Diabetes is an acute disease that happens when the pancreas cannot produce enough insulin. It can be fatal if undiagnosed and untreated. If diabetes is revealed early enough, it is possible, with adequate treatment, to live a healthy life. Recently, researchers have applied artificial intelligence techniques to the forecasting of diabetes. As a result, a new SMOTE-based deep LSTM system was developed to detect diabetes early. This strategy handles class imbalance in the diabetes dataset, and its prediction accuracy is measured. This article details investigations of CNN, CNN-LSTM, ConvLSTM, and deep 1D-convolutional neural network (DCNN) techniques and proposed a SMOTE-based deep LSTM method for diabetes prediction. Furthermore, the suggested model is analyzed towards machine-learning, and deep-learning approaches. The proposed model’s accuracy was measured against the diabetes dataset and the proposed method achieved the highest prediction accuracy of 99.64%. These results suggest that, based on classification accuracy, this method outperforms other methods. The recommendation is to use this classifier for diabetic patients’ clinical analysis.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

Enterprise resource planning (ERP) systems have a major impact on the functioning of organizations and the development of business strategy. However, one of the main reasons that cause failure in ERP implementations to achieve the expected benefits is that the system is not fully accepted by end users. User rejection of the system is the second reason after time and budget overrun, while the fourth barrier to ERP post-implementation. Most studies have focused on ERP adoption and installation while neglecting post-implementation evaluation, which omits insights into the priority of ERP systems and CSFs from the stance of ERP users. Therefore, this study identified factors that led to user acceptance of the use of ERP systems at both implementation and post-implementation stages (after installation). In addition, this study assessed the interrelationship between the factors and the most influential factors toward user acceptance. A survey was conducted among pioneers of the food industry in Saudi Arabia, which included 144 ERP system users from assembly and manufacturing, accounts, human resources, warehouse, and sales departments. The descriptive-analytical approach was deployed in this study. As a result, project management, top management support, and user training had significant impacts on the efficacy of ERP system implementation. On the contrary, support for technological changes in new software and hardware, managing changes in systems, procedures, and work steps already in place within the organization, as well as user interfaces and custom code, displayed a direct impact on user acceptance of ERP systems post-implementation. This study is the first research that provides a rating of CSFs from the perspective of its users in Saudi Arabia. It also enables decision makers of food industries to better assess the project risks, implement risk-mitigation methods, create appropriate intervention techniques to discover the strengths and limitations of the ERP users, and value the “best of fit” solutions over “best practice” solutions when determining the most appropriate option for food industries.

Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

Background: In recent years, microarray datasets have been used to store information about human genes and methods used to express the genes in order to successfully diagnose cancer disease in the early stages. However, most of the microarray datasets typically contain thousands of redundant, irrelevant, and noisy genes, which raises a great challenge for effectively applying the machine learning algorithms to these high-dimensional microarray datasets. Methods: To address this challenge, this paper introduces a proposed hybrid filter and differential evolution-based feature selection to choose only the most influential genes or features of high-dimensional microarray datasets to improve cancer diagnoses and classification. The proposed approach is a two-phase hybrid feature selection model constructed using selecting the top-ranked features by some popular filter feature selection methods and then further identifying the most optimal features conducted by differential evolution (DE) optimization. Accordingly, some popular machine learning algorithms are trained using the final training microarray datasets with only the best features in order to produce outstanding cancer classification results. Four high-dimensional cancerous microarray datasets were used in this study to evaluate the proposed method, which are Breast, Lung, Central Nervous System (CNS), and Brain cancer datasets. Results: The experimental results demonstrate that the classification accuracy results achieved by the proposed hybrid filter-DE over filter methods increased to 100%, 100%, 93%, and 98% on Brain, CNS, Breast and Lung, respectively. Furthermore, applying the suggested DE-based feature selection contributed to removing around 50% of the features selected by using the filter methods for these four cancerous microarray datasets. The average improvement percentages of accuracy achieved by the proposed methods were up to 42.47%, 57.45%, 16.28% and 43.57% compared to the previous works that are 41.43%, 53.66%, 17.53%, 61.70% on Brain, CNS, Lung and Breast datasets, respectively. Conclusions: Compared to the previous works, the proposed methods accomplished better improvement percentages on Brain and CNS datasets, comparable improvement percentages on Lung dataset, and less improvement percentages on Breast dataset.

Brain stroke (BS) imposes a substantial burden on healthcare systems due to the long-term care and high expenditure. Earlier detection and intervention can reduce the impact of BS. Magnetic resonance imaging (MRI) is commonly applied for BS detection. Deep learning techniques can employ MRI images to identify the BS risks in the initial stages. This study developed a BS detection model using an ensemble learning approach that combines the predictions of the base models. A MobileNet V3 model backbone was used to extract the intricate patterns of BS from MRI images. LightGBM and CatBoost models were used as base models to predict BS using the extracted features. In addition, the random forest model was used to integrate the predictions of base models to identify BS. The proposed model was generalized on a public MRI dataset that covers 2888 clinical MRI images. The experimental outcomes showed the effectiveness of the suggested BS detection model. The proposed model has obtained an accuracy of 98.7%, an area under the receiver operating characteristic score of 0.95, and an area under the precision–recall curve of 0.92. The recommended model is believed to be deployed in real-time healthcare settings to assist radiologists and clinicians in making effective decisions.