Real Time Object Detection Using YoloReal Time Object Detection Using Yolo

Object detection is related to computer vision and involves identifying the kinds of objects that have been detected. It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects. Keywords: YOLO, Intersection over Union (IOU), Anchor box, Non-Max Suppression, YOLO application, limitation.

The Device–Object Pairing Problem: Matching IoT Devices with Video Objects in a Multi-Camera Environment

IoT technologies enable millions of devices to transmit their sensor data to the external world. The device–object pairing problem arises when a group of Internet of Things is concurrently tracked by cameras and sensors. While cameras view these things as visual “objects”, these things which are equipped with “sensing devices” also continuously report their status. The challenge is that when visualizing these things on videos, their status needs to be placed properly on the screen. This requires correctly pairing visual objects with their sensing devices. There are many real-life examples. Recognizing a vehicle in videos does not imply that we can read its pedometer and fuel meter inside. Recognizing a pet on screen does not mean that we can correctly read its necklace data. In more critical ICU environments, visualizing all patients and showing their physiological signals on screen would greatly relieve nurses’ burdens. The barrier behind this is that the camera may see an object but not be able to see its carried device, not to mention its sensor readings. This paper addresses the device–object pairing problem and presents a multi-camera, multi-IoT device system that enables visualizing a group of people together with their wearable devices’ data and demonstrating the ability to recover the missing bounding box.

Protecting Android based applications from malware affected through SMS messages

Users use Android-based applications for communicating through emailing, text messaging, and transmission of audio and video objects. The attackers manipulate the email, text, videos, or audio so that users' receipt of the messages causes malware through their handheld devices. A runtime routine is invoked, which causes damage to the local resources of the mobile phone. The manipulation of the messages is done using different signatures, making it difficult to recognize the same using a single approach. Multiple approaches are sometimes required to detect different signature-based incoming messages. Choosing a method that suits the signature of the incoming message is the key. Malware can also enter at the time of installing third-party apps, clicking on the links provided in the messages, installing and invoking the malware in the background. Many issues are involved in dealing with malware detecting, prevention, and curing. A comprehensive architecture is required to deal with every aspect of dealing malware. In this paper, a comprehensive architecture is presented that considers malware's issue, especially concerning malware affected through short message service (SMS) messages operated under the Android operating system. The disection of the SMS messages have been implemented and 99% accuracy has been achieved.

The Pronunciation of Egyptian Arabic and Its Aspect of Sociolinguistic

The purpose of this research is to provide insight into the characteristics of the Amiyah Egyptian language from a sociolinguistic point of view. This research was conducted by examining a variety of literature relating to the object of study and also the deepening of the material regarding sociolinguistics itself. The research method used is note taking, which takes data from YouTube consisting of 10 video objects whose results are presented in descriptive form. The procedures of the research are as 1) listening to every phrase which is spoken by the speaker, 2) writing the vocabulary that has phonological differences with Arabic Fusha, 3) classifying data according to sound change prepositions, 4) analyzing data related to phonological and morphological aspects, 5) doing further analysis related to the sociolinguistic point of view, 6) presents the results of the study. The results of this study, Amiyah Arabic is not included as a language but as a dialect that emerges from a basic language, namely Fusha Arabic. However, amiyah language has different phonological and morphological aspects that have become characteristic of being another language. This was explained by the social conditions of the Egyptian community who held that the language variations formed were higher social classes than the existing basic language namely fusha language.