Blood circulation depends critically on electrical activation, where any disturbance in the orderly pattern of the heart’s propagating wave of excitation can lead to arrhythmias. Diagnosis of arrhythmias using electrocardiograms (ECG) is widely used because they are a fast, inexpensive, and non-invasive tool. However, the randomness of arrhythmic events and the susceptibility of ECGs to noise leads to misdiagnosis of arrhythmias. In addition, manually diagnosing cardiac arrhythmias using ECG data is time-intensive and error-prone. With better training, deep learning (DL) could be a better alternative for fast and automatic classification. The present study introduces a novel deep learning architecture, specifically a one-dimensional convolutional neural network (1D-CNN), for the classification of cardiac arrhythmias. The model was trained and validated with real and noise-attenuated ECG signals from the MIT-BIH dataset. The main aim is to address the limitations of traditional electrocardiograms (ECG) in the diagnosis of arrhythmias, which can be affected by noise and randomness of events, leading to misdiagnosis and errors. To evaluate the model performance, the confusion matrix is used to calculate the model accuracy, precision, recall, f1 score, average and AUC-ROC. The experiment results demonstrate that the proposed model achieved outstanding performance, with 1.00 and 0.99 accuracies in the training and testing datasets, respectively, and can be a fast and automatic alternative for the diagnosis of arrhythmias.

In recent years, deep learning techniques are employed in the mammography processing field to reduce radiologists' costs. Existing breast mass classification systems are implemented using deep learning technologies such as a Convolutional Neural Network (CNN). CNN based systems have attained higher performance than the machine learning-based systems in the classification task of mammography images, but a few issues still exist. Some of these issues are; ignorance of semantic features, analysis limitation to the current patch of images, lost patches in less contrast mammography images, and ambiguity in segmentation. These issues lead to increased false information about patches of mammography image, computational cost, decisions based on current patches, and not recovering the variance of patches intensity. In turn, breast mass classification systems based on convolutional neural networks produced unsatisfactory classification accuracy. To resolve these issues and improve the accuracy of classification on low contrast images, we propose a novel Breast Mass Classification system named BMC. It has improved architecture based on a combination of k- mean clustering, Long Short-Term Memory network of Recurrent Neural Network (RNN), CNN, random forest, boosting techniques to classify the breast mass into benign, malignant, and normal. Further, the proposed BMC system is compared with existing classification systems using two publicly available datasets of mammographic images. Proposed BMC system achieves the sensitivity, specificity, F-measure, and accuracy for the DDSM dataset is 0.97%, 0.98%,0.97%, 0.96% and for the MIAS dataset is 0.97%, 0.97%,0.98%, and 0.95% respectively. Further Area Under Curve (AUC) rate of the proposed BMC system lies between 0.94% - 0.97% for DDSM and 0.94%-0.98% for the MIAS dataset. The BMC method worked comparably better than other mammography classification schemes that have previously been invented. Moreover, the Confidence interval statistical test is also employed to determine the classification accuracy of the BMC system using different configurations and neural network parameters.

Over the last few years, web phishing attacks have been constantly evolving causing customers to lose trust in e-commerce and online services. Various tools and systems based on a blacklist of phishing websites are applied to detect the phishing websites. Unfortunately, the fast evolution of technology has led to the born of more sophisticated methods when building websites to attract users. Thus, the latest and newly deployed phishing websites; for example, zero-day phishing websites, cannot be detected by using these blacklist-based approaches. Several recent research studies have been adopting machine learning techniques to identify phishing websites and utilizing them as an early alarm method to identify such threats. However, the important website features have been selected based on human experience or frequency analysis of website features in most of these approaches. In this paper, intelligent phishing website detection using particle swarm optimization-based feature weighting is proposed to enhance the detection of phishing websites. The proposed approach suggests utilizing particle swarm optimization (PSO) to weight various website features effectively to achieve higher accuracy when detecting phishing websites. In particular, the proposed PSO-based website feature weighting is used to differentiate between the various features in websites, based on how important they contribute towards recognizing the phishing from legitimate websites. The experimental results indicated that the proposed PSO-based feature weighting achieved outstanding improvements in terms of classification accuracy, true positive and negative rates, and false positive and negative rates of the machine learning models using only fewer websites features utilized in the detection of phishing websites.

Proto-oncogenes are the genes that have the potential to transform normal cells into cancer cells as a result of mutations. They usually contain encoding of proteins whose function is to inhibit cell differentiation, stimulate cell division, and prevent the death of cells. While the prognosis regarding proto-oncogene may occur at varying phases of cancer, the accuracy of the identification method is always questionable. The standard procedure for detecting these genes involves in-vitro experimentations but it proves to be very costly, time taking, and laborious. This problem is addressed by the use of computer-aided approaches established in studies encompassing methods in computational biology and bioinformatics. Early diagnosis of cancer is crucial for the full recovery of the patient. Proto-oncogene proteins are an important biomarker that helps identify the onset of a specific type of cancer. Keeping this in mind, this study proposes an efficient methodology for in-silico identification of proto-oncogenes. The predictor proposed in this study computes position and composition relevant statistical features incorporated into the pseudo-amino-acid composition (PseAAC) based on Chou's 5-step rules. Subsequently, the study finds that the use of a random forest classifier performs the most accurate prediction of proto-oncogene proteins. The method was validated using the 10 folds cross-validation, Jackknife testing, Self-Consistency, and Independent set testing, giving 95.44%, 97.17%, 99.8%, and 96.41% accurate results, respectively. These results imply that the proposed model can play a key role in the early prognosis of cancer and aid scientists in the discovery of mechanisms against cancer.

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.