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The purpose of the research is to detect defects in textile industry using computer vision and fuzzy logic techniques. The paper presents various practices used for edge detection, including a fuzzy logic applied. By testing, a fuzzy logic method has been chosen to obtain a respectable result. The algorithm that is observed in the paper is improved by dividing the processing into two phases: a fast one, which allows us to assume that there is a defect, and a searching one, which allows a defect to be highlighted in color. The advanced algorithm was tested on several types of fabrics. An average background arrangement is obtained for them. That allows separating fabric texture and color from potential defects. The tests were also performed on fabrics (cloths) with different brightness, number and type of defects. We obtained the percentage ratios of the defect detection quality and so we suggested the ways to develop the system. [ABSTRACT FROM AUTHOR]

Face Emotion Detection Music Player is an innovative idea that combines computer vision and machine learning techniques to create a unique and interactive music player. This Research Paper studies the uses of Convolutional Neural Networks (CNN) to detect and analyze facial emotions in real-time and dynamically create playlists based on detected emotions. The music player is designed to record facial expressions using a webcam or other suitable camera. To reliably identify facial expressions including happiness, sorrow, rage, surprise, and more, a CNN model is trained on a sizable collection of face images. The trained model demonstrated an astounding 74.46% accuracy in identifying emotions when combined with music player software running on a PC or other suitable device. The CNN model analyses the photos to determine the user's emotional state as they interact with the music player in real-time, as captured by the camera. Based on the detected emotions, the player automatically selects and plays songs from a predefined music collection that matches the user's emotional state. For example, if the user looks happy, the player can play happy and energetic songs, while if the user looks angry, it can play more calming and soothing music. Face Detection Music Player offers a personalized and dynamic music listening experience as the music selection is constantly updated according to the user's emotions. This research paper illustrates how computer vision and machine learning techniques may be used to produce interactive programs that can instantly adjust to the moods and preferences of their users. It has uses in user experience research, entertainment, and mental health, among others. [ABSTRACT FROM AUTHOR]

The paper proposes a novel approach for recognizing and classifying gym exercises using human pose detection with Mediapipe and LSTM, followed by matching the accuracy of the exercises using the Hungarian algorithm and cosine similarity. The proposed approach involves the use of a combination of cutting-edge technologies, including computer vision and deep learning, to recognize and classify a range of gym exercises accurately and efficiently. This study presents several advantages of this approach, including the ability to leverage temporal information, robustness to variations in lighting, camera angle, and occlusions, and the ability to handle a wide variety of gym exercises. Experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods for gym exercise recognition and classification. The paper also compares the proposed approach with existing techniques, highlighting its strengths and cost-effectiveness. It concludes by outlining the methodology, including dataset collection and pre-processing, landmark extraction, LSTM model training, exercise classification, and similarity analysis. Finally, the paper discusses potential future research areas in this field. [ABSTRACT FROM AUTHOR]

This state-of-the-art review paper provides a comprehensive overview of meta-learning techniques for learning to learn in machine learning. Meta-learning called "learning to learn," is an approach that enables models to learn from new tasks learning quickly and efficiently, based on prior knowledge and experience. The paper discusses various meta-learning techniques, such as model-based meta-learning, metric-based meta-learning, optimization-based meta-learning, and memory-based meta-learning. The review also covers the applications of meta-learning in different domains, including natural language processing, computer vision, and reinforcement learning. Additionally, the paper discusses the benchmark datasets, challenges along with the future directions of meta-learning research, highlighting the need for developing more efficient and scalable meta-learning algorithms. Moreover, the proposed Meta-Learn framework incorporates a hierarchical structure to facilitate knowledge transfer across multiple levels of abstraction, and it also includes a memory component that enables the system to store and reuse previous experiences. Overall, this review aims to provide a comprehensive overview of Meta-Learning techniques for learning to learn in Machine Learning and proposes a novel Meta-Learn framework that could potentially enhance the performance and scalability of Meta-Learning algorithms. [ABSTRACT FROM AUTHOR]

With recent advances in computer vision, benefits and challenges related to the adoption of Information and Communications Technology ICT tools in the higher education systems, a retrieval process based on teachers' perspective have become a key component in achieving high efficiency survey queries. In this paper, it is proposed and developed software called, "Smart Survey System", supporting quick access to identify the benefits and challenges related to the adoption of ICT tools in the higher education systems. The proposed system applies data analytics as a backbone, which uses PHP-WAMP server and MySQL database structure. This paper dwells on the analysis of a set of survey data for around 350 teachers. This paper also incorporates recommendations, limitations and future research directions. More efficiently search activities can be conducted through this proposed database system. [ABSTRACT FROM AUTHOR]

