A Waste Management Technique to detect and separate Non-Biodegradable Waste using Machine Learning and YOLO algorithm

With the continuous advancement of industrial automation, product quality inspection has become increasingly important in the manufacturing process. Traditional inspection methods, which often rely on manual checks or simple machine vision techniques, suffer from low efficiency and insufficient accuracy. In recent years, deep learning technology, especially the YOLO (You Only Look Once) algorithm, has emerged as a prominent solution in the field of product defect detection due to its efficient real-time detection capabilities and excellent classification performance. This study aims to use the YOLO algorithm to detect and classify defects in product images. By constructing and training a YOLO model, we conducted experiments on multiple industrial product datasets. The results demonstrate that this method can achieve real-time detection while maintaining high detection accuracy, significantly improving the efficiency and accuracy of product quality inspection. This paper further analyzes the advantages and limitations of the YOLO algorithm in practical applications and explores future research directions.

Dental caries, a widespread oral health issue, necessitates early and accurate detection to ensure effective treatment and prevention. Traditional diagnostic methods often involve manual inspection and X-ray imaging, which can be time-consuming and subject to human error. This study introduces a cutting-edge approach by integrating machine learning with the YOLO (You Only Look Once) algorithm and leveraging the computational power of a Raspberry Pi to automate and enhance caries detection. The research focuses on developing a real-time, portable system for detecting dental caries from intraoral images using a Raspberry Pi. The YOLO algorithm, known for its efficiency in real-time object detection, is employed to train a model capable of identifying and classifying carious lesions in dental radiographs with high precision. This study aims to advance dental diagnostic capabilities by combining machine learning and embedded systems technology. The use of the YOLO algorithm with Raspberry Pi not only enhances the accuracy and speed of caries detection but also makes this advanced technology accessible and practical for everyday clinical use. Key Words: Dental caries, Open cv ,raspberry pi4 ,yoloV7

- The integration of artificial intelligence (AI) into skincare and cosmetics has opened new opportunities for personalized beauty solutions.Introduces a chatbot system that provides tailored cosmetic recommendations based on individual skin types and conditions.Utilizing the YOLO (You Only Look Once) algorithm, the system performs real-time skin analysis by detecting skin types such as acne-prone-skin, burned-skin, dry-skin and oily-skin from user-provided images.The chatbot combines these insights with user input on preferences and concerns to deliver customized product suggestions and skincare routines.The YOLO algorithm's efficiency in object detection ensures rapid and accurate analysis, while the chatbot interface enhances user accessibility and interaction. Key Words: Facial recognition,Skin analysis,Cosmetic suggestions,Chatbot

To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.

Human wildlife conflict occurs when encounters between humans and wildlife result in negative outcomes such as loss of property, livelihood, and even life for both humans and wildlife, which has a significant impact on the eco system. Interactions between people and wild animals are becoming more common as the human population grows and biodiversity increases. Detecting wild animals and preventing them from entering human habitats, resulting in human-wildlife conflict. It is intended to use machine learning to identify various types of endangered animals. The Dataset contains over 10,000 images in which 7,000 images are training images and 3,000 images are testing images. Our proposed solution employs You only Look once (YOLO) algorithm which is a Convolution Neural Network (CNN) to detect and identify the wild animals using Machine Learning model. The proposed method yields 94% accuracy in detecting the endangered wild animals through the computer vision algorithms. The achieved accuracy percentage is comparatively the highest among the existing model [2] because of the YOLO V4 model.

With the help of today's developing technology; many applications such as traffic monitoring, target detection, empty parking lot assignment etc. have become applicable with machine learning and deep learning methods. These applications are aimed to determine the desired object from the photographs. In this study, it is aimed to develop an approach that able to identify the vehicles through aerial images by using the YOLO (You Only Look Once) algorithm, feeding it with a trained convolutional neural network structure. As a result of the study, an application that can detect vehicles was developed, increased the performance rate of the YOLO algorithm by 3.2%.

Advancements in digital imaging and machine learning have transformed healthcare, enabling innovative solutions for automated drug identification. This study develops an image-based system to classify pharmaceutical drugs, tackling errors arising from visual similarities in their shape, color, or size. Accurate drug identification is crucial for healthcare professionals and patients to access reliable information on drug composition, usage instructions, and potential side effects, enhancing safety and efficiency in medical practice. The system leverages the YOLO (You Only Look Once) algorithm, renowned for its speed and precision in object detection. A dataset comprising 5,000 drug images sourced from Kaggle was curated, with annotations and augmentation techniques such as horizontal flipping, rotation, and scaling to improve model robustness. The YOLOv11 model achieved a precision of 97.4%, a recall of 97.6%, and a mean average precision (mAP@50) of 98.4%, demonstrating high reliability in real-world scenarios. Integrated with a user-friendly Tkinter interface, the system facilitates real-time drug detection and information retrieval, streamlining access to critical data. This research underscores the YOLO algorithm’s effectiveness in delivering rapid and accurate drug classification, offering a scalable solution for healthcare applications. The system’s success highlights its potential to reduce medication errors and improve patient outcomes through precise and accessible drug identification technology.

