Multi label restaurant classification using support vector machine

Many internet websites are hosted with a vast amount of information about restaurants which are not identified properly according to some predefined features to fit users’ interests. Thus, restaurant classification was needed to solve this problem. Restaurant classification has become very important for individuals and food business applications to spread their services via the Internet. In this paper, a modest model is proposed to classify restaurants based on their predefined features which are used as factors affecting restaurant's ratings. The usage of multi label classification is utilized for labelling to maintain acceptable requirements for restaurant's services. Two proposed labels are suggested resulted from the output of two classifiers each operate on a specific set of features. Support vector machine is used for classification because of its effectiveness in restaurant's label separation. The final prediction label is yielded after applying the proposed hypothesis rules. The experimental results conducting Zomato dataset show that the proposed multi label model achieved approximately about 88% for prediction accuracy. Using the proposed model for classification had led to get a collection of accepted restaurants according to user favorites

Design and implementation control system for a self-balancing robot based on internet of things by using Arduino microcontroller

This project is designed for attempting on developing an autonomous self-balancing robot. In this work, the two-wheel robotic system consists of a microcontroller (Arduino), Dc motor, and sensor. The Arduino is used to read the sensor data and gives the order of the motor based on the control algorithm to remaine the system is stable at different impediment. The robot is drive with Dc motor and the Arduino cannot drive. A motor driver (L298 type) is used to provide a sufficient current. The Ultrasonic sensor (used to sense impediment during the movement) and 3-axis gyroscope accelerometer sensor (To measure the robot inclination angle) to control the two-wheel robot. The controller laws allow reaching static or moving targets based on three structured IOT interactions between the elementary controllers and the sensor with actuator via Cloud environment. Regarding the technical detail must be designed based on the mathematical model. The mathematical model is used based on the model of some references, after that, the transfer function of the system is found. In this work, the MATLAB Simulink is used in the design of the controller, and the PID controller is used due to the simplicity and good activity in central systems. The PID tuner package Simulink is used to obtain the controller parameter (kp, ki, kd) that gives fast and good system response and stability. The result of the designed controller shows that the system has remained stable (remained vertically) and very fast (less than 1sec) until the system reaches the desired output

Modified Multipath Routing Protocol Applied On Ns3 Dcell Network Simulation System

—In the communication networks, guidance has become an important factor, with a significant impact on network performance, where the network orientation area has been and continues to be an ongoing development, intensive research for many years aimed at optimizing the network. This paper performs three modifications for a multipath routing protocol to solve the problem of routing in a DCell network simulation and apply online solutions on the network, the goal is to improve the transition efficiency of data. The modifications used to avoid data transmission failures which are delay problem, link failure problem, and power off (rack problem). The implementation of multipath routing protocol on the DCell network in actual simulation using the NS-3 program, which represents the rule that the DCell network was built and simulated. Finally, the modifications succeeded and return good results decreasing the delay time and solving the data transaction problems

Modified Multipath Routing Protocol Applied On Ns3 Dcell Network Simulation System

—In the communication networks, guidance has become an important factor, with a significant impact on network performance, where the network orientation area has been and continues to be an ongoing development, intensive research for many years aimed at optimizing the network. This paper performs three modifications for a multipath routing protocol to solve the problem of routing in a DCell network simulation and apply online solutions on the network, the goal is to improve the transition efficiency of data. The modifications used to avoid data transmission failures which are delay problem, link failure problem, and power off (rack problem). The implementation of multipath routing protocol on the DCell network in actual simulation using the NS-3 program, which represents the rule that the DCell network was built and simulated. Finally, the modifications succeeded and return good results decreasing the delay time and solving the data transaction problems.</p

Mobile Application to Detect Covid-19 Pandemic by Using Classification Techniques: Proposed System

Various mobile applications such as Mobile Health (mHealth) have been developed and spread across the world which has played an important role in mitigating the Coronavirus pandemic (COVID-19). As the COVID-19 pandemic spreads, several people have drawn parallels to influenza. While both viruses cause respiratory infections, they propagate in very different ways. This has a major impact on the public health measures that can be used to fight each virus. These viruses are pandemic-causing in the same way. That is, they both cause respiratory disease, and can present themselves in several ways, ranging from asymptomatic to severe and deadly. A proposal is presented in this paper that uses two algorithms to define and classify these pandemics, they are: The Back Propagation (BP) classification algorithm and the Fuzzy C-Mean (FCM) clustering algorithm. Two stages are implemented in the proposed system: in the first step, the FCM algorithm is used to find out the type of virus, and this algorithm is capable of handling ambiguous features of viruses. In the second step, a BP neural network is used as a classifier to detect the pandemic class. The proposed system was trained and tested using a well-known dataset (covid-19 vs influenza). Information Gain (IG) is used to optimize the related features that affect the classification process to improve speed and accuracy.  The proposed mobile application is developed to support users easily detecting the COVID-19 infection by inputting the medical tests as significant features to the proposed system. The proposed system's accuracy is up to (89%), the framework was created using the Matlab programming environment and an Android Studio for Mobil application designing