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Monkeypox disease was declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) on July 23rd, 2022. The purpose of this designation is to encourage the global community to take coordinated measures to contain the spread of the disease and protect communities. The recovery period for Monkeypox disease typically ranges from 2 to 4 weeks, and in some cases, the disease can resolve spontaneously with or without treatment. However, severe cases have been reported, which can lead to fatalities. The fatality ratio for Monkeypox disease is estimated between 3% to 6% at present. It is essential to take precautionary measures to prevent the spread of this disease and seek medical attention if any symptoms are observed. Monkeypox is not highly contagious, and it generally spreads through close contact with an infected person. However, in May 2022, a significant number of cases of Monkeypox disease were identified, indicating that the disease was spreading rapidly. In light of this situation, it is crucial to track and isolate suspected cases promptly to prevent further transmission. Rapid and effective contact tracing, along with timely isolation of cases, is essential to contain the spread of the disease and prevent it from becoming an epidemic. Identifying, controlling, and taking measures to control the spread of Monkeypox disease is of utmost importance. To aid in this effort, this paper aims to create a large-scale dataset of Monkeypox disease images using Generative Adversarial Networks (GANs). GANs have proven to be highly effective in generating high-quality images and can aid in creating a more comprehensive and diverse dataset. The generated images will be used to train different deep learning models for image classification to diagnose Monkeypox disease. The deep learning models will be compared, and their performance will be evaluated using images of infected human skin with Monkeypox disease and healthy skin images. The results obtained from this paper will improve the accuracy of previous works, aiding in the early diagnosis and treatment of Monkeypox disease. [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]

This paper mostly focuses on real-time detection and classification of biomedical waste objects. Traditional methods for detecting the objects like biomedical waste objects in machine learning are substituted by recent technology of object detection methods in deep learning by building Convolutional Neural Network (CNN) which is nothing but the components of deep learning. This paper proposes a deep learning approach for object detection of biomedical waste material by evaluating the TensorFlow object detection API, which can used for real time application, this biomedical object detection API further we can used for automate the biomedical waste segregation system, this proposed system has been trained using more than 500 images for each type of biomedical waste object. Tensorflow is useful and easy to object detection that can form powerful image recognition software, in this proposed work we are using Faster R-CNN algorithm for developing this object detection process. [ABSTRACT FROM AUTHOR]

State-of-the-art solutions in the areas of language modelling, generating text, speech recognition, generating image descriptions or video tagging, have been using Recurrent Neural Networks as the foundation for their approaches. Understanding the underlying concepts is therefore of tremendous importance if we want to investigate frontier methods and develop new approaches for building smart digital solutions. This paper proposes a smart city control system architecture based on deep convolutional neural networks. The control system has a multilayer architecture that combines loosely coupled intelligent components. As the main layer, a solution based on deep learning technology is applied, which allows solving several tasks simultaneously: segmentation, detection and classification of images received from surveillance cameras of the smart city system. The data obtained at the output of this layer is used for further analysis and decision-making in the smart city system. The proposed architecture has a high degree of modularity and allows the replacement of individual elements in a loosely coupled architecture. In this paper, deep learning and computer vision technologies are also considered, on the basis of which the image processing layer from video cameras is implemented. A masked recurrent neural network is used for this task. [ABSTRACT FROM AUTHOR]

In recent years, AI deep learning technology has been recognized as a potential solution in many fields and applications. It has the ability to solve practical problems, especially the ability to identify and classify images. In addition to ordinary daily image recognition, it also includes applications in medical images. On the other hand, music therapy has been commonly used for children with developmental delays to stimulate children's visual, auditory and tactile sensory abilities and enhance children's developmental skills in different aspects. However, there was a lack of clear quantitative evidence of response time in previous music therapy treatments, mainly as there were no extra human resources to measure the response time accurately during the treatment process. In view of this, this paper aims to identify the time point when children turn their heads after being stimulated by the sound and calculate the reaction time after the sound stimulation for children with developmental delays in the process of music therapy, by using AI deep learning technology. In the experimental analysis, the reaction time is measured for children. The experimental results show that the average error of the reaction time measurement method proposed in this paper is less than 1 %, achieving the desired goal of accurately measuring the reaction time and analyzing the time error calculation. The method reduces the average error by about 6.5 times. [ABSTRACT FROM AUTHOR]

In recent years, with the advancement of deep learning, the combination of computer vision and natural language processing has gotten a lot of attention. Image captioning is an example of this field, in which a computer learns to interpret the visual information of an image by using one or more phrases. The capacity to analyze the state, properties and relationships among these objects is required not only for the meaningful description generation process of high-level image semantics but also for the ability to recognize the object and scene. Despite the fact that image captioning is a complex and demanding process, several researchers have made great progress. In this paper, we propose a real-time system using an encoder-decoder architecture to generate captions for the scene. [ABSTRACT FROM AUTHOR]

