Improving spam email classification accuracy using ensemble techniques: a stacking approach

Spam emails pose a substantial cybersecurity danger, necessitating accurate classification to reduce unwanted messages and mitigate risks. This study focuses on enhancing spam email classification accuracy using stacking ensemble machine learning techniques.We trained and tested five classifiers: logistic regression, decision tree, K-nearest neighbors (KNN), Gaussian naive Bayes and AdaBoost. To address overfitting, two distinct datasets of spam emails were aggregated and balanced. Evaluating individual classifiers based on recall, precision and F1 score metrics revealed AdaBoost as the top performer. Considering evolving spam technology and new message types challenging traditional approaches, we propose a stacking method. By combining predictions from multiple base models, the stacking method aims to improve classification accuracy. The results demonstrate superior performance of the stacking method with the highest accuracy (98.8%), recall (98.8%) and F1 score (98.9%) among tested methods. Additional experiments validated our approach by varying dataset sizes and testing different classifier combinations. Our study presents an innovative combination of classifiers that significantly improves accuracy, contributing to the growing body of research on stacking techniques. Moreover, we compare classifier performances using a unique combination of two datasets, highlighting the potential of ensemble techniques, specifically stacking, in enhancing spam email classification accuracy. The implications extend beyond spam classification systems, offering insights applicable to other classification tasks. Continued research on emerging spam techniques is vital to ensure long-term effectiveness

Performance Analysis of Cooperative V2V and V2I Communications under Correlated Fading

Cooperative vehicular networks will play a vital role in the coming years to implement various intelligent transportation-related applications. Both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications will be needed to reliably disseminate information in a vehicular network. In this regard, a roadside unit (RSU) equipped with multiple antennas can improve the network capacity. While the traditional approaches assume antennas to experience independent fading, we consider a more practical uplink scenario where antennas at the RSU experience correlated fading. In particular, we evaluate the packet error probability for two renowned antenna correlation models, i.e., constant correlation (CC) and exponential correlation (EC). We also consider intermediate cooperative vehicles for reliable communication between the source vehicle and the RSU. Here, we derive closed-form expressions for packet error probability which help quantify the performance variations due to fading parameter, correlation coefficients and the number of intermediate helper vehicles. To evaluate the optimal transmit power in this network scenario, we formulate a Stackelberg game, wherein, the source vehicle is treated as a buyer and the helper vehicles are the sellers. The optimal solutions for the asking price and the transmit power are devised which maximize the utility functions of helper vehicles and the source vehicle, respectively. We verify our mathematical derivations by extensive simulations in MATLAB.Comment: Internet of Vehicles (IoV), Vehicular communication, Antenna correlation, Stackelberg game, Vehicle-to-infrastructure (V2I), Vehicle-to-vehicle (V2V), Game theory, Cooperative vehicular network

Efficacy of the Epidural Blood Patch for the Treatment of Post Epidural Puncture Headache

Objective: To evaluate the efficacy of the epidural blood patch for the treatment of post-Dural puncture headache (PDPH). Patients and Methods: This cross sectional study was conducted in the Department of Anesthesia and Intensive care, Nishtar Hospital Multan. Out of total 326 patients, 186 were male 140 were female. Patients having age 16 years and above, presented with PDPH started from previous 24 hours up to last six days were included in the study. Efficacy of treatment was measured on patients comment about relief from pain after PDPH. Chi-Square along with Fisher exact test was used to see effect modification. Results: Overall, there were 326 (100%) patients in this study, among them 57% (n=186) were males and 43% (n=140) were females. ASA-1 and ASA-2 noted as 73.3% and 26.7% respectively. The main outcome of this study was efficacy of treatment. It was observed that after 1st patch, efficacy was noted as good in 75.8% (n=247) patients, while after 2nd patch it was good in 97.5% (n=318) patients. There was significant difference between the efficacy of 1st and 2nd patch. (P value=0.000), according to Fisher exact test. Conclusion"Results of our study concluded that epidural blood patch (EDBP) is the better choice for treatment of epidural puncture headache (EDPH). If one time it is incompletely effective its 2nd patch can be considered

