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In today's world which is digitizing increasingly, facial expressions serve as one of the vital indicators of variety of human emotions. With the advancement of technologies like Artificial Intelligence (AI) systems, even machines can process and understand these facial expressions with wide number of applications across various domains. However, the challenge still lies in accounting the human diversity, which leads to distinct characteristics for each individual. And an attempt of creating a single model to fit all such individuals becomes nearly impractical. To tackle this challenge, an organized approach based on inter country facial distinctions could be a feasible solution. This approach works better within the specific country, but the universality is also required which is again a very big challenge. For this as a solution a collaborative methodology is being proposed. The aim of this paper is to prove that it is possible to create a universal model for detecting facial expressions with higher precision and accuracy by combining various datasets from different nationalities to obtain a single dataset with uniform distribution. This paper does so by using datasets of two countries i.e., JAFFE and CK datasets and generating a convolutional neural network-based model by optimizing the trade-offs between model complexity, training time, and required computational resources. [ABSTRACT FROM AUTHOR]

Natural disasters cause devastation, chaos, destruction, death, displacement, and much more when they occur. With the advancement of recent technologies especially in artificial intelligence and deep learning, the severity and impact of natural disasters could be reduced by predicting their occurrence. In this research, the flood detection (using images) system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). In this paper, the main aim is to propose an automated System for precision disaster detection, in particular flood detection. The intelligent system will monitor vast problematic areas that are dubbed flood risk areas during the season. The intelligent automated system will systematically take images with real-time processing to detect floods. If a flood is detected, the system will automatically communicate to the ground station with the early warning to issue an early warning to all the inhabitants in the path of the flood. Since this paper will prove a concept, the other main contribution of this paper is a new dataset consisting of 9000 images collected from various sources on the Internet, labeled, and preprocessed. The Deep CNN architectures were trained and tested by constructing a new dataset of more than 9000 labeled images with flood and non-flood collected from different sources. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. Experimental results show that 92.50% accuracy was achieved using GoogleNet. The second highest performance was achieved using AlexNet with an accuracy of 92.20%. [ABSTRACT FROM AUTHOR]

Nowadays, regular intake of nutritious foods is essential for maintaining a healthy eating routine and avoiding obesity in the human body.. In this paper, a novel framework dependent on AI that naturally performs exact grouping of food pictures and gauges food credits. In the preparation section of the model system, it proposes a profound learning model that includes a convolutional neural organization that orders food into explicit classifications. The principle reason for the proposed technique is to improve the exactness of the pre-preparing model. The paper plans a model framework dependent on the customer worker model. The customer sends a picture location solicitation and cycles it on the worker side. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Network (CNN) are the three classifiers that have been modified to analyze the framework's enhanced precision. Exploring various avenues about a variety of food groups, each with a large number of images, using Artificial Intelligence (AI) to achieve higher grouping precision. [ABSTRACT FROM AUTHOR]

Despite advances in technology Handwritten text is still considered as a method for correspondence and recording data in everyday life. Offline handwritten text recognition is an infamously challenging problem in Artificial Intelligence. Recent studies show great potential of Neural Networks for handwritten text recognition segmented at word level. In this paper, a prototype is presented for offline handwritten text recognition using deep neural networks. Preprocessing techniques like data augmentation and line-level segmentation enrich the input images and hence benefits the classifier. The proposed approach performs thorough feature engineering on preprocessed input images and propagates them through a three layered architectures comprising of CNN (Convolutional Neural Network), LSTM(Long Short Term Memory) and CTC(Connectionist Temporal Classification). [ABSTRACT FROM AUTHOR]

