Devanagari Handwritten Character Recognition Using Deep Learning

In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

CTI View: APT Threat Intelligence Analysis System

With the development of advanced persistent threat (APT) and the increasingly severe situation of network security, the strategic defense idea with the concept of “active defense, traceability, and countermeasures” arises at the historic moment, thus cyberspace threat intelligence (CTI) has become increasingly valuable in enhancing the ability to resist cyber threats. Based on the actual demand of defending against the APT threat, we apply natural language processing to process the cyberspace threat intelligence (CTI) and design a new automation system CTI View, which is oriented to text extraction and analysis for the massive unstructured cyberspace threat intelligence (CTI) released by various security vendors. The main work of CTI View is as follows: (1) to deal with heterogeneous CTI, a text extraction framework for threat intelligence is designed based on automated test framework, text recognition technology, and text denoising technology. It effectively solves the problem of poor adaptability when crawlers are used to crawl heterogeneous CTI; (2) using regular expressions combined with blacklist and whitelist mechanism to extract the IOC and TTP information described in CTI effectively; (3) according to the actual requirements, a model based on bidirectional encoder representations from transformers (BERT) is designed to complete the entity extraction algorithm for heterogeneous threat intelligence. In this paper, the GRU layer is added to the existing BERT-BiLSTM-CRF model, and we evaluate the proposed model on the marked dataset and get better performance than the current mainstream entity extraction mode.

Cyber Security Challenges for Smart Cities

Smart cities are composed of interlinked components with constant data transfer and services targeted at increasing the life style of the people. The chapter focuses on diverged smart city components as well as the security models designed to be implemented. The four major paradigms discussed in this chapter are smart grids, building automation system (BAS), unmanned aerial vehicle (UAV), and smart vehicles. Apart from addressing the security concerns of every component, the major highlights of this chapter are architecture, smart environment, industry, lifestyle, services, and digital lifestyle quality. Finally, the chapter focuses on privacy preserving mechanisms, its essence over smart cities, strong architecture related to privacy, preserving mechanism, and various approaches available that can retaliate these issues in a smart city environment.

Modular automation cabinet for proactive monitoring in ATEX Zone 2

The paper presents an automation system dedicated to oil & gas machinery applications, such as electrical gas compressors. The future aim is to introduce it into the Romanian oil & gas industry. The system consists of a low size cabinet and its dependencies, which can be used in ATEX Zone 2. The electronic equipment is dimensioned and chosen according to the standards in force, so that the limited current and voltage requirements for methane gas to be met. The key feature is that this system complies with all ATEX standards for Zone 2. Moreover, it can also be a mobile and modular solution if needed, for a versatile and easy use in proactive monitoring of multiple locations across various compressor plants. The design, wireless transmission and possible implementation of this cabinet are presented. Remote data transmission via Wi-Fi and RS-485 communication interfaces between the devices is considered, due to several advantages: mobility, versatility, wide compatibility, environmentally friendly, as well as higher resources efficiency (reducing time and costs).

Internet of Things for Automation in Smart Agriculture

By combining the different monitoring and automation techniques available today, we can develop cutting-edge internet of things (IoT) systems that can support sustainable development through smart agriculture. Systems are able to monitor the farming areas and react to the parameters being monitored on their own without the presence of human beings. This automation can result in a more precise way of maintaining the aspects that affect the growth of plants, leading to an increase in the food production on farmlands. This chapter focuses on IOT for automation in smart agriculture and provides a pathway to develop automation system in the smart environment.

Residential security through the Home Assistant Platform

The etymology of the word safety means: lack of danger; feeling of peace and confidence; shelter from any danger; feeling of security. A security system of goods, values and protection of people can be implemented through an automated platform, under the name of Home Assistant. This server software application can communicate with an easy control system for fast and two-way access, which can be mounted to a restricted main entrance. The automatic opening of the access door is controlled both by stand-alone RFID (Radio-Frequency Identification) modules, which act on an electric closing mechanism, and by an automation system configured in the Home Assistant application. The ultimate goal is the security of the premises through a system that can operate both locally and online via internet connection. The research methodology was based on the study of intelligent applications for residential access, described in the literature, aiming at integration into the open-source HA platform, by developing new code sequences. I come with this paper in support of those who want to bring an improvement in the existing automation system, respectively to encourage those who want an automated residence at a higher level.

IOT Home Automation

The Internet of Things (IoT) is the most recent technology platform. We may regulate our daily routine work, such as home applications, control, and simple communication systems, using IOT, and improve our digital services. The Internet of Things (IoT) is a system that intelligently adds everyday content information to the internet in order to facilitate communication between objects and humans, as well as among themselves. In this study, we demonstrate how IoT can improve home automation. The relay, Node MCU, which is a network access component, is used in our suggested system. We can control that equipment using an IoT-based system technique. Mobile phones, computers, and tablets are used as user interfaces in home automation systems. They can connect to the home automation network through a wireless internet connection. The user will interact with the system directly via the control interface, while home appliances will be controlled remotely via a mobile app. An additional aspect of the home automation system is that it may be protected remotely. This redesigned design concept provided a home automation system that could be controlled efficiently. Keywords: IOT, home automation, smart home, relay, response time, Node MCU (ESP8266)