Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a mixed-signal architecture that implements the minion recurrent unit for efficient temporal processing with on-chip continual learning. The architecture integrates weighted-bit streaming, which enables multi-bit digital inputs to be processed in crossbars without high-resolution conversion, and an experience replay mechanism that stabilizes learning under domain shifts. M2RU achieves ∼13 GOPS at 16.76 mW, corresponding to 776 GOPS per watt, and maintains accuracy within 5 percent of software baselines on seque

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

DBN Rashid, IMPAT: International Journal of Research in Humanities, Arts, and Literature, 2016 - Cited by 5

Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks

     The aim of this research is to identify the effectiveness of thinking skills in developing the life skills of the students of the first Academic year, particularly the differences in life skills according to the variable of study specialization (human-scientific). To achieve the research objectives, the life skills scale was constructed on the classification of (WHO). The psychometric properties of the scale were examined. The validity of the scale, which is the final form of (60) items, was valid for measuring the life skills. The scale was then applied to the research sample of (112) students of the first year of the University of Bisha.  The SPSS program processed statistical analysis.  Resul

This research aims to:
1 – Make a proposed module for (aesthetics) for the second stage - Department of Art Education under education theories.
2 - Verification from the effect of the proposed module on student achievement and motivation towards learning aesthetics material.
To verification the second goal we wording these two hypotheses:
1- There are no individual differences with statistically significant at level (0.05) between the student's scores average. (Experimental group ) who studied according to the proposed module and the average student's scores (control group) who studied in the usual way for the achievement test for the Aesthetics material.
2- There are no individual differences with statistically signifi

Abstract

     The aim of this research is to identify the effectiveness of thinking skills in developing the life skills of the students of the first Academic year, particularly the differences in life skills according to the variable of study specialization (human-scientific). To achieve the research objectives, the life skills scale was constructed on the classification of (WHO). The psychometric properties of the scale were examined. The validity of the scale, which is the final form of (60) items, was valid for measuring the life skills. The scale was then applied to the research sample of (112) students of the first year of the University of Bisha.  The SPSS program processed

The research aimed to identify "the effectiveness of educational-learning design according to the model of brain compatibility in achievement among firstmiddle grade students in mathematics", in schools affiliated with the Second Karkh Directorate of Education. To achieve the goal of research, the following zero hypothesis has been formulated: " There is no statistically significant difference at the semantic level (05.0) between the average scores of experimental group students who will study with design accreditation (educational - learning) according to the brain compatibility model and the grades of control group students who will study in the usual way in the achievement thinking test". The research community, which is represented by

ملخص البحث
تبحث الدراسھ عن تنفیذ افضل لمفھوم التعلم مدى الحیاة كھیكل موجھ للسیاسة التربویة في العراق بشكل عام وفي
التعلیم العالي بشكل خاص. تحدد الدراسة استراتجیات التعلم مدى الحیاة وتناقش اھمیتھ وسماتھ الرئیسیة لتسھیل
الوصول الى فرص تعلم متمیز و ملائم لحاجات الطلبة مدى الحیاة، كما تناقش دور الجامعة في تحقیق ھذا الھدف.

Natural settings make it challenging to identify facial expressions since head position, illumination level, and ‎‎occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This ‎research proposes a facial expression ‎recognition model based on pre-trained deep convolutional neural networks ‎with transfer learning. The model was trained ‎on several cases to classify face expressions into seven ‎classifications efficiently. The proposed system used the EfficientNetB0 model ‎that has one dense dropout layer. The model first rescales and norms the input dataset in the input ‎layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential