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

Formulation and in vitro evaluation of orodispersible tablets of fexofenadine hydrochloride

Purpose: To develop orodispersible tablets (ODTs) of fexofenadine hydrochloride using three different superdisintegrants in various ratios and to compare their disintegration properties.Methods: Direct compression technique was used for the preparation of ODTs. Mannitol and Avicel CE-15 (microcrystalline cellulose and guar gum) were used as direct compression diluents. The disintegration time of tablets using each polymer (superdisintegrant) was evaluated as well as othertablet properties including weight fluctuation, hardness, friability, wetting time and water absorption ratio.Results: Satisfactory values were obtained for all the evaluated parameters. As the polymer concentration increased, there was a decrease in disintegration time. A comparison of the three different polymers used revealed that CCM3 formulated with 12 % croscarmellose sodium and 14.66 % lactose had the least disintegration time of 32.33 ± 3.23 s. In vitro release studies showed that the maximum drug release of 94.38 ± 0.12 % in 25 min was obtained for ODT tablets containing croscarmellose sodium (CCM3).Conclusion: The orodispersible tablets had quick disintegrating property which was achieved using superdisintegrants. Thus, superdisintegrants improve the disintegration efficiency of orodispersible fexofenadine tablets at low concentrations, when compared to traditional disintegrants. Keywords: Croscarmellose sodium, Direct compression, Fexofenadine, Orodispersible tablet

A Contactless Sensor for Human Body Identification using RF Absorption Signatures

Indoor human detection and localization sensors are at the base of many automation and monitoring systems. This work presents an indoor tagless passive human body identification method. It uses a load-mode capacitive sensor to detect the differences in the conductive and dielectric properties of the human body due to differences in body constituency. The experimental results show that four male individuals with similar height but different body mass index (BMI) standing at 70 cm in front of a chest-level 16 cm x 16 cm sensor plate determine different capacitance-frequency characteristics over a 5 kHz-160 kHz range, which can be used to identify the person

Artificial intelligence and sustainable development goals nexus via four vantage points

Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fronts including Curricula, Frameworks, Projects, and Research papers. We then analyze these datasets to extract meaningful information for policymakers and researchers alike - shedding light on how AI is being used and can potentially be employed in the future to achieve the SDGs. To this end, we devised a methodology using keyword-matching and keyword-similarity to compute the relevance of the SDGs for a given document. SDG metadata and AI4SDG Projects (Oxford initiative on AI4SDGs) were used to validate our methodology. We find an imbalance of coverage with SDG 9 (Industry Innovation and Infrastructure) having the highest representation (with 50.3% of our data containing references to it) compared to SDGs 5, 6, 14, and 15, which have the lowest representation (5% of observed data). Findings from this study suggest that the development of AI technology is focused on improving the current economic growth, but it might neglect important societal and environmental issues. 2022 The AuthorsRV acknowledges the financial support of KTH Climate Action Centre, and the KTH Sustainability Office. SG acknowledges the funding provided by the German Federal Ministry for Education and Research (BMBF) for the project "digitainable". JQ acknowledges the financial support of Qatar National Research Fund (QNRF) (a member of Qatar Foundation) through the National Priorities Research Program (NPRP) grant #[13S-0206-200273]. Open Access funding provided by the Qatar National Library. The statements made herein are solely the responsibility of the authors.Scopu

Role of Flavonoids as Wound Healing Agent

Flavonoids are found as the most abundant bioactive compounds all around the world. It is found in a number of medicinal plants that are used as wound healing agents in traditional medicinal uses such as Buddleja globosa, Moringa oleifera, Lam, Butea monosperma, Parapiptadenia rigida and Ononis spinosa. Flavonoids nowadays are being used in different formulation and wound healing dressings. Inflammation, proliferation and reepithelialization are involved in wound healing. Most of the wound healing medicinal plants possess multiple flavonoids that act as synergistic effect or combined effect. This chapter briefly reviews the role of flavonoids as wound healing agent in traditional and modern medicine

Third ventricular tumors: A comprehensive literature review

Third ventricle tumors are uncommon and account for 0.6 - 0.9% of all the brain tumors. Tumors of the third ventricle are classified into primary tumors, such as colloid cysts, choroid plexus papillomas, and ependymomas, or secondary tumors, such as craniopharyngiomas, optic nerve gliomas, pineal tumors, and meningiomas. Third ventricular tumors are uncommon, and their treatment involves significant morbidity and mortality. The colloid cyst has a better surgical outcome and many approaches are available to achieve a complete cure. Choroid plexus papilloma is also a common tumor documented with its treatment majorly based on surgical resection. In addition to multiple treatment options for craniopharyngiomas, surgery is the most preferred treatment option. Ependymomas also have few treatment options, with surgical resection adopted as the first line of treatment

Image‐based malware classification using VGG19 network and spatial convolutional attention

In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state‐of‐the‐art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image‐based classification of 25 well‐known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image‐based malware detection with high performance, despite being simpler as compared to other available solutions

Eco-friendly incorporation of crumb rubber and waste bagasse ash in bituminous concrete mix

The consumption of waste materials in the construction sector is a sustainable approach that helps in reducing the environmental pollution and decreases the construction cost. The present research work emphasizes the mechanical properties of bituminous concrete mix prepared with crumb rubber (CR) and waste sugarcane bagasse ash (SCBA). For the preparation of bituminous concrete mix specimens with CR and SCBA, the effective bitumen content was determined using the Marshall Mix design method. A total of 15 bituminous concrete mix specimens with 4%, 4.5%, 5%, 5.5% and 6% of bitumen content were prepared, and the effective bitumen content turned out to be 4.7%. The effect of five different CR samples of 2%, 4%, 6%, 8% and 10% by weight of total mix and SCBA samples of 25%, 50%, 75% and 100% by weight of filler were investigated on the performance of bituminous concrete. A total of 180 samples with different percentages of CR and SCBA were tested for indirect tensile strength (ITS) and Marshall Stability, and the results were compared with conventional bituminous concrete mix. It was observed that the stability values rose with an increase in CR percentage up to 6%, while the flow values rose as the percentage of SCBA increased in the mix. Maximum ITS results were observed at 4% CR and 25% SCBA replacement levels. However, a decrease in stability and ITS result was observed as the percentages of CR and SCBA increased beyond 4% and 25%, respectively. We concluded that the optimum CR and SCBA content of 4% and 25%, respectively, can be effectively used as a sustainable alternative in bituminous concrete mix