In a practical and real-world, colour matching is achieved effectively only by the colour constancy. This colour matching plays a very important role in each and every fields of our daily uses. Colour constancy refers to the study of how every humans will perceive the colours. The goal of colour correction is to obtain the colour of objects correctly regardless of the differences in light sources, illumination, etc. In this paper, we will learn to achieve the colour correction of the objects or materials used in our day to day life with the help pf the open computer vision and python programming. This respective technique can be effectively used for photographers, and also the computer vision practitioners. [ABSTRACT FROM AUTHOR]

In this study, we take a different approach to feature extraction by focusing on the diagonal rather than the horizontal in order to statistically and structurally utilise handwritten characters. Substances and Techniques: This paper employs the Computer Vision Laboratory's (CVL) publicly available database for its dataset (NTHU). Using a G intensity of 0.8 and alpha and beta quality values of 0.05., 0.2, and a 95% confidence interval, we determined that a sample size of 280 (group 1=140, group 2=140) handwritten characters would provide adequate statistical power. Novel sample number (N=10) diagonal feature extraction and sample number (N=10) horizontal feature extraction are used to classify statistical and structural aspects of handwritten characters, resulting in greater precision. The outcomes show that the accuracy of the diagonal feature extraction classifier is 95.883, while that of the horizontal feature extraction classifier is 92.736. This study has a significance level of p=0.0213. In conclusion, statistical and structural features of handwritten characters make diagonal feature extraction more accurate than horizontal feature extraction. [ABSTRACT FROM AUTHOR]

One of the areas of computer vision is superpixel segmentation, and the widespread use of superpixels in a wide range of practical applications shows that it has unique properties. A superpixel is created by combining pixels with similar content, which significant reduce the computational complexity of the algorithm at the next stages of image processing. After the superpixel is built, it is possible to solve problems such as classification, reduction of image dimensions, construction of spectral combination, selection of a certain channel, noise reduction, and change detection based on the application of superpixel to spectral images. This paper presents an analysis of SLIC, SEEDS, LSC, DFIC, AHP, DRW, WSS, BACA, and superpixel segmentation approaches on remote sensing images. The analysis of approaches was considered according to the characteristics of the color model, scale, regularity, connection, smoothness, texture, and boundary strength. Approaches were evaluated using the criteria of Undersegmentation error, Achievable segmentation accuracy, and Boundary Recall. The study revealed that superpixel segmentation techniques that employ all of the spectral image channels have not received enough attention; instead, they are typically applied using PCA to shrink the size or convert them to color models. Additionally, it is important to investigate the issue of applying deep learning techniques to the implementation of superpixel segmentation in remotely sensing spectral images. [ABSTRACT FROM AUTHOR]

OpenCV (Open Source Computer Vision) library can be used for face, nose and mouth detection. And the library was ready to use with the Haar-Cascade Classifier method. In this paper, the library was used to predict the incorrect use of facemask. Furthermore, the system was equipped with the knowledge that if the mouth and nose or one of them was detected, then the face had not used a mask correctly. The system was supported by an image capture device such as a camcorder, a processing device was used a single board computer, such as a Raspberry Pi and the output device was a display. And the result that the system was able to make a decision whether the face was wearing a mask correctly or not. If you don't use the mask correctly, a label will appear around the face in the form of a red angular line. The detection success rate was 88.4% using the detector parameters, namely: scale factor = 1.1 for all face, nose and mouth object libraries. [ABSTRACT FROM AUTHOR]

Technologies of Industry 4.0 are revolutionizing the way companies manufacture their products. Modern manufacturing systems are connected with the application of advanced sensors, increased automatization, embedded software, and robotics that collect and analyze data and allow for better decision-making. These trends are also reflected in recent innovations in the field of electrical discharge machining (EDM). One can state that recent progress in EDM is connected with the following areas: increase of automatization and robotization of the process, real-time data analysis, adaptive control and application of artificial intelligence, use of advanced sensors (especially machine vision systems), improvement of communication between machine tool and operator (i.e., voice-operated assistant technology) and application of additive manufacturing to shaping electrodes. All listed above and detailed innovations discussed in the paper cause EDM to become faster, more reliable, and more efficient technology, and the amount of unattended machining hours increases. [ABSTRACT FROM AUTHOR]

The paper discusses the prerequisites for the creation, technical solutions and implementation of an automated control system for the assembly of a small spacecraft. Both the hardware and software implementation of the system that provides control and logging of the assembly process of individual units at various workplaces are analyzed. The article presents solutions to reduce the requirements for equipment used to control the assembly technology, in particular, to use cameras with a lower resolution, through the use of special algorithms for the formation and processing of technological marks. A tool is presented that allows you to control the tightening torques of threaded connections and limit the tightening torque according to a given algorithm with wireless control. The developed system provides the functions of not only control, but also logging of the technological process, which can be useful in the future when creating a digital twin of the product. [ABSTRACT FROM AUTHOR]

