Designing of Dynamic Re-clustering Leach Protocol for Calculating Total Residual Time and Performance

Wireless sensor networks (WSN), a plurality of sensors in an energy limited deliveries. the sensors are arranged randomly for many applications. Thus, the battery Or download a replacement would be practical. Therefore, the energy efficient routing protocol to expand the network. In this article we propose a new cluster based Re-Leach dynamic Protocol Dynamic Reclustering based Leach protocol (DR-Leach), the expansion of the lattice energy consumption and reduce the age. The idea is that the energy cluster leaders  next production cluster to balance In each round, the same number of nodes in the network of life. Make your first calculation It calculates the optimum amount of CHS in each round, and the optimum amount for each cluster. The results showed that the improvement of reliability protocols proposed frame and the total energy consumption than BCDCP Leach and protocols

The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms

Weeds are a major threat to crops, making early detection critical for maintaining agricultural productivity. Weeds are generally toxic, equipped with thorns and burrs, and can disrupt crop management by contaminating harvests. This research aims to identify weeds in a field using image processing and deep learning techniques. Images were collected from an Indian farm and pre-processed using image processing techniques. The images were then analysed to extract features that distinguish between weed and crop properties. Traditional crop weed identification methods mainly focused on identifying weeds directly but weed species can vary significantly. This study proposes a method that combines deep learning and image processing technology. Identifying weeds in crops is a challenging task that has been addressed through image processing, feature extraction, and image labelling to train deep learning algorithms. The study examines the performance of various deep learning algorithms and convolution neural networks to detect weeds using images obtained from an Indian crop field. Once the input image is identified as a weed or not, the crop class prediction is made. These results could have significant implications for optimizing agricultural fertilizer usage, leading to increased crop yields and less environmental impact

IoT Sensor Based Sustainable Air Quality Monitoring System for Humans and Ecosystems in the World Empowerment

Due to elements that can harm human health, such as industries, urbanisation, population growth, and automobile use, the level of pollution is rising quickly. Using an Internet-connected web server, an IOT-based air pollution monitoring system is employed to track the air quality which sustains environment. When the amount of dangerous chemicals including CO2, smoking, alcohol, benzene, NH3, and NOx is high enough, it will sound an alarm when the air quality drops below a specified threshold. It will display the air quality in PPM on the LCD and on the website, making it very simple to monitor air pollution. The MQ135 and PM 2.5 sensors are used by the system to monitor air quality since they can accurately measure and detect the majority of hazardous gases. In recent years, air pollution has become a severe issue on a global scale and has surpassed advised national limitations. In addition to harming ecosystems and human health, air pollution also has an impact on global climate. The population is expanding, there are more industries, and there is an excessive amount of transportation that uses fuel, which are all contributing factors to the rapid rise in air pollution. To address this danger, the Air Quality Monitoring System was developed

An efficient novel approach to E-commerce retail price optimization through machine learning

Businesses can use price optimization to discover the most profitable price point by using customer and market data to drive their decisions. The optimal price points will result in the company making the most money possible, but they may also be created to help the company expand into untapped markets or increase its market share, for example Businesses can use machine learning to price products and services to maximise sales or profitability by using data instead of educated guess-work. When utilised for price optimization, ML-based algorithms can be used to forecast demand for a particular product as well as the ideal price and how buyers will respond to specific pricing. Pricing decisions can be made more accurately using machine learning, which will boost a company's revenue

An Efficient Novel Approach on Machine Learning Paradigmsfor Food Delivery Company through Demand Forecastıng in societal community

A food delivery business must be able to accurately forecast demand on a daily and weekly basis since it deals with a lot of perishable raw components. A warehouse that keeps too much inventory runs the danger of wasting items, whereas a warehouse that maintains too little inventory runs the risk of running out of stock, which might lead consumers to switch to your competitors. Planning for purchasing is essential because most raw materials are perishable and delivered on a weekly basis. For this issue to be resolved, demand forecasting is crucial. With the aid of historical data-driven predictive research, demand forecasting determines and forecasts future consumer demand for a good or service. By predicting future sales and revenues, demand forecasting assists the organisation in making more educated supply decisions. Regression methods like linear regression, decision trees, and Xgboost are used to overcome this issue

IoT Sensor-based sustainable smart home management for human needs through Micro Controller

Smart home technology is emerging rapidly as an exciting new paradigm. Appliances inside the home can be watched over, managed, and controlled by the consumer. Additionally, the gadgets are linked together via the internet, enabling the user to remotely control features like home lighting, air conditioner, and security systems providing sustainable approach. To aid the elderly and others with disabilities, this paper’s major goal is to teach consumers how to properly handle their household appliances. Thus, a smart home is being developed where the environmental conditions like air conditioner, television and lights adjust themselves with the person entering the room or even according to the weather conditions. Due to the complexity, in this paper an Microcontroller, LCD and other necessary materials in place of TV, AC and light are used to show that if a house consists of two or more people how the LCD will adjust itself and displays the standard required channel, likewise same LCD adjusts itself to the standard temperature required by the person and similarly adjusts itself according to the person entering into the room. Additionally, if it is raining outside the LCD will display a monsoon mood song, this is done using rain sensor. If these sensors are connected to the cloud, then the information related to the person entering the room is stored. This information can be accessed using your mobile

An Efficient, Novel, and Sustainable IoT-Based Approach for Attendance Detection through RFID Module and IR Sensor

The maintenance of student attendance at a facility is a challenging endeavor. The attendance is manually inputted, making it easily manipulatable. To effectively monitor and control student attendance in a setting, this research intends to build an intelligent attendance system with a sustainable approach to the problem. This study indicates employing RFID and IR sensors to automatically take attendance. Data can be viewed and stored in a real-time database using RFID (Radio Frequency Identification). The firebase database/google sheets are updated when the student’s card is brought close to the RFID module. Additionally, an IR sensor is employed to keep track of the overall number of students entering and leaving the classroom. It would be useful to know how many students are in the class overall, and how many students are in the class overall would be displayed on the LCD outside the class. The outcomes of the research on student attendance will be communicated to the parents, and the number of students in attendance overall will be forwarded to the higher representatives of the institutions. The system alerts the user if there is any discrepancy between the number of RFID tags scanned and the total number of students present in the classroom. The teacher must manually rectify the attendance error after the disparity in the count reveals the proxy in attendance. Future expansion of this research may include the use of biometrics and face recognition to detect the proxy and open up the possibility for a more sustainable environment