In 2019, the world witnessed a rapid spread of the Coronavirus (COVID-19) disease in one of China's cities, Wuhan. The disease was announced in December 2019 to be a springboard for the whole world, where most countries were infected with it. It greatly affected the health of society and became a real danger to humans. Therefore, it is necessary to diagnose the infected people and quarantine them to combat this pandemic and limit its spread. This paper aims to give a review of COVID-19 diagnosis methods. According to the diagnostic methods, the previous works are classified into two categories for diagnosing Coronavirus; X-ray and Computed Tomography (CT)-Scan. Artificial intelligence (AI) is used to improve the diagnosis accuracy of the presented methods. On this basis, automated diagnostic tools have been developed that distinguish people infected with the Coronavirus from other diseases. Moreover, the performance parameters of the previous studies were compared in terms of diagnosis method, adopted algorithm, diagnosis accuracy, sensitivity, and specificity. Furthermore, the challenges and limitations of the existing methods for diagnosing Coronavirus were explored. AI has proven to be a reliable way to diagnose the spread of the epidemic through advanced algorithms based on blind and machine learning, especially the convolutional neural network (CNN), which plays a vital role in extracting sensitivity, specificity, and accuracy. [ABSTRACT FROM AUTHOR]

In recent times, several fields that exist in engineering have been heavily dominated through the integration of artificial intelligence, however machine vision systems are simultaneously being studied and observed, they encapsulate certain tools that help in deriving technical information such as the working, errors and relative faults. Such systems play a vital role in manufacturing production lines, where important factors such as efficiency and productivity are sought to be at optimum levels. This paper captures the essence of using convolutional neural networks and also the addition of VGG-19 that are used in the detection and inspection of the various type of bottle caps. The respective bottle cap classes that have been pre-determined include normal cap, no cap a misplaced cap. The implementation of this system should enable us to determine whether a bottle with a specific cap class should be rejected or accepted. High metrics of precision and accuracy of the system should be observed and beneficial to the existing and upcoming processes. [ABSTRACT FROM AUTHOR]

Artificial intelligence & Deep Learning plays a vital role in this era. It is doing great progress in all aspects. "Automatic Image Captioning" is a Deep Learning mechanism; its primary aim is to generate the caption for an input Image by the machine. This mechanism initially identify the object in the image by using computer vision (CV) and generate the concise meaning of an image using Natural Language processing (NLP). In this paper we use Convolution Neural Network (CNN), which takes an image as input and assigns importance (suitable biases and weights) to varies object in the given image and it can distinguish one object from other and Long Short Term Memory (LTSM) give the proper meaning to each object in the image and gives a proper description to an entire image. [ABSTRACT FROM AUTHOR]

Advancements in deep learning and computer vision have resulted in a surplus of counterfeit facial content that is incredibly realistic in appearance, which is controlled by artificial intelligence., for example, Deep Fake or Face2Face that control facial personalities or on the other hand articulations. Counterfeit appearances made utilizing Generative Adversarial Networks (GANs) are becoming increasingly sophisticated and difficult to distinguish. The synthetic faces were designed primarily for amusement purposes, but their abuse has created emotional harm. For example, a few celebrities have fallen victim to Deep Fake's false erotic entertainment. Concerns are also growing regarding false political debate recordings generated by Face2Face. Building models that can identify fake faces in the media is essential for maintaining personal, societal, political, and international security. The purpose of this paper is to suggest a novel way to identify these counterfeits on the internet using Convolutional Neural Networks (CNN) and image processing techniques. We first used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Complex Shearlet-based edge detection on the dataset and then used the resultant images obtained to train a proposed CNN to categorize fake and real personalities and separate them. [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]

The deep learning technology is growing on day by day with increasing the internet access. The techniques of deep learning used to process information as human brain activities like learning some knowledge, solving the problems, and making the decision. Generally, deep learning provides self.training. In deep learning, CNN architecture is commonly used in many areas such as image processing, video processing, speech recognition, natural language processing etc. In the paper, the overview of deep learning is represented in which artificial intelligence, machine learning deep neural network are introduced. After that, layers of CNN architecture with its layers and techniques of deep learning are discussed. The CNN architecture is also used in image processing for security purposes. [ABSTRACT FROM AUTHOR]

