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WEKA and MOA are a free open-source software project specific for data mining and data stream mining, respectively. They are written in Java and developed at the University of Waikato, New Zealand. This research paper presents a comprehensive study of both consists of algorithms, evaluation, visualization, correlation between WEKA and MOA, workflow of implementation, and the classification accuracy.

The imbalance and irrelevant or redundant features often hinder the performance of accurate prediction models. This can aid healthcare professionals in the early detection and intervention of heart failure (HF). HF is a critical cardiovascular condition that poses a significant risk to human health, frequently resulting in high mortality rates if not diagnosed and managed early. In this paper, we propose an enhanced prediction approach for HF survivors based on a hybrid model (hybrid HF) that integrates feature selection techniques with the resampling technique—synthetic minority oversampling technique combined with edited nearest neighbors (SMOTEENN). SMOTEENN effectively addresses the class imbalance problem by synthesizing new minority class instances and improving noisy samples after selecting the most relevant features from the data, thereby enhancing the quality of the training data. Additionally, applied feature selection to identify the most relevant predictors, reducing model complexity and enhancing interpretability. Experimental results demonstrate that our hybrid HF model outperforms existing methods by uniquely integrating SMOTEENN with feature selection to achieve 0.9315 accuracy in predicting a heart patient’s survival, improving model accuracy by 8% compared to the baseline methods for the RF algorithm. Finally, improving classification, sensitivity, and overall model robustness compared to the baseline method.

It is essential to recognize extremely complicated hand movements with comparable shapes. Because the human community depends on gestures to convey their goals, it is imperative that a system be able to effectively recognize these gestures. Otherwise, it could do harm to the gesture recognition community. The purpose of the suggested approach is to highlight the most important phases in the hand gesture identification procedure which is the process of identifying and recognizing hand motions utilizing a multi-connect associative memory (MCAM) neural network and hand landmark points for hand detection. The problem with the similarity between the signs is because of the strong correlation between the movements of the fingers. In addition to the non-high accuracy between complex and very similar signs (e.g., A, S, and E) and the problem of response time in hand gesture recognition in real-time, thus, using the MCAM neural network improved efficiency in dealing with the correlation between similar patterns by taking similar vectors for each hand gesture pattern only once. The proposed system demonstrated promising outcomes in real-time with an accuracy for ASL is 96.28 and 99.77 for numbers. As well as the system's work in an uncontrolled environment, in addition to being an applicable system by converting the sign into its meaning as words and sentences, not just letters.

The experiment was carried out during agricultural season of 2023-2024, aimed of knowing the genetic evaluation of some cultivars of the faba bean ( Vicia faba L.), by effect of infection with Cucumber mosaic virus, cultivar (Syrian Local large), designed with a completely randomized block design with a split plot system and three replicates. The main plots included infection with the virus (a healthy plant free of the virus and a plant infected with it), the secondary plot included three cultivars, there were Furada, Equadollus2 and Leodotono. The following traits were studied: pod length, pods number, seeds number per pod, 100 seeds weight and seed yield. The results of the analysis of variance showed that the plants infected with the virus and the cultivars were significant at the probability level 1% for all the studied traits, while the interaction between plants infected with the virus and the cultivars was significant at the probability level 1% on pod length, seeds number and 100 seeds weight, significant at the probability level 1%, it did not significance on the pods number and seed yield. The treatments of healthy plants free of infection with the virus excelled in all the characteristics studied and in all cultivars, the cultivars differed significantly in all characteristics. Leodotono cultivar excelled in plants/m 2 , while the interaction among the treatments of healthy plants free of infection with the virus and the cultivar Leodotono was superior in all the traits studied.

PurposeThis study aims to assess the relationship between intellectual and social capital and financial statement fraud and money laundering of Iraqi firms before and after the emergence of the Islamic State of Iraq and Syria (ISIS). In other words, this paper seeks to answer the question of “whether the intellectual and social capital can contribute favourably to fraud in financial statements and money laundering or not.”Design/methodology/approachFor the study, the multivariate regression model is used for hypothesis testing. Research hypotheses have also been examined using a sample of 35 listed firms on the Iraqi Stock Exchange during 2012–2018, using the panel data technique-based multivariate regression pattern and fixed-effect model.FindingsThe results show a negative and significant relationship between social capital and intellectual capital, fraud in financial statements and money laundering. Besides, the results indicate a positive and significant effect of the interactive variable of ISIS on the relationship between social and intellectual capital and fraud in financial statements and money laundering.Originality/valueSince this paper is the first study on such a topic in the emergent markets, it provides helpful information for the users, analysts and legal institutions about intellectual capital and social capital that contributes significantly to fraud and money laundering of business units. Moreover, the study results help the development of science and knowledge in this field and fill the existing gap in the literature.

Due to the enormous growth in Internet usage and computer networks in recent years, new risks and challenges have arisen to network security. Among lots of security problems, network attack is a significant one. For instance, Distributed Denial of Service (DDoS) attacks have become appealing to intruders, and these have presented destructive threats to network infrastructures. Thus, Intrusion Detection Systems (IDSs) and Machine Learning (ML) approaches play a key role to detect such attacks effectively and efficiently. An essential part of several classification issues is the feature selection phase because to detect DDoS attacks depends on how one selects the minimal and relevant features in the network traffics. Unlike recent studies, in this work, a real-life SNMP-MIB dataset is used, as well as, we suggest an Ensemble-Weighted average approach (EnWaFS) that excludes the irrelevant features. An EnWaFS approach consists of two methods, first, Ensemble features by using a majority-voting method that mixed the outcomes of three feature selection approaches, second, a weighted average method that gives one weight for each feature and diminishes also the number of attributes. To evaluate an EnWaFS approach, we have performed four Machine Learning classifiers Neural network (Multi-Layer Perceptron), Vector Support Machine (SVM), Naïve Bayes (NB), and Random Forest (RF) utilizing the optimal set of attributes. The results reveal that our EnWaFS approach can efficiently decrease the number of attributes from 34 to 12 and also, from four ML classifiers were used, the RF technique achieved better performance due to the accuracy, sensitivity (recall), F-1 measure, precision, true-positive-rate, and the false-positive-rate which is decreased.

