Autism spectrum disorder(ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson correlation coefficient (PCC) are chosen from 10: Sex, Speech delay, Jaundice, Genetic disorders, and family history. Next, chosen ASD feature dataset through its paces using five ML techniques: Naive Bayes (NB), K-Nearest Neighbor (k-NN), Decision Tree (DT), Support Vector Machine (SVM), and AdaBoostM1 (ABM1). The proposed framework is assessed in the third phase utilizing five measurements such as accuracy, precision, predicting time, recall, and F1-score,. The findings revealed that: The NB and K-NN approaches exhibit superior accuracy rates of 99.2% and 97.2%, with minimal prediction times of approximately 0.3 seconds and 0.45 seconds, correspondingly. Conversely, the DT and AdBM1 methods demonstrate a minor decline in accuracy, achieving 94.8% and 87.6%, respectively, along with increased prediction times. Nonetheless, the SVM approach exhibits the least performance, achieving an accuracy of 80.4% with a highest prediction time of 0.84 seconds.
Spraying pesticides is one of the most common procedures that is conducted to control pests. However, excessive use of these chemicals inversely affects the surrounding environments including the soil, plants, animals, and the operator itself. Therefore, researchers have been encouraged to...
Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l
... Show MoreIn this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.
Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu
... Show MoreWith the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect
... Show MoreCutaneous leishmaniasis is one of endemic diseases in Iraq. It is considered as widely health problem and is an uncontrolled disease. The aim of the study is to identify of Leishmania species that cause skin lesions among patients in Thi-Qar Province, South of Iraq, also to detect some virulence factors of L. tropica. This study includes three local locations, Al-Hussein Teaching, Suq Al-Shyokh General and Al-Shatrah General Hospitals in Province for the period from the beginning of December 2018 to the end of September 2019. The samples were collected from 80 patients suffering from cutaneous leishmaniasis, both genders, different ages, various residence places and single and multiple lesions. Nested-PCR technique was
... Show MoreThe aim of this research to study.
The dimensions of organizational learning have been defined(learning dynamics, individuals empowerment, knowledge management and technology application) as well as the dimensions of learning organization have been defined (culture values, knowledge transfer, communication and employee characteristics), Asset completion questionnaire was used to collect data of this research from a purposely sample represent forty employees who works in Iraqi Planning Ministry at different positions. The research divided to four parts :
The first to the research methodology, the second to the theoretical review o
... Show MoreThe purpose of this article is to introduce reverse engineering procedure (REP). It can achieved by developing an industrial mechanical product that had no design schemes throughout the 3D-Scanners. The aim of getting a geometric CAD model from 3D scanner is to present physical model. Generally, this used in specific applications, like commercial plan and manufacturing tasks. Having a digital data as stereolithography (STL) format. Converting the point cloud be can developed as a work in programming by producing triangles between focuses, a procedure known as triangulation. Then it could be easy to manufacture parts unknown documentation and transferred the information to CNC-machines. In this work, modification was proposed and used in RE
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