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Drowsiness and Fatigue of motorists and drivers cause more than 30% of road accidents. Every time, they increase the quantities of deaths, losses, and injuries encyclopedically. Discovery of drowsiness is needed so that it cautions the motorist and the drivers which might save their lives. It's being done using the CNN algorithm in image processing. In this discovery fashion, a motorist is continuously monitored through a webcam. In this technique, the driver's face is taken and it is fed to Deep Learning so that the blinking of the eye is predicted. We use an algorithm, if a motorist's eye seems to be closed for a particular quantum of time, the system cautions the motorist with a sound. To perform the segmentation of the images, ways similar as Image recognition and Image Processing. [ABSTRACT FROM AUTHOR]

This study looks into a number of recently developed pre-trained models and assesses how well they perform using the ImageNet Dataset. In the disciplines of image processing, computer vision, and machine learning, image Classification is a well-known issue. In this study, we studied six different models—three transformer-based models and three convolutional neural network-based models. The findings of this research offer insightful information on the perks and drawbacks of using pre-trained models for image classification tasks. The results indicate that while transformer-based models exhibit promising outcomes, convolutional neural network-based models continue to perform better overall and with regard to accuracy. Additionally, the pre-trained models' performance may be greatly enhanced by fine-tuning them on a smaller dataset. This paper provides an assessment of recently developed pre-trained models and their performance on the ImageNet dataset, which adds to the current research in the fields of image processing, computer vision, and machine learning. The knowledge gathered from this work can guide the creation of models for image classification tasks that are more effective and accurate. [ABSTRACT FROM AUTHOR]

The development of world technology is growing rapidly, including the health sector in the form of a tool for detecting various kinds of diseases. These technologies include systems that can recognize images and perform classification automatically. An automated system like this is called an intelligent system which is very useful in the health sector because it can help even replace a doctor or health expert in carrying out the task of diagnosing disease. The aim of this research is to build a system that can be used to detect COVID-19 infections automatically using image processing. The classification method used is an image processing technique using machine learning, namely a support vector machine (SVM) with entropy and correlation parameters. The image that will be recognized is the image of the lungs from the X-ray machine. The results show that the system built has succeeded in detecting normal lungs and lungs infected with Covid-19 with 98 percent accuracy [ABSTRACT FROM AUTHOR]

This work involves the development of a computer application known as the Emotion-Based Music Player, which is aimed specifically at music enthusiasts. Because choosing music can be difficult, most people prefer to play the playlist's tracks at random. Some of the songs selected as a result did not accurately reflect the users' current mood. Furthermore, no widely used music player can play songs based on the user's emotions. The proposed model can identify the user's emotions by extracting the user's facial expression. The songs in the suggested model will then be played by the music player based on the type of emotion identified. It aims to give music fans more ways to enjoy music. The proposed model encompasses a wide range of emotions, including Neutral, Sad, Happy, and Angry. The system heavily employs facial recognition and image processing technology using Fisher Face Method. The model's train and test inputs are still photographs in.jpeg format that is available online. To assess the proposed model's accuracy in identifying emotions, eighty users of various ages and genders—twenty for each type of emotion— are tested. According to the testing results, the proposed model has an 85% recognition rate. [ABSTRACT FROM AUTHOR]

Many devices, including digital cameras, washing machines, smart cars, computers, etc., will have a PCB as an integral component. Today's PCB demands the highest density of components and increased tolerance on these components. Surface mount technology (SMT) is making it possible to add many miniaturized components to PCBs. Component size is decreasing, and the component number is increasing; inspecting these components is becoming even more challenging. As many products are becoming smart, there is a huge demand for PCBs. Huge demands for PCBs also demand quicker inspection techniques. This shows the increased demand for online and inspection techniques. Many types of defects are encountered in the manufacturing of PCBs, such as missing components, cracks, etc. Many of the existing inspection techniques are not suitable for online PCB evaluation. In this scenario, the method presented in this article becomes very important. The present method uses image processing and deep learning-based technique for image identification and classification. Also, the method helps in the real-time assessment of PCBs. [ABSTRACT FROM AUTHOR]

