Artificial Intelligence based Intelligent Tourism System-Go My Way

At the moment, we find that tourists usually spend more time planning their trip because they need to spend every minute. In this context, this application aims to identify the main computer needs to support the improvement of the tourist promotion point, using the mobile application proposal. Currently, for regular tourists and travelers they spend a lot of time planning and deciding on their trip to achieve maximum satisfaction. In this case, the app aims to identify the main computer needs to support the development of the tourist promotion point. This paper suggests a model for use in an intelligent visitor information system. It uses the concept of knowledge-base. The model will be based on a study of human behavior as a tourism guide. It builds a relationship between an information-based system and a guide, to provide a service to any visitor who meets their needs and the purpose of obtaining location information. There are different modules, different methods of acquisition methods and a shorter way to acquire the ingenuity of the artificial intelligence in this thesis. The proposed system should be designed in such a way that it works on most devices namely palmtop and mobiles. So it can be helpful when visiting new places. This application will find the route using user terms. The short-term method of finding an algorithm should work well and in the right way in most cases. The system must find a method that fulfills the user's terms, indicating the name of the item, images related to a brief description of the location. It should also be able to find the distance, time and travel costs to your destination and over time the user can also make bookings using the app interface only.

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.

METHODS AND MEANS OF COUNTERACTION OF ACOUSTIC AND OPTICAL RECONNAISSANCE UAV

For the last few years, the ease of purchasing and using unmanned aerial vehicles (UAVs), their affordable cost has increased the demand for them both by companies and individuals. However, these devices might carry out illegal actions, starting with smuggling of illegal goods, unauthorized intelligence and computer attacks. As a result, this led to the urgency of developing effective and available countermeasures to detect and neutralize drones that perform reconnaissance of objects with confidential information. The most successful are autonomous systems for detecting and suppressing drones, which include optoelectronic, acoustic radar and radio frequency sensors, information from which is combined on the main computer to identify the threat and make further decisions. However, real-time monitoring is a rather difficult process that requires timely detection of adverse events or conditions. This creates many complex tasks such as object detection, classification, tracking multiple objects, and combining information from multiple sensors. In recent years, researchers have used various techniques to solve these problems and made notable progress. Applying deep learning to detect and classify UAVs is considered a new concept. In this regard, it became necessary to provide a generalized overview of UAV control technologies used for reconnaissance.

Study of Robot Localization and Control Based on Motion Capture in Indoor Environment

This paper researches a robotic localization system based on motion capture indoor environment. We apply motion capture technology to realize multi-robot positioning which achieves the multi-robots’ localization in indoor environment. It includes motion capture system, some robots, and main computer for data calibration and communication. Motions capture system based on the theory of photogrammetry which was analyzed. And the virtual experiment environment was established according to the actual experimental environment. The performance is demonstrated by numerical simulation and experiment.

Implementation of Cell Phone Detection Mobile Robot for Restricted Areas Using NodeMCU

This paper involves the design and implementation of cell phone detection mobile robot. This is applicable in examination halls, private conferences and meeting rooms in which the using of cell phones is highly restricted. The robot is able to detect the presence of unauthorized communications by active phones and then distort them. The detection process is achieved by implementing an electrical circuit for sensing undesirable signals and using NodeMCU for interfacing the robot with a main computer unit. The main computer unit is used as a controlling platform in terms of mobile robot navigation as well as detection and jamming activating. This is accomplished by creating a graphical control panel programmed using a special HTML script. In this work, the cell phone detection mobile robot has been applied in an examination hall to simulate real educational environment. The robot has detected active calls by cell phones with a circle diameter of 1.2 meter and then jammed them directly. The whole area has been covered for detection and jamming by roaming the robot wirelessly via the remote main computer. Different voltage measurements for different detection distances have been recorded.

The feasibility of using remote data collection tools in field surveys

Background: The objectives of the study were to conduct a field survey to measure the prevalence of chronic diseases by taking history, to assess the feasibility of using remote data collection tools in field surveys and to create the map of the survey area using global positioning system (GPS). Methods: A community survey was carried out in two urban municipal wards by trainees with medical sociology back ground among those aged 35 years and above. There were a total of 563 participants from whom history of chronic diseases were collected and from those aged 60 years and above the presence of frailty was assessed using Canadian Study of Health and Ageing (CSHA) Clinical Frailty Scale. The data was collected using a remote data collection application named KoBo Toolbox, downloaded in their smart phones, which was sent directly to the main computer in the Clinical Epidemiological Unit, using mobile data or Wi-Fi hotspots. The co-ordinates of the households were marked using GPS which was also sent through the KoBo Toolbox to the main computer. At the centre the data was converted into excel sheets and various percentages were calculated. Results: In the survey the proportion affected with diabetes, hypertension, coronary artery disease and cerebrovascular accidents were 24%, 20.6%, 10.5% and 3.5% respectively. Among the older population 2.2% were found to be severely frail or worse requiring special care. The field map of the area surveyed was also generated using the co-ordinates marked using the GPS enabled phones. Conclusions: The remote data collection tool enabled us to conduct a survey on chronic diseases, effectively, within a limited period of time, creating a map of the area surveyed.

DESIGN AND DEVELOPMENT OF LOW COST SMART BILLING SYSTEM

This paper presents the design of smart billing systems which can be placed to reduce the huge crowd in the malls in towns and cities. Especially it becomes more crowded on holidays. Customers pick different items in the malls and put them into trolley. At the cash counter billing process is done using bar code scanner and it is very time consuming process. To avoid this we are developing a system which we called as “Smart Billing System Using RFID and ZIGBEE”. In this system we are using RFID tags instead of barcodes. This RFID tag will be on the products. Whenever the customer puts a product into the trolley it will get scanned by RFID reader and product name and cost will be displayed on visual display. Like this the process goes on. We are using ZIGBEE transceiver which will be on the trolley which is used to transfer data to main computer. At the main computer ZIGBEE transceiver will be placed which will receive data from transmitter. To store the products price and total billing controller memory is used. Product name and the total cost will be displayed on LCD.