Waste Segregation using IoT and Image Recognition
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... In [7] by Rashmi C et.al., the implementation of IoT devices and sensors facilitates accurate waste categorization, enhancing the overall process of waste segregation for disposal. By utilizing image recognition technology and moisture data, the research enables more informed and precise decisions regarding the classification of waste into wet and dry categories. ...
The battery power limits the energy consumption of wireless sensor networks (WSN). As a result, its network performance suffered significantly. Therefore, this paper proposes an opportunistic energy-efficient routing protocol (OEERP) algorithm for reducing network energy consumption. It provides accurate target location detection, energy efficiency, and network lifespan extension. It is intended to schedule idle nodes into a sleep state, thereby optimising network energy consumption. Sleep is dynamically adjusted based on the network's residual energy (RE) and flow rate (FR). It saves energy for a longer period. The sleep nodes are triggered to wake up after a certain time interval. The simulation results show that the proposed OEERP algorithm outperforms existing state-of-the-art algorithms in terms of accuracy, energy efficiency, and network lifetime extension.
Traditional waste management system operates based on daily schedule which is highly inefficient and costly. The existing recycle bin has also proved its ineffectiveness in the public as people do not recycle their waste properly. With the development of Internet of Things (IoT) and Artificial Intelligence (AI), the traditional waste management system can be replaced with smart sensors embedded into the system to perform real time monitoring and allow for better waste management. The aim of this research is to develop a smart waste management system using LoRa communication protocol and TensorFlow based deep learning model. LoRa sends the sensor data and Tensorflow performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper, and general waste compartment which are controlled by the servo motors. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This object detection model is trained with images of waste to generate a frozen inference graph used for object detection which is done through a camera connected to the Raspberry Pi 3 Model B+ as the main processing unit. Ultrasonic sensor is embedded into each waste compartment to monitor the filling level of the waste. GPS module is integrated to monitor the location and real time of the bin. LoRa communication protocol is used to transmit data about the location, real time and filling level of the bin. RFID module is embedded for the purpose of waste management personnel identification.
IOT and machine learning based household waste management system for Green smart society are aimed to make management of waste from the every apartment of the society more efficient using the most upcoming technology IOT. This paper discusses the collection and decomposition of waste in the smart way so that benefit from the waste is maximized and the actual waste is minimized efficiently. This paper focus on the segregation of the waste at two levels: the first level of segregation is on the individual house of the society and the second level of segregation is at the society. Author, discuss the recycling of the biodegradable waste for making compost. The machine learning technique such as KNN is used to generate an alert message for various combinations of three sensor values like level of bio and non biodegradable waste,concentration of poisonous gas. The overall impact of this research is in the upliftment of the green technologies by reducing pollutants, conserving, resourcing and reusing the energy through the use of technology.
Introduction
In the present scenario, social media network plays a significant role in sharing information between individuals. This incorporates information about news and events that are presently occurring in the real world. Anticipating election results is presently turning in to a fascinating research topic through social media. In this article, we proposed a strategy to anticipate election results by consolidating sub-event discovery and sentimental analysis in micro blogs to break down as well as imagine political inclinations un covered by those social media users
Methodology
This approach discovers and investigates sentimental data from micro-blogs to anticipate the popularity of contestants. In general, many organizations and media houses conduct prepoll review and expert’s perspectives to anticipate the result of the election, but for our model, we use twitter data to predict the result of an election by gathering twitter information and evaluate it to anticipate the result of the election by analyzing the sentiment of twitter information about the contestants.
Results
The number of seats won by the first, second and the third party in AP Assembly Election 2019 has been deter-mined by utilizing PSS’s of these parties by means of equation(2),(3), and(4), respectively. In Table 2 actual results of the election and our model prediction results are shown and these outcomes are very close to actual results. We utilized SVM with 15-fold cross-validation, for sentiment polarity classification utilizing our training set, which gives us the precision of 94.2%. There are 7500 tuples in our training data set, with 3750 positive tweets and 3750 negative tweets.
Conclusions
Our outcomes state that the proposed model can precisely forecast the election results with accuracy (94.2 %) over the given baselines. The experimental outcomes are very closer to actual election results and contrasted with conventional strategies utilized by various survey agencies for exit polls and approval of results demonstrated that social media data can foresee with better exactness.
Discussion
In the future we might want to expand this work into different areas and nations of the reality where Twitter is picking up prevalence as a political battling tool and where politicians and individuals are turning towards micro-blogs for political communicates and data. We would likewise expand this research into various fields other than general elections and from politicians to state organizations.