In recent years, improvements in natural language processing and computer vision have come together to provide automatic image caption generation. Image captioning is the process of creating a description for an image. Captioning an image needs the recognition of significant items, their properties, and their connections within the image. Additionally, it must create phrases that are syntactically and semantically accurate. Deep learning approaches can address the complexities and difficulties associated with image captions. This paper describes a joint model which is capable of automatically captioning images using EfficientNet-B0 and a transformer with multi-head attention. The model is an aggregation of an EfficientNet & Transformer single encoder and decoder. The encoder utilizes EfficientNet-B0, a convolutional neural network-based algorithm that generates a detailed input image, represented by embedding them into a fixed-length vector. The decoder employs a transformer, and a multi-head attention mechanism to selectively concentrate attention on certain regions of images to predict the sentence. The proposed model was trained using a large dataset Flickr8k to optimize BLEU N-Gram (N=1,2,3,4), METEOR score, and CIDEr, to assess the probability of the target description phrase given in the training images. Our studies show that the proposed model can produce captions for images automatically. [ABSTRACT FROM AUTHOR]

The agricultural industry is the most prominent sector for the economic growth of the country. Cotton is an important crop that brings out major returns to the economy. In the current era, smart agriculture technology is incorporated which includes various modern techniques such as sensors, artificial intelligence, and computer vision to digitalize the whole process. This digitalization through modern technology enhances the fast and intelligent productivity of crops. In this paper convolutional neuralnetwork-based methods have been used to predict the maturity of the cotton balls using image features of mature and immature images. Real-time maturity detection is a challenging task for cotton fiber because of the many constraints in natural light conditionsand environmental effects. The proposed scheme uses annotation to classify the cotton balls. The classification is labeled as matureand immature images then the annotated images are used for training the deep learning models Resnet 50 and VGG 16. The averageaccuracy achieved from these methods is 92.47% for Resnet 50 and 96.58% for VGG16. The loss of the model is 21.05% for Resnet50 and 14.91% for VGG16. [ABSTRACT FROM AUTHOR]

Nowadays in India, due to the increase in the number of accidents vehicles are being automated using a variety of Computer vision algorithms. This paper focuses on the detection of tail signals of cars under different illumination circumstances. This system is implemented using FAST and SIFT algorithm which helps to extract features from the images. The obtained features were optimized by using K-Means Clustering Algorithm. This huge feature vector is converted into 8 clusters. These optimized feature vectors were trained on five different classifiers as Decision Tree, SVM(RBF), Random Forest, and Voting Classifier. The trained data set used in this algorithm contains around 9052 images. The obtained accuracy results of different classifiers are as follows, Decision tress has 76.36%, SVM(RBF) has 77.91%, Random Forest has 87.52%, KNN has 89.72%, the Voting classifier has 86.52%. It is observed that KNN gives the highest accuracy among the five used classifiers. This has, to the best of the authors' knowledge, not been presented in literature before. [ABSTRACT FROM AUTHOR]

Currently, many companies are competing to produce robots that can help humans. Robots can work with high accuracy, efficiency, and low power in the production of goods. Automated Guided Vehicle is one form of robot that is useful in industry. Automated Cars are another form of functional industrial robot that transports with programmable paths and directions automatically. In this paper, we will discuss the navigation system in Automatic Cars based on previously mapped paths. The robot wheel serves to adjust the direction and position of the robot. The automatic movement can be carried out using computer vision. Warehouses that are not neatly arranged cause the navigation system to be less than optimal. The types of controllers used include classical controllers, fuzzy logic, artificial neural networks, and H∞. The purpose of this research is to produce a precise car front view monitoring system using image processing with high enough quality for automatic car navigation. The benefit for science is the development of automatic cars, which so far have not been widely carried out in Indonesia. The results of this study can be useful for the development of methods to build an automatic car system. The benefit for the electronics industry is the availability of control equipment designs to be able to control cars automatically. The effect on society is in the form of future vehicles that can move automatically without a driver. Based on the computer vision system test for Raspberry Pi-based car navigation, it can be ascertained that the car works well and has a small physique, so it looks neat and concise. The movement of the car from the first coordinate point to the next coordinate point is very precise and precise. By installing the L298N motor driver module, it is easier to set the program in python, so that the results sent to the DC motor can be used directly. Using a DC motor that has an excellent performance in terms of torque and speed. [ABSTRACT FROM AUTHOR]

