Conformalized temporal convolutional quantile regression networks for wind power interval forecasting

Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.

A systematic literature review of deep learning neural network for time series air quality forecasting

This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets. Accurate attentive scene detection and analysis on resource-constrained platforms is challenging because these tasks are computation and energy intensive. We propose a new temporal visual attention network that unifies human visual attention tracking and salient visual content analysis. Attention tracking focuses computation-intensive video analysis on salient regions, while video analysis makes human attention detection and tracking more accurate. Using the YouTube-VIS dataset and 30 participants, we experimentally show that MemX significantly improves the attention tracking accuracy over the eye-tracking-alone method, while maintaining high system energy efficiency. We have also conducted 11 in-field pilot studies across a range of daily usage scenarios, which demonstrate the feasibility and potential benefits of MemX.

MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture

Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm. Review Article Kareem et al.; AJRCOS, 9(1): 1-22, 2021; Article no.AJRCOS.68652 2

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Skin Lesions Classification Using Deep Learning Techniques: Review

Internet of things is the advanced technological enhancement to make every city as smart. Recent days everywhere across the world IoT became one of the leading technologies. The popularity behind this technology is lies in its diverse nature as it integrates various heterogenous systems to work together. This heterogeneous nature lead to many challenging tasks. Apart from all these aspects Data fusion is one the peak improvement in any autonomous systems, it improves the systems functionality by applying fusion algorithms. This paper is going to discuss the challenging things of data fusion and will propose specific fusion algorithm to make any autonomous system as perfectly autonomous. Lastly, it is going to present how Data Fusion is achieved in IoT. Managing heterogeneous Bigdata and timely data and sensor fusion in IoT is a challenging task meanwhile data analysis, accuracy, reliability and decision-making is also vital role, these aspects are all presented in this paper. It has taken connected car as an example to discuss the above said.

Data Fusion Method and Internet of Things (IoT) for Smart City Application

Machine learning can be a technique of nursing lysis that automatically develops an analytical model. It is a branch of synthetic intelligence that believes that systems are going to learn information, determine patterns of information and decide with degraded human intervention. Machine learning addresses the question of how computers can be constructed that improve mechanically through knowledge. It lies at the intersection of technology and statistics and at the center of artificial data and information science, one in all the quickest increasing technical fields of nowadays. Recent advances in machine learning were driven by the event of latest learning and theories also as by the constant explosion. The event of latest learning algorithms and also theory and the in-progress growth within the accessibility of on-line information also as low-priced computation crystal rectifier to recent progress within the field of machine learning. Additional evidence-based decision-making could be carried out in science, technology and trade, including healthcare, production, education and monetary modelling, enforcement and promotion, with adoption of mechanical learning techniques based on data-intensive methods. The results are also available. The infection can be a life-threatening disease. The bite of a nursing partner is often transmitted in dipterous Anopheles. In infected mosquitoes, plasmodium parasite is a gift. The parasite is discharged into your blood after you bite this dipterous insect once it bites you. Once your body is composed of the parasites, they mature into the liver. The mature parasites enter the blood for several days when red blood cells start to infect. In red blood cells, parasites increase over 48-72 hours, causing infected cells to divide. The parasites still infect red blood cells, which last 2 to 3 days in cycles. This paper is used for observation of protozoan infection with a deep learning idea.

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An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

Role of convolutional neural networks for any real time image classification, recognition and analysis

The ash-bin may be essential would like everywhere the place. Nowadays numerous models of dustbins like open dustbins, mesh dustbins, pedal dustbins, swing lid dustbins square measure accustomed throw garbage or trash. the matter with open and mesh dustbins is that if the trash is unbroken for several days it's going to result in dangerous odour and child can unfold the trash all around. Another drawback is those that have kids, it's terribly troublesome to watch and to distant them from dustbins. The matter with pedal ash-bin is that it's not economical to be utilized by incapacitated folks. The issues with the prevailing system are often overcome by good ash-bin that is Associate in Nursing improvement of traditional ash-bin by elevating it to be good victimization Arduino board, motor and ultrasonic sensors for garbage detection. Associate in Nursing ultrasonic sensing element detects the rubbish that is unbroken on the subject of it and also the ash-bin opens followed by beep sound by permitting user to throw garbage into it. when the rubbish is thrown, the ash-bin is mechanically closed. These dustbins are often employed in homes, offices, hospitals, streets etc. Here the planned paper presents the economical use of dustbins. the aim of this project is to cut back human manual efforts and to automatize the task of ash-bin at the side of improvement of good home vision and good town vision.

K. Kranthi Kumar

Papers

An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

Role of convolutional neural networks for any real time image classification, recognition and analysis

Data Fusion Method and Internet of Things (IoT) for Smart City Application

Iot based trash collection bin using arduino

Criminal face identification system using deep learning algorithm multi-task cascade neural network (MTCNN)