python

The identification and diagnosis of plant diseases have long been considered. This research presents a system for diagnosing the volume and type of apple diseases and the spoilage percentage of rotten apples. To estimate the volume of apples, the method of immersion in water to change the volume of the container was used, ensuring more accurate volume estimation. For disease detection and spoilage analysis, a chamber with constant lighting conditions and a halogen lamp was used. Four images were taken with a camera for better analysis. The volume of apples was calculated through two approximations of the cylinder and incomplete cone. The average error rate in this system was 5%. Also, in the present research, a novel method for feature selection was identified using a combination of the weight feature and the calculated volume of hollow apples. To calculate the percentage of failure of each apple, first, the type of failure was identified. Then, the ratio of loss of each apple relative to the whole apple was calculated and compared with the number obtained from the desired region method, which was accurate. In this study, three major diseases of apples were studied, and an algorithm was written to distinguish these three types of infections from healthy apples. The results showed that the proposed method had the necessary efficiency to calculate the volume and percentage of failure and diagnose the type of apple diseases. In addition, the system's accuracy compared to previous studies increased by up to 95%.

This article presents the development of an automated Information System (IS) using Python, the Django framework, and a Telegram bot. The system processes user requests through a chatbot interface, providing data management and retrieval functionalities via a REST API. The integration of Django and Django Rest Framework (DRF) allowed for rapid backend development, while the Telegram bot provided an efficient means of interaction with users. The article describes the system's architecture, development process, and the tools used to implement the solution.

The Adomian decomposition method (ADM) is a powerful mathematical technique to find closed-form solutions to nonlinear functional equations including ODEs, PDEs, differential-difference, integral, integro-differential, algebraic, and transcendental equations or systems of such equations. It features a particular infinite series for the representation of nonlinear terms of the equation under study, referred to as the Adomian polynomials. Nevertheless, the computation of such polynomials manually, devoid of any assistance from computational resources, can often be a laborious and protracted endeavor. In this paper, an innovative Python code is proposed, which exploits the SymPy library to perform the involved symbolic calculus operations to generate the Adomian polynomials of any given nonlinear expressions. The use of the code would substantially facilitate the implementation of the ADM to the equations arising in various branches of science and engineering. A number of nonlinear expressions are decomposed to their relevant Adomian polynomials for the sake of demonstration.

A deep learning-based convolutional artificial neural networks structured a new image classification method approach was implemented in the study. Sample application was carried out with Diabetic Retinopathy disease. Obtaining information about the blood vessels and any abnormal patterns from the rest of the phonoscopic image and assessing the degree of retinopathy is the problem itself. To solve this problem developed methodology and algorithmic structure of this new approach is presented in the study. An approach called care model was used in this study different from the classical CNN structure. The care approach is based on the idea that the best solution will be taken from the new data obtained by rescale the available data according to total number of pixels before the average data pool is created and then CNN processes will continue. In the care model approach, all data is multiplied by the number of elements by the number of epoch time eight tensors. The purposed care model include VGG19 image classification model and developed mathematical model presented. Pre-trained model and all image dataset taken from kaggle and keras for implementation of case study. The purposed model provide train accuracy 87%, test accuracy 88%, precision 93% and recall 83%.

Air pollution is the biggest environmental hazard that cannot be ignored. Due to increase in number of industries and urbanization increases air pollutants concentrations in many areas because of this different changes are been happening in human life like health issues and as well as other living organisms. We have some pollutant emission monitoring systems, like Opsis, Codel, Urac and TAS-Air metrics which are expensive. As well as these systems have limitations to be installed on chimney due to their principle of operation. In this work I like to propose a function that is easy to use and causes less cost compared to the other ones. That is an industrial air pollution monitoring system based on the technology of Wireless Sensor Networks (WSNs). This system is integrated with the Global System for Mobile (GSM) communications and the protocol it uses is zigbee. The system consists of sensor nodes, a control center and data base through which sensing data can be stored for history and future plans. It is used to monitor Carbon Monoxide (CO), Sulfur Dioxide (SO2) and dust concentration caused by industrial emissions due to process.

Wind energy is a renewable energy source that has grown rapidly in recent decades. This energy is converted into electricity using advanced INVELOX wind turbines. However, the wind velocity is critical, and predicting this velocity in real-time is challenging. As a result, a deep learning (DL) model has been developed to predict the velocity in advanced wind turbines using a novel enhanced Long Short-Term Memory (LSTM) model. The LSTM enhancement is executed by employing the Black Widow optimization with Mayfly optimization in the Python platform as application software. The dataset has been prepared using Ansys Fluent fluid flow analysis. In addition to that, the wind turbine power generation was computed analytically. A subsonic wind tunnel test is also performed by employing a 3-Dimensional printed physical model to validate the simulation dataset for this innovative design. The proposed MFBW-LSTM model (Enhanced LSTM with BWO and MFO) predicts efficiently, with an accuracy of 95.34%. Furthermore, the performance of the proposed model is compared to LSTM, BW-LSTM, and MF-LSTM. Accuracy, MAE, MAPE, MSE, and RMSE are among the performance criteria the proposed DL model achieves efficiently. As a result, the proposed DL model is best suited for velocity prediction of an Advanced INVELOX wind turbine in various cross sections with high accuracy.

