Home automation systems are gaining a lot of attraction globally and changing the way we live. They simplify our lives, reduce workloads, improve home safety and security, and pave the way for newer developments. It is no wonder why these systems are in such high demand and why modernization is needed to keep up with consumer needs. Nevertheless, utilizing home automation system technology can be energy-intensive and costly—especially for middle-class families in developing countries. In this article, we discuss SECHA, a smart, energy-efficient, and cost-effective home automation system. It empowers users to automate their homes with IoT regardless of the residence type. SECHA is developed with the goal of being energy-efficient, simple to use, and open-source for everyone’s benefit. SECHA has developed a low-cost smart home automation system that incorporates Wi-Fi and GSM technology, enabling remote monitoring and control of appliances through an Android application. This solution enables users to easily monitor and manage their homes. An automation system has been developed using an ESP32 microcontroller equipped with Wi-Fi and GSM SIM800. This impressive setup is further enhanced by the integration of several sensors that enable monitoring of temperature, humidity, movement, and other aspects at home.
Objectives: This study aims to advance Sheko language name entity Recognition first of its kind. Named Entity Recognition (NER) is one of the most important text processing in machine translation, text summarization, and information retrieval. Sheko language named entity recognition concerns in addressing the usage of the bidirectional Long Short-Term Memory (LSTM) model in recognizing tokens into predefined classes. Methods: A bidirectional long shortterm memory is used to model the NER for sheko language to identify words into seven predefined classes: Person, Organization, Geography, Natural Phenomenon, Geopolitical Entity, time, and other classes. As feature selection plays a vital role in long short-term memory framework, the experiment is conducted to discover the most suitable features for Sheko NER tagging task by using 63,813 words to train and test our model. Out of which is 70% for training and 30% for testing. Datasets were collected from Sheko Mizan Aman Radio Station (MARS), Sheko southern region mass media, Language, and Literature Department. Findings: Through several conducted experiments, Sheko NER has successfully achieved a performance of 97% test accuracy. From the experimental result, it is possible to determine that tag context is a significant feature in named entity recognition and classification for Sheko language. Finally, we have contributed a new architecture for Sheko NER which uses automatically features for Sheko named entity recognition which is not dependent on other NLP tasks, and added some preprocessing steps. We provide a comprehensive Comparison with other traditional NER algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.