The reality is that the population and number of automobiles on the street are growing day by day. The efficient monitoring of continuously increasing traffic is a critical challenge that traffic operators are facing. Traffic control is generally achieved using stationary video recorders, observers or manual counters. Hence, Unmanned Aerial Vehicles or drones are the high-quality answers to this trouble as they lessen the price of the set-up of numerous cameras and sensors. This paper's principal ideology is split into phases. The first one is the monitoring of traffic using a drone which helps in analyzing the traffic in a particular area and making interpretations. The second is fire detection and extinguisher. Here the drone is used to detect fire with the aid of a fire sensor as it helps the fire-fighters to analyze the affected area and prioritize their work. This project also up-skills in designing a quad-copter. Our motivation for this project lays both personal interests in a better understanding of object detection and academic research. A drone consists of a camera to cover a wider area which helps in traffic analysis. Here, the number of vehicles are estimated. Drones have recently emerged as an outstanding technology in the business market and are geared up with a camera, geo-positioning sensors, and communications hardware that may relay statistics. This project is implemented by using artificial intelligence and the Internet of Things for Fire Detection. To be more specific traffic detection is achieved using Deep Learning algorithm which is the Convolutional Neural Networks Algorithm using python. Fire sensors are mounted on the drone, and it flies on a flight path within the building and sends fire alert data to the system controller upon sensing a fire condition via fire sensor and also puts off the fire using the water pump. For fire detection the sensor used in the drone basically detects IR (Infra-Red) light wavelength that is emitted from the fire flame which helps fire-fighters to quickly assess and monitor dangerous areas which results in more safety and limits the time it takes to scan the whole area. [ABSTRACT FROM AUTHOR]

Technologies focusing on the visually impaired play an important role in assisting the visually challenged and easing their living standards. Previously there have been several other technologies and papers proposed under this category to help the visually challenged. Applications have been built, and are being developed, to help these people work independently without anybody else's need and complete daily tasks easily, without any difficulty using Artificial Intelligence. The proposed application, Noon Gil, supports voice commands and performs operations accordingly using architectures and algorithms like The technologies used are Optical Character Recognition (OCR), Convolutional Neural Networks (CNNs), Natural Language Processing (NLP), Mobile Vision API, TensorFlow Lite, Google ML Kit, MobileNet SSD, etc. Our project which is an android application with an overall accuracy of 94.74% comprises three main functions: Text Detection with an accuracy of 90.83%, Object Detection with 94.40% accuracy and barcode scanning with 99%. [ABSTRACT FROM AUTHOR]

Medical imaging has seen substantial improvements not only in image procurement but also in terms of interpretational methodologies. The practice of using technologies such as image processing and artificial intelligence has shown remarkable results in the detection, classification, and localization of human bone fractures. Breakage or cracks in the bone occur when an external force is applied to a bone that is significantly larger than the bone's tolerance, which may lead to dislocation requiring extensive treatment. However, manual examination by experts is a complex and arduous process with a high chance of false detection. Many approaches under Artificial Intelligence and its subsets are proposed to facilitate fracture diagnosis and prevent misinterpretation, including Deep Learning and Convolutional Neural Networks (CNN). This paper is a comprehensive study of the various techniques and models designed using image processing approaches and the advances in machine learning and Deep Learning that have demonstrated improved performance in the precise detection of fractures in human bones. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) will shape human civilization differently because AI is increasingly playing a key role in our lives. AI is an Art that facilitate computer, robot or machine to mimic human behaviour. Deep learning is a subfield of AI that focuses more on how machine work and mimics the human brain. Deep learning is one of the recent, trending and emerging fields of study and research. Deep learning is revolutionizing every field from robotics to medicine and has provided an easy solution to many state-of-the-art problems. The recent developments in deep learning are virtual assistants, self-driving cars, news aggregation, natural language processing, digital marketing, visual recognition and image recognition etc. Despite many developments in deep learning, there still exist some challenges. Many researchers are working on deep learning, but most of them focus on field-specific issues, while this paper presents a brief survey on deep-learning advancements from the last few decades. This work gives a clear and concise direction to new researchers and academicians. This article thoroughly discusses Deep Neural Network (DNN); it also covers different topics, i.e. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including most of the other deep learning deep algorithms. This article also covers the open research issues and recent trends in deep learning. [ABSTRACT FROM AUTHOR]

