Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

MH Hamzah, AF Abbas, International Journal of Early Childhood Special Education, 2022

Nowadays, due to our everyday stress and current stressful lifestyle, the loss of items appears a frequent issue and may be very inconvenient. In this regard, until the IoT becomes part of everyday life, we can use the software as an efficient tool to assist a person's searching, verifying, and finding lost belongings. This paper presents an Android-based application that we proposed and implemented to help users find lost items. Utilizing this software will enable the subscriber to record his request to the relevant authority. In addition, a special section offers to insert a contact telephone number or email to communicate between the person who found the item and the person who lost it. During testing, among other services, the p

Genetic variation was studied in 22 local and imported samples collected from local Iraqi market by using Single sequence repeat (SSR-PCR). Six primers set were used in this study. These primers produced 33 bands. Molecular weights of these bands ranged between 100 bp to 1500 bp. The number of polymorphic bands is 24, whereas the number of monomorphic bands is 9. The results of Dendrogram of the studied samples depended on SSR-PCR results by using Jaccard coefficient for genetic similarity was distributed the samples into 10 groups. This Dendrogram revealed a higher similarity between Iraqi/Balad green bell pepper and Iraqi/Yousifia green bell pepper with 1 value. This value is the highest between samples in comparison with lowest values (0

It takes a lot of time to classify the banana slices by sweetness level using traditional methods. By assessing the quality of fruits more focus is placed on its sweetness as well as the color since they affect the taste. The reason for sorting banana slices by their sweetness is to estimate the ripeness of bananas using the sweetness and color values of the slices. This classifying system assists in establishing the degree of ripeness of bananas needed for processing and consumption. The purpose of this article is to compare the efficiency of the SVM-linear, SVM-polynomial, and LDA classification of the sweetness of banana slices by their LRV level. The result of the experiment showed that the highest accuracy of 96.66% was achieved by the