Food sales prediction model using machine learning techniques

Food sales prediction means how to obtain future results of sales of companies. The purpose of this step is to increase the profits of these companies by avoiding spoilage of food products and avoiding buying more quantities than the needs of these companies, which means the accumulation of these products in the warehouses without selling them. Stocked and expired products require a model that guesses the actual future need for these products. In this study, a model for food sales prediction using machine learning algorithms is proposed to achieve two objectives, first: make a comparison between two datasets, one dataset with a high correlation between its features, and another dataset has a low correlation between its features. The second objective is to use several machine learning algorithms for prediction and comparing between these algorithms to find the best three algorithms that give the best prediction. By using the most important metrics such as root mean square error (RMSE) and mean square error (MSE) found the best three algorithms by using the first dataset are support vector machines (SVMs), least absolute shrinkage and selection operator (LASSO), and bagging regressor) and the best three algorithms by using the second dataset are (gradient boosting, random forest regressor, and decision tree)

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately

Innovative Approaches To Nursing Administration Education; A Systematic Review Based Study

Background: Nursing administration education plays a crucial role in the development of skilled leaders in the ever-evolving healthcare industry. To meet the current challenges in healthcare, it is crucial to employ innovative pedagogical approaches. These approaches include the integration of virtual reality simulations, fostering interdisciplinary collaboration, utilizing real-world case studies, implementing telehealth platforms, and establishing mentorship programs. Addressing this need for forward-thinking nursing administrators is imperative. Aim: This study comprehensively examines the impact of these innovative strategies on nursing administration education. It assesses how their integration enhances decision-making, communication, strategic thinking, technological proficiency, and leadership skills among students. The goal is to illuminate the potential of these approaches in molding skilled healthcare leaders. Method: A mixed-methods approach is utilized. Qualitative interviews with nursing administration students exposed to innovative approaches provide insights. Thematic analysis is employed to extract meaningful patterns, capturing both subjective experiences and skill development outcomes. Results: Findings highlight the transformative potential of innovative approaches in nursing administration education. Virtual reality simulations enhance decision-making, interdisciplinary collaboration fosters effective communication and teamwork, real-world case studies cultivate strategic thinking, telehealth platforms enhance remote service proficiency, and mentorship programs empower leadership competencies. Conclusion: This study underscores the pivotal role of innovation in shaping adept nursing administrators. Integration of innovative approaches equips healthcare leaders with holistic perspectives, adaptable skills, and technological readiness. As healthcare systems evolve, these approaches offer promise for addressing challenges effectively. Innovative Contribution: By delving into cutting-edge nursing administration education, this study offers insights that reshape healthcare leadership. It bridges theory and practice, equipping future administrators to navigate the dynamic healthcare landscape

Nurses knowledge towards atrial fibrillation in cardiac care unit in babylon governorate

Background: Atrial fibrillation is an irregular, often very rapid heartbeat that can lead to clots and increase the risk of stroke, heart failure and other heart-related complications. Nurses' knowledge of patient management is critical in the cardiac care wards. Therefore, the study aimed to assess nurses knowledge towards atrial fibrillation and determine the differences in knowledge with regards socio-demographic variables. Methods: A descriptive cross-sectional study was conducted with an purposive sample of 200 nurses was selected through the use a non-probability sampling approach. This sample was distributed throughout three cardiac care unit according to the Babel Health Directorate, including (Margan Teaching hospital, Shahid AL-Mihrab center and Imam AL-Sadiq Hospital). The reliability of the questionnaire was achieved through a pilot study and then presented to experts to prove its validity. The total number of items included in the questionnaire was 40-items. The data was collected by using the self-report method and analyzed by the application of descriptive and inferential statistical data analysis approach. Results: The results of the study indicated that (55%) of the nurses exhibited a poor level of knowledge about atrial fibrillation.&nbsp

Viprof: Vertically integrated full-system performance profiler

In this paper, we present VIProf, a full-system, performance sampling system capable of extracting runtime behavior across an entire software stack. Our long-term goal is to employ VIProf profiles to guide online optimization of programs and their execution environments according to the dynamically changing execution behavior and resource availability. VIProf thus, must be transparent while producing accurate and useful performance profiles. We overview the design and implementation of VIProf and empirically evaluate the system using a popular software stack – one that includes a Linux operating system, a Java Virtual Machine, and a set of applications. This composition is commonly employed and important for highend systems such as application and web servers as well as Computational Grid services. We show that VIProf introduces little overhead and is able to capture accurate (function-level) full-system performance data that previously required multiple profiles and extensive, manual, and offline post-processing of profile data. 1 1

Efficient remote profiling for resource-constrained devices

The widespread use of ubiquitous, mobile, and continuously-connected computing agents has inspired software developers to change the way they test, debug, and optimize software. Users now play an active role in the software evolution cycle by dynamically providing valuable feedback about the execution of a program to developers. Software developers can use this information to isolate bugs in, maintain, and improve the performance of a wide-range of diverse and complex embedded device applications. The collection of such feedback poses a major challenge to systems researchers since it must be performed without degrading a user’s experience with, or consuming the severely restricted resources of the mobile device. At the same time, the resource constraints of embedded devices prohibit the use of extant software profiling solutions. To achieve efficient remote profiling of embedded devices, we couple two efficient hardware/software program monitoring techniques: Hybrid Profiling Support(HPS) and Phase-Aware Sampling. HPS efficiently inserts profiling instructions into an executing program using a novel extension to Dynamic Instruction Stream Editing(DISE). Phase-aware sampling exploits the recurring behavior of programs to identify key opportunities during execution in order to collect profile information (i.e. sample). Our prior work on phase-aware sampling required code duplication to toggl

Camel-Related Facial Injuries: A Seven-Year Retrospective Study

Facial injuries caused by camels can be associated with adverse long-term effects on patients’ quality of life. We aimed to investigate camel-related facial injuries in Al-Ain City, UAE, focusing on their incidence, types, mechanisms, anatomical distribution, and outcomes, to enhance preventive measures. We retrospectively collected data from all patients who were admitted to our hospital with camel-related facial injuries from January 2014 through January 2021. Thirty-six patients were included; all were males, with a mean (range) age of 31 (14–66) years, 29 (80.5%) were camel caregivers. The most common mechanisms of injury were falling while riding a camel and camel kicks. The head was the most commonly injured region in 52.7%. Twenty-three (63.8%) patients had facial bone fractures. The middle third of the face accounted for 71.4% of the bony fractures. The most performed surgical procedures in our patients were soft tissue laceration repair and open reduction with internal fixation of fractures (ORIF). Camel-related facial injuries affect young adult male camel caregivers working on camel farms. Orbital and maxillary bone fractures are the most predominant fractures requiring operative management. Legislation for compulsory helmet usage may reduce the incidence of these injuries and their serious consequences