Experimental study on impact of high voltage power transmission lines on silicon photovoltaics using artificial neural network

The recent trend of renewable energy has positioned solar cells as an excellent choice for energy production in today’s world. However, the performance of silicon photovoltaic (PV) panels can be influenced by various environmental factors such as humidity, light, rusting, temperature fluctuations and rain, etc. This study aims to investigate the potential impact of high voltage power transmission lines (HVTL) on the performance of solar cells at different distances from two high voltage levels (220 and 500 KV). In fact, HVTLs generate electromagnetic (EM) waves which may affect the power production and photocurrent density of solar cells. To analyze this impact, a real-time experimental setup of PV panel is developed (using both monocrystalline and polycrystalline solar cells), located in the vicinity of 220 and 500 KV HVTLs. In order to conduct this study systematically, the impact of HVTL on solar panel is being measured by varying the distance between the HVTL and the solar panels. However, it is important to understand that the obtained experimental values alone are insufficient for comprehensive verification under various conditions. To address this limitation, an Artificial Neural Network (ANN) is employed to generate HVTL impact curves for PV panels (particularly of voltage and current values) which are impractical to obtain experimentally. The inclusion of ANN approach enhances the understanding of the HVTL impact on solar cell performance across a wide range of conditions. Overall, this work presents the impact study of HVTL on two different types of solar cells at different distances from HVTL for two HV levels (i.e., 220 and 500 KV) and the comparison study of HVTL impact on both monocrystalline and polycrystalline solar cells

Learning-based Resource Allocation for Backscatter-aided Vehicular Networks

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework

Mortality of emergency abdominal surgery in high-, middle- and low-income countries

Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low- or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI). Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression. Results: Data were obtained for 10 745 patients from 357 centres in 58 countries; 6538 were from high-, 2889 from middle- and 1318 from low-HDI settings. The overall mortality rate was 1⋅6 per cent at 24 h (high 1⋅1 per cent, middle 1⋅9 per cent, low 3⋅4 per cent; P < 0⋅001), increasing to 5⋅4 per cent by 30 days (high 4⋅5 per cent, middle 6⋅0 per cent, low 8⋅6 per cent; P < 0⋅001). Of the 578 patients who died, 404 (69⋅9 per cent) did so between 24 h and 30 days following surgery (high 74⋅2 per cent, middle 68⋅8 per cent, low 60⋅5 per cent). After adjustment, 30-day mortality remained higher in middle-income (odds ratio (OR) 2⋅78, 95 per cent c.i. 1⋅84 to 4⋅20) and low-income (OR 2⋅97, 1⋅84 to 4⋅81) countries. Surgical safety checklist use was less frequent in low- and middle-income countries, but when used was associated with reduced mortality at 30 days. Conclusion: Mortality is three times higher in low- compared with high-HDI countries even when adjusted for prognostic factors. Patient safety factors may have an important role. Registration number: NCT02179112 (http://www.clinicaltrials.gov)

Performance Analysis of Cooperative V2V and V2I Communications Under Correlated Fading

Cooperative vehicular networks will play a vital role in the coming years to implement various intelligent transportation related applications. Both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications will be needed to reliably disseminate information in a vehicular network. In this regard, a roadside unit (RSU) equipped with multiple antennas can improve the network capacity. While the traditional approaches assume antennas to experience independent fading, we consider a more practical uplink scenario where antennas at the RSU experience correlated fading. In particular, we evaluate the packet error probability for two renowned antenna correlation models, i.e., constant correlation (CC) and exponential correlation (EC). We also consider intermediate cooperative vehicles for reliable communication between the source vehicle and the RSU. Here, we derive closed-form expressions for packet error probability, which help to quantify the performance variations due to fading parameter, correlation coefficients, and the number of intermediate helper vehicles. To evaluate the optimal transmit power in this network scenario, we formulate a Stackelberg game, wherein, the source vehicle is treated as a buyer and the helper vehicles are the sellers. The optimal solutions for the asking price and the transmit power are devised which maximize the utility functions of helper vehicles and the source vehicle, respectively. We verify our mathematical derivations by extensive simulations in MATLAB.peerReviewe