In last years, computer vision has shown important advances, mainly using the application of supervised machine learning (ML) and deep learning (DL) techniques. The objective of this review is to show a brief review of the current state of the field of supervised ML and DL techniques, especially on computer vision tasks. This study focuses on the main ideas, advantages, and applications of DL in computer vision and highlights their main concepts and advantages. This study showed the strengths, limitations, and effects of computer vision supervised ML and DL techniques.

اضطراب طيف التوحد، المعروف أيضًا باسم ASD، هو مرض نمائي عصبي يضعف الكلام والتفاعل الاجتماعي والسلوك. التعلم الآلي هو مجال من مجالات الذكاء الاصطناعي يركز على إنشاء خوارزميات يمكنها تعلم الأنماط وتصنيف ASD بناءً على بيانات الإدخال. كانت نتائج استخدام خوارزميات التعلم الآلي لتصنيف ASD غير متسقة. هناك حاجة إلى مزيد من البحث لتحسين دقة تصنيف ASD. لمعالجة هذا الأمر، تم اقتراح التعلم العميق مثل 1D-CNN كبديل لتصنيف اكتشاف ASD. يتم تقييم التقنيات المقترحة على ثلاث مجموعات مختلفة من بيانات ASD المتاحة للجمهور (الأطفال والبالغون والمراهقون). تشير النتائج بقوة إلى أن 1D-CNN أظهرت دقة محسّنة في تصنيف ASD مقارنة بخوارزميات التعلم الآلي التقليدية، في كل مجموعات البيانات هذه بدقة أعلى تبلغ 99.45٪ و98.66٪ و 90٪ لفحص اضطراب طيف التوحد في البيانات للبالغين والأطفال والمراهقون على التوالي لأن النموذج المقترح أكثر ملاءمة لتحليل بيانات السلاسل الزمنية التي يشيع استخدامها في تشخيص هذا الاضطراب.

A precise prediction of student performance is an important aspect withineducational institutions to improve results and provide personalized support ofstudents.However, the predication accuracy of student performance considers anopenissue within education field.Therefore, thispaper proposes a developedapproachto identifyperformance of students using a group modeling. This approach combinesthe strengths of multiple algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM). Afterward, thelastensemble estimatesas one of the bets logistic regressionmethodswas utilizedto create a robust and reliable predictive modelbecause it considers The experiments were evaluated usingtheOpen University Learning Analytics Dataset (OULAD)benchmark dataset.The OULADdataset considersa comprehensive dataset containingvarious characteristics related to the student’sactivitiesthereby five cases based on the utilized dataset were investigated. Theexperimentresults showed that the proposed ensemble modelpresented its ability with accurate results to classify student performanceby achieving95%of accuracy rate. As a result, the proposed model exceeded the accuracy of individual basic models by using the strengths of variousalgorithms toimprovethe generalization byreducingthe potential weaknesses of individual models. Consequently, the education institutescan easilyidentify students who may need additional support and interventions to improve their academic performance

Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection accuracy highlighting potential to revolutionize the field of medical imaging and improve patient care. The proposed approach is build based on EfficientNet B3 model to classify four different types of lung cancer. The approach used CT scan images labeled into Normal, Squamous.cell.carcinoma, Large.cell.carcinoma, and Adenocarcinoma for the purpose of lung cancer detection. The results showed that the proposed model provided an improvement rate of 2.13% compared with the best-trained classifier with accuracy of 96%. This model can be generalized to improve lung cancer detection. The finding of deep neural networks, particularly EfficientNet B3, in supporting the diagnosis and detection of the lung disease, particularly in its early times.

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting ADR.

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for molecular sensing and has gained significant attention due to its high sensitivity and selectivity. SERS based on deep learning technology have been used in this study of materials, biological recognition, food safety, and intelligence. Deep learning techniques have shown tremendous potential in various scientific fields, including spectroscopy-based detection methodologies. In this study, we propose an effective deep learning approach for SERS detection using an artificial neural network (ANN). Then, the results are compared using two datasets batch1 and batch2. This study used Rhumamine 6G (R6G) as an aim molecule in this study. The experimental develops show the effectiveness of proposed ANN