Deep learning's application to machine learning has seen significant growth in recent years. One of the greatest difficulties now confronting AI systems is the integration of visual data with machine learning in the field of medical diagnostics. Brain tumours, the result of unchecked cell proliferation, are a particularly lethal kind of illness. If painful brain tumours aren't treated properly, they may spread and cause other problems. Cancer treatment relies heavily on an accurate diagnosis. In order to determine whether a tumour is benign or malignant, accurate identification is required. More individuals are diagnosed with cancer now than ever before in part because physicians lack the knowledge to effectively treat tumours at an early stage. In this research, we classify brain MRI and PET scans into tumour and nontumor categories using a convolutional neural network (CNN) technique, augmented by Data Augmentation (DA) and Image Processing (IP) with Image Acquisition (IA). Although we only used a tiny dataset in our experiment, the high level of accuracy (96%) it achieved vouched for the efficiency and minimal complexity of our model. Our approach requires much less computational resources to achieve comparable levels of accuracy as compared to previous pre-trained models. [ABSTRACT FROM AUTHOR]

The use of image processing to improve early diagnosis and treatment has been on the rise in several medical domains in the last few years. The high frequency of lung cancer and the difficulties in treating bronchogenic carcinoma have piqued the attention of the medical and scientific communities in the illness in recent years, particularly in situations when a rapid diagnosis is necessary for patients. Machine learning is used in cancer diagnostics to evaluate the patient's resistance, the probability of malignant growth recurrence and development, and the disease's permissiveness. In order to detect cancer in its early stages, the suggested framework uses computer tomography images of different stages of malignant development to predict its likely progression. The employment of a model was the most efficient and accurate technique to utilise the information to identify non-b malignant or malignant CT scan pictures. Furthermore, the model provided the most accurate and trustworthy predictions about the benign or malignant nature of CT scan pictures. [ABSTRACT FROM AUTHOR]

Skin Cancer is one of the most common types of disease in the world, including Indonesia. This disease is caused by food that can be toxic to the human body and the effects of global warming. Especially melanoma, this type of skin cancer that occurs in melanocyte cells is a very serious threat. In the United States, although melanoma is only 5% of all cases of skin cancer that occur, melanoma has been the cause of death in 75% cases of death from skin cancer in the country. Melanoma has a similar shape as mole, birthmark or beauty sign that is often ignored. To recognize the presence of melanoma early on, this study uses the method of deep convolutional neural network to classify skin cancers that occur in melanocytes, where melanoma grows. The deep learning method used has good accuracy and good use in image recognition. Dermoscopy image of skin cancer is used as input for image processing. Before entering the classification stage, the image is processed with the image processing Scaling and contrast enhancement. After testing phase in this study, it concludes that the proposed method has the ability to classify skin cancer that occurs in melanocytes quite well with an accuracy of 93.3%. [ABSTRACT FROM AUTHOR]

The diverse motifs of Lampung Batik require an automated identification process using digital image processing technology. This research aims to identify Lampung Batik motifs by utilizing computer technology, particularly in digital image processing, to assist in recognizing these motifs. In this era of digitization, there are various object recognition methods, one of which is image recognition that utilizes GLCM feature extraction to obtain the characteristics of Lampung Batik motifs. After the feature extraction process on Lampung Batik images is completed, this data is used for classification. This study employs the K-Nearest Neighbor and Naïve Bayes classification methods, with a total dataset consisting of 300 images, comprising 150 images of "siger" motifs and 150 images of "sembagi" motifs. In testing with the K-Nearest Neighbor method, the highest accuracy is achieved at K = 3, approximately 77% for the training data and 81% for the testing data. Meanwhile, in testing with the Naïve Bayes method, an accuracy of around 96% is attained for both the training and testing data. [ABSTRACT FROM AUTHOR]

The automatic safety system for the swimming pool system has been proposed using Computer vision and IoT. So many drowning incidents are happening, in order to avoid this drowning; the system tracks the position of the person in the water using videos. In this work, we have tracked the particular person in the water and if the person is drowning the system will give the alert message. Image processing algorithms were used to classify the drowning objects or persons. IoT and transfer learning has been combined to identify the pool safety automatically. Transfer learning model uses the pretrained model "ImageNet" that has more than 14,000 images for classification. Using ImageNet, classification of human or objects drowning can be performed correctly. The classification results have been sent to the mobile device if required. [ABSTRACT FROM AUTHOR]

The identification and diagnosis of plant diseases is one of the key factors in determining crop losses in crop production and agriculture. An analytical study of plant diseases is the study of any visible points in any part of a plant that help us to distinguish between two plants, technically or any dots or shades of color. Crop sustainability is one of the key factors in agricultural development. Diagnosis of plant disease requires a lot of work and expertise, a lot of knowledge in the field of plants, and research to diagnose those diseases. Therefore, image processing is used to differentiate plant diseases. Disease diagnosis follows methods for image acquisition, image removal, image classification, and pre-image processing. Leaf color, amount of damage to leaves, leaf area of the unhealthy plant is used for sorting. In this case, we reviewed the learning algorithms of the various machines to identify the leaf diseases of different plants and to determine the best accuracy. [ABSTRACT FROM AUTHOR]