Household object detection is a brand-new computer technique that combines image processing and computer vision to recognize objects in the home. All objects stored in the kitchen, room, and other areas will be detected by the camera. Low-end device techniques for detecting people in video or images are known as object detection. With picture and video analysis, we've lost our way.

We all know forest is very important resource of oxygen. Saving our environmental resources is human beings responsibility. One of the techniques to save forests is forest fire detection. This is a technique used to detect the fire and prevent them in less time. Forest fire leads to death of wild life and trees. There are other techniques used to detect fire in forests like cameras, satellite system, manual monitoring but they take time to detect the fire whereas Forest fire detection system detects the fire within seconds and triggers the alarms. In this way we can save tress and wildlife in very less time.

Wireless sensor network technologies normally deploy a large number of small, low cost sensors, fairly densely that can observe and influence the physical world around them by gathering physical information, transform it into electrical signals, send it to a remote location to do some analysis and deploy the results in different applications. This means there is no need to build towers or set up complicated communication links such as; microwave and satellite. It can be deployed anywhere, even in inaccessible places. This technology can provide a real time monitoring for forest fire, where it can provide information at the ignition instance or at very small delay, depends on the node used wake up/sleep schedule. It’s more reliable because it can influence the world in the surrounded area, if it is used in appropriate methods, rather than expecting events over large distances and long delay like other satellite and camera towers techniques. In this work, all nodes only use temperature sensors and they are programmed on a certain threshold temperature, above it the node will send an alarm message to the sink. This concept relies solely on the node behavior to alert of crises possibility using simple node components to provide detection and information on whether this is a peaceful fire, or the beginning of wild fire. The key in this method is to make decisions by tracking the fire propagation and check the logic behind it.

Household object detection is a brand-new computer technique that combines image processing and computer vision to recognise objects in the home. All objects stored in the kitchen, room, and other areas will be detected by the camera. Low-end device techniques for detecting people in video or images are known as object detection. With picture and video analysis, we've lost our way.

The present research is a pioneering work in the studies of wind farms in Iran and an attempt to compute static ATC with a high penetration of wind farm. This research investigated Python's ability (in DIgSILENT) in a power system. It also investigated the effects of adding several wind farms to the Iranian grid through a static and dynamic analysis of static and dynamic constraints, transient stability and reliability. At the end of this research, a new method is presented entitled as the Quadratic Approximation of the path of the Minimum Distance Curve (QAMDC) via Python in DIgSILENT. This method can compute static ATC in a large wind farm. These analyses were tested on a segment of a real Iranian network called Khorasan with 2000 buses.

Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset.

The recognition of pathways and identification of cars was seen with a prospective camera, which recognizes trajectories and predicts control points. The aim is to propose the location of the path. In this paper, lane detection algorithm Steering Assistance System (SAS) is introduced. Guiding helps to learn driving and anticipates the control points and defines the direction that makes it easy to learn in a potential way and a lane keeping assistance system which warns the driver on unintended lane departures. Path keeping is an important element for self-driving cars. This article describes the beginning to end adapting the approach to holding the car in the right direction.

Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies from the thousand already existing ones. Content-based recommendation system describes the items that may be recommended to the user. Based on a data set, it predicts what movies a user will like considering the attributes present in the previously liked movies. Recommendation systems can recommend movies based on one or a combination of two or more attributes. While designing a movie recommendation system various factors are considered such as the genre of the movie, the director or the actors present in it. In this paper, the recommendation system has been built on cast, keywords, crew, and genres. A single column is created which will be the sum of all the 4 attributes, and it acts as a dominant factor for this movie recommender system.

There are currently two ways to vote in India. They are secret ballots and electronic vote machines, but these two processes have some limitations or disadvantages. The current system is also insecure. Many people miss the opportunity to vote simply because they need to go to the polling station and wait for several places to vote. In this paper, we proposed a voting method. In our method, the voting process has three security stages. The first stage is facial recognition, the second stage is Election ID (EID) number verification, and the third stage is One-Time-Password (OTP) verification using the user's mobile phone number registered.

Amid the previous three decades, the topic of image processing has gained vital name and recognition among researchers because of their frequent look in varied and widespread applications within the field of various branches of science and engineering. As an example, image processing is helpful to issues in signature recognition, digital video processing, remote sensing and finance. Image processing models are used for detecting the face. The aim of this thesis is to solve the face-detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a new method to deal with the frontal face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect the image as well as the coordinates. The main attraction of this paper is to solve different types of images having one object, two objects, and three objects which can’t be solved by any of the existing methods but can be solved by our proposed method.

Nowadays, the control of the traffic in the urban roads and in the highway has been a big challenge as the number of increase in the auto mobiles. So to overcome this problem we use the detection and tracking the vehicles using the traffic surveillance system. We can manage and control the traffic more easily. It is very complicated and a challenging task to identify the vehicle or a moving object in a complex environment with various background. The ratio detected of such algorithms depends on the quality of the foreground mask generated. Therefore this project is to present the detection and tracking the vehicles and the pedestrians in an efficient method which focus on trajectory motion of the vehicles and the pedestrians. In this proposed method, the pixels in the background are preserved which can be cars, bikes, buses, pedestrian, etc., the rest is discarded as the noise. Hence, our proposed method detects the vehicles and the pedestrians as mentioned and discards the rest noise as well in the same time. Here the quality of the generated foreground mask is more to increase the detection ratio. The performance is compared with other standard methods qualitatively and quantitatively.