The degree of Guyabano fruit is decided commercially by human assessment of color and texture to confirmthat the fruits are of good quality. Manual grading of Guyabano fruit using human vision is both time-consuming andharmful. Guyabano is graded for ripeness using vision and touch to assess color and texture. Farmers and consumers who are ready to harvest and eat Guyabano fruit but are having trouble determining maturity will be able to do utilizingCNN techniques. In today's world, the most widely utilized class of image identification and classification models is the Convolutional Neural Network (CNN). The application of strategies for grading the quality of Guyabano fruit is discussed in this paper. From hand grading, an automatic gradation is done to ensure that the fruits are of the greatest possible quality. Based on the results, the study looked at various CNN designs to determine the best prediction classifier. The goal of this study is to develop a non-destructive way for the significant maturity of Guyabano fruits. We employed a MobileNetV2 network that had been pre-trained by the ImageNet dataset as a base network and then substitute the top layer of the base network with a normal convolution layer. The test results were reviewed using generated accuracy and loss evaluation to establish the model's overall effectiveness. The average grading accuracy for Guyabano fruit is 99%. [ABSTRACT FROM AUTHOR]

Recently, there have been several automations percolating around the Banking Sector. Banks use several automation tools to fasten their banking jobs which makes them cumbersome if not used. Vehicle Insurance Policy is a huge banking section which involves direct involvement of bank employees for vehicle damage verification and customer authentication. Insurance policies do not cover all sorts of vehicle damage, hence the type of damage incurred to the vehicle has to be verified. This process can be automated by Image Recognition using convolutional neural networks (CNNs) which has the capability to detect the type of vehicle damage (bumper dent, tail lamp dent, door scratchetc.,). The required input is an Image of the damaged part of the vehicle. In this paper, several hybrid CNN models are established to show the changes evolved in the accuracy metrics. Experimental results show that transfer learning technique performs a great deep feature learning on the present limited dataset. [ABSTRACT FROM AUTHOR]

A key component of tumour diagnosis, treatment planning, and creating an outcome for assessing brain tumours is highly accurate and dependable segmentation. Magnetic resonance imaging (MRI) is a tool used in medicine to help find very small abnormal growths in any human body part. Deep neural networks (NNs) and machine learning methods are efficient at segmenting 2D images, but it is challenging for NNs to distinguish significant organs from 3D medical MR images. The segmentation for tumour detection includes a number of processing techniques thatfall into the categories of pre-processing, segmentation, optimization, and feature extraction. The study focuses mainlyon 3D-based Convolution neural networks (CNN), ANN (Artificial Neural Networks), SVM, and Multi-class Support vector machines for deeper segmentation. In order to lessen the computational burden of processing 3D medical scans,this survey paper will examine recent developments in image segmentation and image classification based on effective and efficient processing of tumor-infected human brain MRI adjacent image patches that can pass through the network. The focus will be on gliomas. More discriminative 3D NNs and computational machine learning are used to process the input images at different scales simultaneously. This article also suggests the state of the art in tumor-based imageprocessing using deep learning models for tumour segmentation and detection. [ABSTRACT FROM AUTHOR]

Identifying bird songs is essential in monitoring birds and their behaviour without physically interacting with them and disturbing their natural habitat. The field of audio classification is a recently emerging area of research that is mostly interpreted using supervised machine learning techniques. In recent studies, image classification methods and algorithms have shown great promise in this field. This paper analyses three different deep-learning models which are ANN, CNN and RNN-LSTM and compares these models comprehensively with the above-mentioned models. Metrics such as precision, recall and F1-score have been taken into consideration to decide which model among the three models is best for audio classification. The audio files are converted into Mel-Spectrogram to obtain MFCCs and are fed into the models. It is observed that RNN-LSTM provides the highest accuracy followed by CNN and ANN respectively. This comprehensive study of existing models will help the researchers to choose the perfect model for audio classification for further research. [ABSTRACT FROM AUTHOR]

Implementation of Machine Learning/Deep Learning techniques in Image Processing have paved way to advanced object detection, classification and tracking in a real time environment. Recent advancements are enabling these techniques to be used in the field of real time Infra Red (IR) object detection. IR image processing is being used extensively in the field of military, industrial and medical applications. This paper emphasizes upon the various Deep learning techniques which can be used for automatic target detection/recognition in infra red maritime imagery of sea borne military assets. Our review begins with the introduction to Object Detection, IR imaging, techniques available and various architectures for feature extraction and object detection. Then, we also provide a comparison of performance/detection capabilities on widely used PASCAL VOC 2007 and 2012 datasets. [ABSTRACT FROM AUTHOR]

The objective of this paper is to build an Object Recognition model using 'TINY YOLO' and FPGA. Object Recognition is a CV (i. e., Computer Vision) technique for identifying and detecting objects in images or videos. Object recognition is a key output of DL (i. e., Deep Learning) and ML (i. e., Machine Learning). When we humans look at an image or video, we can readily spot people, object, scene and visual details. The aim of this is to teach a computer to do what comes innately to a human being. Object recognition is done with Image Classification, Object Localization and Object Detection that combines these two tasks and localizes and classifies an object in an image. Object Recognition has become a pivotal technology for driverless cars, security purpose, traffic surveillance etc., It is also used in a various application such as disease detection in bio imaging, industrial examination, and robotic vision. Hence, in a nutshell, 'Object Recognition is the staple of automation industry'. [ABSTRACT FROM AUTHOR]