Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing the control plane and decoupling it from the forwarding plane. Through a symmetric and centralized controller, SDN can prevent security breaches, but it can also bring in new threats and vulnerabilities. The central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this research, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with random forest classifier using the gain ratio feature selection evaluator. In the later phase, the second approach is combined with a deep neural network (DNN)-based intrusion detection system based on gated recurrent unit-long short-term memory (GRU-LSTM) where we used a suitable ANOVA F-Test and recursive feature elimination selection method to boost classifier output and achieve an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller

A comparative study between capillary and venous blood glucose levels of type 2 diabetes mellitus patients in intensive care units

Introduction Blood glucose estimation is required to be done rapidly or even on hourly basis for effective dosing of insulin in diabetic patients admitted in intensive care units (ICUs). Glucose test strip precision is usually considered clinically acceptable if the difference from the reference value (lab method) does not exceed 15%. To use glucometers in ICU settings the correlation between capillary blood glucose and venous blood glucose is essential. Materials and Methods This study was a cross sectional, descriptive study which was carried out in a large tertiary care teaching hospital in Pune, from 01 Nov 2015 to 31 Oct 2016. After excluding all the confounding factors, 65 patients were included in this study. The laboratory results and glucose strip results were tabulated in a master chart as sample 1(capillary/venous- time of admission), sample 2(capillary /venous 24 hrs), sample 3 (capillary /venous 48 hrs) and also segregated into less than 250 mg/dl and more than 250 mg/dl for determining the correlation and agreement between the capillary and venous blood glucose levels. Results A good correlation existed between the CBG and VBG of sample 1 (R2 =0.995, p [Med-Science 2018; 7(2.000): 342-6

Facial emotion recognition based on deep transfer learning approach

Facial expressions play a major role in the communication of emotions through nonverbal channels. In recent years, the topic of automatic facial expression recognition (FER) has become very popular. Researchers are looking at how it may be used in education, security surveillance, smart healthcare system, and to understand the behavior of a community or a person. As long as there are variations in images, such as diferent poses and lighting, accurate and robust FER remains a challenge using computer models. We developed an approach to automatically classifying facial expressions based on deep transfer learning. The approach was constructed with convolutional neural networks (CNN) and VGG19, which is a transfer learning model. To train the model, we employed contemporary deep learning techniques such as optimal learning rate fnder, fne-tuning, and data augmentation. On both the Extended Cohn-Kanade (CK+) and the Japanese Female Facial Expression (JAFFE) datasets, the proposed model achieved accuracy values of 94.8% and 93.7%, respectively. The developed system has already been tested on a vast database and can be used to assist online education systems, surveillance systems, and smart healthcare systems in their daily activities

Machine learning-based diagnosis of breast cancer utilizing feature optimization technique

Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react

An analysis of morphological and genetic diversity of mango fruit flies in Pakistan.

Fruit flies of genus Bactrocera are important insect pests of commercially cultivated mangos in Pakistan limiting its successful production in the country. Despite the economic risk, the genetic diversity and population dynamics of this pest have remained unexplored. This study aimed to morphologically identify Bactrocera species infesting Mango in major production areas of the country and to confirm the results with insect DNA barcode techniques. Infested mango fruits from the crop of 2022, were collected from 46 locations of 11major production districts of Punjab and Sindh provinces, and first-generation flies were obtained in the laboratory. All 10,653 first generation flies were morphologically identified as two species of Bactrocera; dorsalis and zonata showing geography-based relative abundance in the two provinces; Punjab and Sindh. Morphological identification was confirmed by mitochondrial cytochrome oxidase gene subunit I (mt-COI) based DNA barcoding. Genetic analysis of mtCOI gene region of 61 selected specimens by the presence of two definite clusters and reliable intraspecific distances validated the results of morphological identification. This study by morphological identification of a large number of fruit fly specimens from the fields across Pakistan validated by insect DNA barcode reports two species of Bactrocera infesting mango in the country

Global landscape of COVID-19 vaccination progress: insight from an exploratory data analysis

The next big step in combating the COVID-19 pandemic will be gaining widespread acceptance of a vaccination campaign for SARS-CoV-2. This study aims to report detailed Spatiotemporal analysis and result-oriented storytelling of the COVID-19 vaccination campaign across the globe. An exploratory data analysis (EDA) with interactive data visualization using various python libraries was conducted. The results show that, globally, with the rapid vaccine development and distribution, people from the different regions are also getting vaccinated and revealing their positive intent toward the COVID-19 vaccination. The outcomes of this exploration also established that mass vaccination campaigns in populated countries including Brazil, China, India, and the US reduced the number of daily COVID-19 deaths and confirmed cases. Overall, our findings contribute to current policy-relevant research by establishing a link between increasing immunization rates and lowering COVID-19’s rising curve

XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer

Lung cancer has been the leading cause of cancer-related deaths worldwide. Early detection and diagnosis of lung cancer can greatly improve the chances of survival for patients. Machine learning has been increasingly used in the medical sector for the detection of lung cancer, but the lack of interpretability of these models remains a significant challenge. Explainable machine learning (XML) is a new approach that aims to provide transparency and interpretability for machine learning models. The entire experiment has been performed in the lung cancer dataset obtained from Kaggle. The outcome of the predictive model with ROS (Random Oversampling) class balancing technique is used to comprehend the most relevant clinical features that contributed to the prediction of lung cancer using a machine learning explainable technique termed SHAP (SHapley Additive exPlanation). The results show the robustness of GBM's capacity to detect lung cancer, with 98.76% accuracy, 98.79% precision, 98.76% recall, 98.76% F-Measure, and 0.16% error rate, respectively. Finally, a mobile app is developed incorporating the best model to show the efficacy of our approach