Medicinal plants are types of plants that are often used by the community because they have many benefits, such as to prevent or cure various diseases. Betel and binahong are medicinal plants that are widely used by Indonesian people. Part of the plant that is commonly used to classify plant species is the leaf. Convolutional Neural Network (CNN) is a very commonly method that used for image classification. This method produces the most significant accuracy in image recognition. Image processing using the Central Processing Unit (CPU) will require a long execution time. The Graphics Processing Unit (GPU) is one alternative to increase the speed of data processing. This research use 900 images of data with a comparison of training and testing data, namely 8:2. Based on global test results using GPU can increase data processing speed up to 55% compared by CPU without much affecting accuracy. [ABSTRACT FROM AUTHOR]

Coffee is an important international commodity, especially for Indonesia, which ranks fourth as the world's coffee producer. However, in the field, coffee plants are susceptible to pests and diseases, which can reduce the yield and quality of the finished product. For farmers who often rely on rain-fed crops and have limited access to financial and technical support, this is a significant obstacle. Image processing and machine learning techniques, particularly deep learning and convolutional neural networks (CNN), can help identify and early detect plant diseases. This study used a quadcopter drone for image acquisition and employed CNN architectures, specifically AlexNet with 50, 75, and 100 epochs, to classify coffee leaves into three classes: healthy, leaf rust, and leaf spot. The best results were achieved by Alexnet with 100 epochs with an accuracy of 93.7%, precision of 98%, and recall of 100%. Thus, it can be concluded that the CNN method using the Alexnet model with 100 epochs and combined with a quadcopter drone can be used to detect coffee plant diseases. [ABSTRACT FROM AUTHOR]

Glomeruli are tiny clusters of blood vessels found in the kidneys which filter bodily waste. However, various diseases such as diabetes and obesity are risk factors for glomerulosclerosis, the scarring of glomeruli. This impedes the filtration process and leads to more advanced renal diseases and even kidney failure. Diagnosis currently involves a process of biopsies and classification of numerous glomeruli whole slide images. The classification of these images are time-consuming and prone to human error. Thus, there is an opportunity to utilize image processing and artificial intelligence to aid in the automation of the process, thereby aiding nephrologists to shorten diagnostic time. To this end, current literature explores the use of image classification using neural networks in the identification of glomerulosclerosis. However, existing studies make use of single convolutional neural network architectures. To improve the results, ensemble learning can be used. Ensemble learning is a method that aggregates multiple CNN architectures to reduce bias and to improve overall accuracy. Previous works demonstrated that using ensemble learning can increase accuracy in the classification of skin lesions, showing promise to its application in other fields such as glomerulosclerosis. As such, this paper uses an ensemble of CNNs to classify glomerulosclerosis in a dataset of 2,340 whole slide images of glomeruli as either "normal" or "sclerosed." Results are further optimized through the testing of different weight distributions using the weighted averaging ensemble aggregation method. Ultimately, we show that the use of ensemble learning in the binary classification of glomeruli can increase the accuracy of architectures compared to single neural networks. [ABSTRACT FROM AUTHOR]

Due to the overuse of antibiotics, antibiotic resistance in bacteria has emerged as a significant public health concern. Various methods can be employed to determine the susceptibility of bacteria to antibacterial compounds, and artificial intelligence (AI) has proven to be highly impactful in this field, especially with the advancements in image processing and classification. This research investigates two approaches for classifying antibiotic-resistant bacteria: traditional machine learning (ML) using the Orange Data Mining tool and deep learning with Convolution Neural Network (CNN). Using an antibiotic disk image dataset, various classifier models are compared using the Orange Data Mining tool. The traditional classifier model Logistic Regression is found to be the most accurate with a 91% accuracy rate, followed by Neural Networks (NN) with 90% accuracy. Other techniques such as Support Vector Machine (SVM), k-nearest neighbors (KNN), and decision trees (DT) are also analyzed, with varying levels of accuracy. Deep learning with CNN is found to be more efficient in classification, achieving an impressive 99.99% accuracy. This study's high accuracy results suggest that CNN could be a promising tool for the rapid and reliable diagnosis of antibiotic-resistant bacteria in the future. [ABSTRACT FROM AUTHOR]