Corona virus disease (COVID-19) is an infectious caused by a new virus, the virus causes respiratory disease with symptoms such as coughing and fever, causes pneumonia in more severe cases. problem statement: The new Corona virus, or what has become known as \"COVID-19\", has spread to more than 79 countries outside China, where the Wuhan region is the epicenter of the virus. Until now researchers not discovered vaccine COVID-19. For prevalence observation there are countries has spread the virus significantly, others in an average and other less, with countries until now where the virus hasn't spread. proposed solution: This research based on studying the impact geographical latitude on knowing the spread of the COVID 19 in confirmed cases. result: To help scientists and researchers whom still to work on discovery the vaccine, taking importance of the zones the spread of the Covid-19. Using Dataset from Kaggle by classification algorithms linear regression data mining to extract a knowledge from data beneficial.

The zaragozic acids/squalestatins are a family of bicyclic tricarboxylic acids, isolated from a series of different fungi. Members of this class of highly oxygenated natural products show potent inhibitory activity against squalene synthase at the last stage of cholesterol biosynthesis, and so represent attractive candidates for cholesterol-lowering drugs. This thesis describes an asymmetric total synthesis of (-)-6,7-dideoxysqualestation H5 (DDSQ), focusing on the use of a bromide substituent to protect the side-chain alkenyl group from electrophilic addition reactions. Specifically, this alkene-protection strategy was used to circumvent an undesired cyclisation reaction during a late-stage acid-catalysed 6,8- to 2,8- dioxabicyclo[3.2.1]octane rearrangement en route to DDSQ. The synthesis addresses several synthetic challenges, including: efficient construction of the requisite alkenyl bromide functionality in the side-chain; preparation of the crucial β-hydroxy- α-ketoester precursor to the 6,8-cycloadduct; and removal of the bromide substituent through stereoselective methylation cross-coupling in the presence of ester and hydroxyl functionalities. After the fruitful total synthesis of DDSQ, further work was carried out to extend the scope of Seebach's alkylation of dimethyl tartrate acetonide, enabling access to enantiopure dialkylated products. Also, conditions have been developed for epimerising the monoalkylated tartrate products that may find application in the synthesis of other natural products. Furthermore, to address a key synthetic challenge in a previous synthesis of DDSQ, a concise route to synthesise α-diazo-ε-ketoesters was also developed that demonstrates the utility of chemoselective alkene ozonolysis in the presence of a diazo functionality.

Aortopathies refer to a broad class of pathological conditions affecting the aorta and are a major cause of morbidity and mortality in the US. Specifically, aortic aneurysms and aortic dissections have diverse etiologies that are initiated by alterations in the tissue’s extra-cellular-matrix (ECM) proteins, namely collagen and elastin, thereby predisposing the aortic wall to weaken and rupture. Pentagalloyl glucose (PGG) has recently emerged as a non-surgical treatment to reduce the risk of dissection or rupture. PGG is a known antioxidant and anti-inflammatory and has been shown to have ECM-restorative qualities that enhance collagen and elastin’s functional properties. Prior studies using PGG were largely performed in vivo or acutely in vitro; here, we aim to create a controllable, repeatable, fast, and inexpensive in vitro experimental platform to allow testing of this and other vascular therapeutics. To that end, we first created and validated the in vitro platform by assuring that cultured aortas maintained viability and mechanical properties for up to 2 weeks using multiple freely floating and stress-free configurations within an oscillating bioreactor. Next, we used this platform to investigate the effect of PGG on otherwise healthy thoracic aortas. Finally, we used tissues taken from a genetic mouse model of elastin damage, Marfan Syndrome (Fbn1C1039G/+), to test PGG’s restorative capability on diseased aortas.

This paper examines the social causes of epidemics and its consequences which lead to form new life cycles. Epidemics, in addition to other factors, including natural disasters and wars are one of the factors that would cause a sudden and rapid decline in population numbers. The theme of epidemics and their role in the decline of human numbers in addition to other topics such as the injustice use of natural resources and exploitation of the lower classes, are the most important themes that London discusses in his novel The Scarlet Plague. The opinions and ideas of some researchers and theorists are adopted like Turchin and Nefedov's theory, \"Secular Cycles\" for analyzing the socio-economic aspects of London's addressed novel. The study follows the social and political causes of the spread of these epidemics, which, due to the spead of such epidemics, have led to the elimination of humanity and all the features of urbanization on the one hand, and the social caste system on the other hand, the side that man has always sought to preserve. To clarify and discuss the social and political reasons that led to the emergence of these epidemics, the study adopts the socio-economic framework. The study concluded that the insistence of the dominant classes on the unfair exploitation of natural resources and the working classes led to a shrinking of healthy environments for humans, which led to an increase in population density in small areas, which helped the spread of epidemics randomly and rapidly. Besides, Natural forces, including epidemics, have the ability to displace all unjust man-made systems and establish a new life cycle.