framework for Speech Emotion Recognition

Speech Emotion Recognition (SER) system, which analyzes human utterances to determine a speaker’s emotion, has a growing impact on how people and machines interact. Recent growth in human-computer interaction and computational intelligence has drawn the attention of many researchers in Artificial Intelligence (AI) to deep learning because of its wider applicability to several fields, including computer vision, natural language processing, and affective computing, among others. Deep learning models do not need any form of manually created features because they can automatically extract the prospective features from the input data. Deep learning models, however, call for a lot of resources, high processing power, and hyper-parameter tuning, making them unsuitable for lightweight devices. In this study, we focused on developing an efficient lightweight model for speech emotion recognition with optimized parameters without compromising performance. Our proposed model integrates Random Forest and Multi-layer Perceptron(MLP) classifiers into the VGGNet framework for efficient speech emotion recognition. The proposed model was evaluated against other deep learning based methods (InceptionV3, ResNet, MobileNetV2, DenseNet) and it yielded low computational complexity with optimum performance. The experiment was carried out on three datasets of TESS, EMODB, and RAVDESS, and Mel Frequency Cepstral Coefficient(MFCC) features were extracted with 6–8 variants of emotions namely, Sad, Angry, Happy, Surprise, Neutral, Disgust, Fear, and Calm. Our model demonstrated high performance of 100%, 96%, and 86.25% accuracy on TESS, EMODB, and RAVDESS datasets respectively. This revealed that the proposed lightweight model achieved higher accuracy of recognition compared to the recent state-of-the-art model found in the literature

An automated mammogram classification system using modified support vector machine

Purpose: Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer diagnosis process; hence this study. Methodology: The inputs to the proposed model are raw mammograms downloaded from the Mammographic Image Analysis Society database. Prior to the classification, the raw mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract fifteen textural features from the mammograms at four different angular directions: θ={0°, 45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector machine was used to classify the mammograms as normal, benign and malignant. Results: All of the 37 normal images used as test data were classified as normal (no false positive) and all 41 abnormal images were correctly classified to be abnormal (no false negative), meaning that the sensitivity and specificity of the model in detecting abnormality is 100%. After the detection of abnormality, the system further classified the abnormality on the mammograms to be either “benign” or “malignant”. Out of 23 benign images, 21 were truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be malignant. From these findings, the sensitivity, specificity, positive predictive value, and negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively. Conclusion: This article has further affirmed the prowess of automated CADx systems as a viable tool that could facilitate breast cancer diagnosis by radiologists

Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis

Abstract Classification and analysis of high-resolution satellite images using conventional techniques have been limited. This is due to the complex characteristics of the imagery. These images are characterized by features such as spectral signatures, complex texture and shape, spatial relationships and temporal changes. In this research, we present the performance evaluation and analysis of deep learning approaches based on Convolutional Neural Networks and vision transformer towards achieving efficient classification of remote sensing satellite images. The CNN-based models explored include ResNet, DenseNet, EfficientNet, VGG and InceptionV3. The models were evaluated on three publicly available EuroSAT, UCMerced-LandUse and NWPU-RESISC45 datasets containing categories of images. The models achieve promising results in accuracy, recall, precision and F1-score. This performance demonstrates the feasibility of Deep Learning approaches in learning the complex and in-homogeneous features of the high-resolution remote sensing images

Deep Learning Approach for Medical Image Analysis

Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in the identification and detection of diseases. In this research, we explore the application of a deep learning approach in the analysis of some medical images. Traditional methods have been restricted due to the coarse and granulated appearance of most of these images. Recently, deep learning techniques have produced promising results in the segmentation of medical images for the diagnosis of diseases. This research experiments on medical images using a robust deep learning architecture based on the Fully Convolutional Network- (FCN-) UNET method for the segmentation of three samples of medical images such as skin lesion, retinal images, and brain Magnetic Resonance Imaging (MRI) images. The proposed method can efficiently identify the ROI on these images to assist in the diagnosis of diseases such as skin cancer, eye defects and diabetes, and brain tumor. This system was evaluated on publicly available databases such as the International Symposium on Biomedical Imaging (ISBI) skin lesion images, retina images, and brain tumor datasets with over 90% accuracy and dice coefficient

A Probabilistic-Based Deep Learning Model for Skin Lesion Segmentation

The analysis and detection of skin cancer diseases from skin lesion have always been tedious when done manually. The complex nature of skin lesion images is one of the key reasons for this. The skin lesion images contain noise and artifacts such as hairs, oil and bubbles, blood vessels, and skin lines. They also have variegated colors, low contrast, and irregular borders. Various computational approaches have been designed in the past for aiding in the detection and diagnosis of skin cancer diseases using skin lesion images. The existing techniques have been limited due to the interference of the aforementioned features of skin lesion. Recently, machine learning techniques, in particular the deep learning techniques have been used for the detection of skin cancer. However, they are still limited to the fuzzy and irregular borders of skin lesion images coupled with the low contrast that exists between the diseased lesion and healthy tissues. In this paper, we utilized a probabilistic model for the enhancement of a fully convolutional network-based deep learning system to analyze and segment skin lesion images. The probabilistic model employs an efficient mean-field approximate probabilistic inference approach with a fully connected conditional random field that utilizes a Gaussian kernel. The probabilistic model further performs a refinement of skin lesion borders. The whole framework is tested and evaluated on publicly available skin lesion image datasets of ISBI 2017 and PH2. The system achieved a better performance, having an accuracy of 98%

CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis

Machine learning technique such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyses and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in diagnosis and monitoring of some diseases. Diseases such as skin cancer, age�related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images

Electronic Medical Information Encryption Using Modified Blowfish Algorithm

Security and privacy of patients’ information remains a major issue of concern among health practitioners. Therefore, measures must be put in place to ensure that unauthorized individual do not have access to this information. However, the adoption of digital alternative of retrieving and documenting medical information has further opened it up to more attacks. This article pre�sents a modified blowfish algorithm for securing textual and graphical medical information. The F-function used in generating round sub-keys was strength�ened so as to produce a strong key that could resist differential attacks. Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI) of 98.85% and 33.65% revealed that the modified algorithm is sensitive to changes in its key and also resistive to differential attacks. Furthermore, the modified algorithm demonstrated a better encryption and decryption time than the existing blowfish algorithm