The current research is concerned with studying the decisive answers which are considered quick and conclusive. These answers can effectively interrupt the opponent's argument and close the dialogue.This research is concentrated on deliberative methodology focusing on the decisive answer's activity and ending them through several completing and argument sides. This research consists of an introduction and three parts, the current introduction is focused the light on the concept of decisive answers and its uses in literature and the scarce of speech, and how to consider it with one dialogue description,that dialogue constitute by ? The first part is concerned with those answers through the deliberative methodology and classifying decisive

يهتم هذا البحث بدراسة الأجوبة المسكتة,وهي أجوبة سريعة وحاسمة تقطع حجة الخصم وتفحمه وتغلقالحوار.وقد أرتكز البحث على منهجية تداولية تنظرإلى فاعلية الجواب المسكت في المحاورة وإنهائها من زواياإنجازية و حجاجية.وقد تكون البحث من ديباجة ممهدةوثلاثة مباحث.وسلطتالديباجةالممهدةالضوء علىمفهومالجواب المسكتوشيوعهفي كتب الأدبوالأخبارونوادر الكلاموكيف يمكن النظر لهبوصفه وحدة حوارية تنبنيمنها المحادثة؟أما المبحث ا

Subcutaneous vascularization has become a new solution for identification management over the past few years. Systems based on dorsal hand veins are particularly promising for high-security settings. The dorsal hand vein recognition system comprises the following steps: acquiring images from the database and preprocessing them, locating the region of interest, and extracting and recognizing information from the dorsal hand vein pattern. This paper reviewed several techniques for obtaining the dorsal hand vein area and identifying a person. Therefore, this study just provides a comprehensive review of existing previous theories. This model aims to offer the improvement in the accuracy rate of the system that was shown in previous studies and

Image Fusion Using A Convolutional Neural Network

The physician's commitment to medical insight is affected by several factors that vary from patient to patient in terms of the nature of the disease, the severity of the disease, the age of the patient, and the purpose of undergoing medical intervention. There are circumstances surrounding patients that require the physician to reduce the insight towards them, by concealing medical information. The physician must firmly commit to expanding the scope of his vision to a wider extent than in normal medical work. Therefore, we will discuss in this regard the cases in which medical explanation is reduced and the cases that require confirmation in the following order.

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N