This paper describes the detection of unusual event that occurs in ATM machine. ATM is a simple source for people to get their money in a faster way. Also, there are many thefts and crime happening in ATM. The COVID-19 outbreak is the toughest challenge we have faced. The main precautions to avoid the transmission of virus are to wear a mask and maintaining the social distance between us. Here we introduce a system to solve such problems by using a computer vision to keep track of activity and detect violations (Not Wearing Mask) and alerts a mail with violation photo attached to concerned authority. In addition to this object detection and weapon detection is also appended. [ABSTRACT FROM AUTHOR]

The deaths caused by road accidents are one of India's most pressing issues. The inattentiveness of the driver is responsible for almost 80% of all collisions. Mobile phone use, conversing with passengers, reaching behind the wheel to grab something, and drinking while driving are just a few of the factors that can cause a driver to become distracted. This project aims to create a reliable and effective system for detecting and classifying distracted drivers. The efficacy of CNNs in the field of computer vision was the inspiration for this paper, which proposes a paradigm based on Convolutional Neural Network which not only senses but also recognizes the driver who is distracted. For this purpose it is using a publicly available dataset from the Kaggle website as an input for this model. There are several different forms of distractions, but this study focuses on the manual distraction, which is focused on the driver's stance. The goal of this work is to keep an eye on the driver's actions and seek for diversion signs. Distractions are divided into two groups in the model: safe driving and unsafe or distracted driving. For this purpose, various architectures of CNN can be used. This work, however, employs sequential architecture and achieved the highest observed accuracy of 99% with a total of 9.5M trainable parameters. The primary goal of this research is to classify a test image into two states of the driver that have been considered. [ABSTRACT FROM AUTHOR]

Pothole detection is a challenging task as it involves inhomogeneity in intensity, complex topologies, contrast less, and noise rich background. Still, it's a very important task concerning the maintenance of roads. Potholes are being detected using computer vision which generally involves analysis of a two-dimensional image of the road or modeling of the road surface. Provided both these methods are independently used. The accuracy of this method of pothole detection is far from the top point. These bring the usage of digital image processing which develops the detection of potholes with at most accuracy. Digital Image processing provides strong receptivity, usage of huge actions, and broad benefits. In this paper, we propose a system that supports digital image processing techniques of high efficiency to result in consistent and accurate detection of potholes. This project studies an intelligent digital industrial pothole detection mechanism to increase human safety, detection capability and decrease the factor of risk. Firstly, the sources of images from the road are pre-processed to remove noise and other irrelevant features. Then a training model was developed to train the system to classify between path holes and normal cracks using convolution neural networks and MATLAB. Also, an IoT device is used in this system which updates the detected potholes on the road to the respective web server along with the location coordinates, as a part to notify a complaint to check. This system works on providing a better pothole detection technique and also updating the same to the concerned. [ABSTRACT FROM AUTHOR]

Applications of deep learning to solve complex problems related to computer vision and human level control system has been successfully implemented in real life. On the other hand, deep leaning based systems can also cause harm to the privacy and security of the personnel or a system. One of the threats that is powered by deep learning and AI is deep fake technique which is used to create fake images, video and audios that are not identified by human visual or audio system. This makes it difficult for human to distinguish between the original data and the deep fake data. This paper aims at discussing the techniques or algorithm require to create deepfake and to detect the deepfake in audiodata. [ABSTRACT FROM AUTHOR]

Accidents on the road are happened in Low visibility Conditions like overcast, rainy night, foggy day in order to enhance machine vision in bad climate conditions. We suggest in this paper, a Different class climate classification method. Firstly, visual feathers are obtained from different traffic scene pictures, and features are state as eight dimensions of matrix. Secondly, Six supervised learning techniques are used to train six classifiers. The suggested approach has a high rate of identification precision and adaptability. The Proposed method is used to detect night time illumination changes and driver's vision in foggy day. [ABSTRACT FROM AUTHOR]

The document is a preface to the 1st International Conference on Artificial Intelligent Systems for Sustainable Solutions (AIS3 2023). The conference, organized by the Department of Electronics and Communication Engineering at C Abdul Hakeem College of Engineering and Technology in India, focused on the applications of artificial intelligence systems for sustainable engineering solutions. The conference covered various topics such as computer vision, data analytics, communication networks, IoT, renewable energy, security and surveillance, healthcare, industry 4.0, and smart cities. Keynote speeches were given by Dr. Muhammad Usman from Edge Hill University in the United Kingdom and Dr. Jobin Christ from Rajalakshmi Engineering College in Tamil Nadu, India. Out of more than 50 papers received, only 28 were selected for publication. The document is published by AIP Publishing. [Extracted from the article]