Face Mask recognition has developed as an extremely admired issues in several domains such as image processing, computer vision and artificial intelligence. Several novel approaches are being developed through deep learning based Convolutional Neural Network model to detect the face mask identification. In this paper, we propose "FMNet" model which indicates Face Mask detection Neural Network model for recognition of face mask person on publicly available face masks images as resources. Furthermore, we applied image preprocessing, feature extraction have performed using CNN, applied FMNet model for face mask recognition from images, and finally perform classification technique to categorize the images as person "wearing masks" and "not wearing masks". Our experimental outcome generates better performance in finding face mask by achieving accuracy as 99.7%. Moreover, comparing our proposed algorithm results with pre-trained models namely VGG 16 attains 99.6%. This system has capability to perform in real time application creates it applicable to identify people in airports, buses, schools etc. [ABSTRACT FROM AUTHOR]

Internet learning has advanced a great deal in the beyond couple of years. Web based learning can be partitioned into two classes: live classes and recorded classes. This paper mostly centers around live classes. In live web-based classes, ensure whether the understudy is paying attention to the live meeting or not. It is hard for the guide/teacher to ensure that everybody in that live meeting is paying attention to the class as he can't outwardly see each understudy's face on his PC/work area screen. With the assistance of AI, it's feasible to tackle the previously mentioned issue. A nostalgic based look identification model is utilized to discover the mindfulness of the understudies. For this at first numerous edges from the video are taken at a customary span and these edges are given as info. Two principle classifiers are utilized in this task, VGG based Convolutional Neural Network for wistful investigation and Haar Cascade Classifier for Face discovery. This model predicts the state of mind of the understudy like dismal, exhausted, lazy, and others. The expectation made by the model is to be sure used to discover the mindfulness of each understudy. Our model has been tried with numerous experiments and is effective. This framework can be utilized in applications through which online classes are led. [ABSTRACT FROM AUTHOR]

Brain tumors are caused by the proliferation of abnormal cells in the brain. Because of the various nature of aberrant cells in the brain, diagnosing and classifying brain tumors has become very important in recent years. Treatment for brain tumors is mostly determined by characteristics such as the kind of tumor, the abnormality of the cells, and the tumor's location in the brain. Deep learning models are being utilized to diagnose brain malignancies using the Magnetic Resonance Imaging (MRI) approach, thanks to the fast growth of Artificial Intelligence (AI) libraries. Strong magnetic fields and radio waves are used in MRI scanning to obtain detailed pictures of the interior body. In this study, we use a convolutional neural network (CNN) to create a deep learning model that diagnoses, classifies, and locates the afflicted area of the tumor region in scanned brain pictures. This research is only focused on building a deep-learning application for brain tumor detection and classification. [ABSTRACT FROM AUTHOR]

The very rapid development of technology today cannot be separated from the role of artificial intelligence. Artificial intelligence is a thought process or intelligence like humans' that machines can use to do specific jobs. The use of artificial intelligence is very diverse in various fields, including the health sector. Recently the world's media has been shocked by the virus spread in China, namely the coronavirus. Corona can cause severe pneumonia for sufferers. Pneumonia itself is inflammation of the lungs caused by a virus. One of pneumonia sufferers' characteristics is that there is a fluid that looks like white spots on radiology or chest X-rays. However, it will be challenging to diagnose because at a glance, the radiological test results of lungs with pneumonia and normal lungs are similar. So, misdiagnosis is possible. Therefore, this study aims to create a machine learning model to classify lungs with pneumonia and normal lung on chest radiological images using the Convolutional Neural Network (CNN) algorithm. CNN is well known as a method in which the data process both image and video. The classification obtained for validation results in an accuracy value of 94%-99% for the pneumonia category, which suggests that the model can predict the image correctly. Meanwhile, for normal lung images, the accuracy rate is 84% -98%. This shows that the model can predict almost all of the images well; there is only one image that is not predicted correctly. [ABSTRACT FROM AUTHOR]