There are new regulations requiring the use of masks or face shields to prevent the transmission of Covid-19. Using deep learning, a model can be made to detect faces that use masks and face shields by training the model using the previous pre-trained model and using a custom dataset. The purpose of this study is to create a deep learning model that can detect faces with and without masks and as well as face shields for the prevention of covid-19 transmission using YOLO (You Only Look Once) with pre-trained models and custom datasets in real-time. In this study, using pre-trained models from YOLOv3, YOLOv3-Tiny, YOLOv4, YOLOv4-Tiny, and YOLOv4-Tiny-3l with Darknet Framework and compare between average pooling and max pooling in the convolutional neural network YOLO to detect face masks and face shields as a real-time. From experiment the highest mAP was obtained from YOLOv4 using average pooling with a value is 97.64% although the difference is not too much with YOLOv4 using max pooling with value 97.57% and the lowest was YOLOv3-Tiny using max pooling, which was 94.09%, and for the highest FPS was obtained by YOLOv4-Tiny with Fps values is 171 and mAP 96.75%. And for real-time detection of face masks and face shields, the best model used in testing using webcam 1080p is from YOLOv4-Tiny, because the FPS is quite good and the mAP is quite high.

This research aimed to develop detection model for aflatoxin-contaminated cocoa beans using YOLO (You Only Look Once) method based on fluorescence imaging. Aflatoxin level measurement and image acquisition were carried out on cocoa beans inoculated with Aspergillus flavus (6 mL/100 g) and incubated for 7 days. The quantification of aflatoxin levels was achieved through Liquid Mass Chromatography (LCMS), which served as the basis for categorizing the images into 3 groups, namely aflatoxin-free, contaminated below and contaminated above the threshold. The image acquisition was conducted in a mini studio equipped with UV light. Furthermore, image annotation was carried out using Roboflow, while YOLOv5 was employed as detection model for aflatoxin-contaminated cocoa beans. The performance of YOLO model, based on testing data, yielded the following metrics: precision (all): 0.91, recall (all): 0.93, mean average precision (all): 0.95 and mAP@[.5:.95] (all): 0.47. The average accuracy based on the confusion matrix from the testing data was 91%. These results showed that a combination of YOLOv5 model and fluorescence imaging presented a highly performing alternative for detecting various aflatoxin contamination levels in cocoa beans.

Among the population of India, nearly 1.6 million children are blind or visually impaired by birth. The challenges faced by the visually impaired persons are high in day-to-day life. There are number of challenges faced by them like requiring guidance for the usage of public transport, to walk independently. One of the most common problems is the dependency on others for the purchase of household items. Hence, a unique solution is required which makes the people to identify and purchase the products in the supermarket without dependency. This research focuses on a portable camera-based assistance that helps them to identify the grocery items along with the framework to read the text on the labels. This experimental system consists of three modules: object detection and classification; reading the text from the label; text to audio converter. The input to the camera is fed by the Raspberry Pi kit. The captured video is segregated as pictures of the grocery items. The proposed deep learning algorithm for the detection of object is You only look once (YOLO). The accuracy of the detection is pre-dominantly high compared to the existing algorithms. The captured image of the grocery item label is preprocessed to blur the unwanted text. Optical character recognition (OCR) is applied over the label to convert as the text where machine can read. At last, the processed text is converted as audio for the guidance of the people. Thus, a flexible supervisory mechanism with user-friendliness is developed for helping the visually impaired people.KeywordsDeep learningVisually impairedSmart super market assistanceOptical character recognition

Real-time deep learning-based position control of a mobile robot

Shrimp farming activities can be divided into hatchery and rearing. During the hatchery process, there is an activity of counting shrimp larvae. Currently, the method of counting larvae was still done manually using the sampling method by taking a full cup of shrimp larvae and counting them manually. This process was timewasting and can make the result in miscalculations due to human error. Therefore, this study proposed the You Only Look Once (YOLOv3) algorithm to perform automatic counting of the quantity of shrimp larvae. The results of the YOLOv3 final model show a good performance with a mean Average Precision (mAP) value of 96.83% and an average accuracy value of 76.48%. This YOLO algorithm can calculate shrimp larvae with hight precision.

The novel coronavirus (COVID-19) has affected more than two million people of the world, and far social distancing and segregated lifestyle have to be adopted as a common solution in recent years. To solve the problem of sanitation control and epidemic prevention in public places, in this paper, an intelligent disinfection control system based on the STM32 single-chip microprocessor was designed to realize intelligent closed-loop disinfection in local public places such as public toilets. The proposed system comprises seven modules: image acquisition, spraying control, disinfectant liquid level control, access control, voice broadcast, system display, and data storage. The STM32 microcontroller is the main control chip and collects the disinfectant liquid level information and crowd density by using flow sensors, pressure and image. Single chip microcomputer enabling composite control of disinfectant concentration and liquid level through proportion integration differentiation (PID) control and logical increase/decrease ratio control. The use of the You Only Look Once (YOLO) algorithm aids in improving the accuracy of human target recognition, dynamically obtaining the crowd density, and regulating the spraying strategy. A comparison of the dynamic changes in crowd density with the user-defined crowd density threshold is performed to optimize the access control time and model parameters and obtain the optimal access control time limit. This approach enables dynamic optimization and intelligent control of the proposed full-cycle, closed-loop disinfection model for public toilets, effectively reducing the risk of virus transmission.

Identifying the clods with traditional image processing is insufficient due to the irregular shape of the clods and the variety of soil types. In the literature, clod analysis is often performed after the clods are removed from their place of formation or damaged due to sectioning, thus limiting the number of similar approaches. The highly popular Deep Learning algorithms provided the solution. In this study, YOLO (You Only Look Once) algorithm was used. For its training, image processing, primarily the Watershed algorithm, is used for which a Python-based program was developed. After marking the clods and the background with image processing techniques and then augmenting it manually, the Watershed algorithm searches for the boundaries of the marked clods, significantly reducing the manual segmentation process that would otherwise require numerous human hours. Resulting in a methodology than can be used for both on field and Discrete Element Method simulations.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.