The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

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IoT-Based Applications in Healthcare Devices.

https://cyberleninka.org/article/n/1084009.pdf

Biodegradation of dairy effluent by using microbial isolates obtained from activated sludge

Deep learning has recently achieved great success in many areas due to its strong capacity in data process. For instance, it has been widely used in financial areas such as stock market prediction, portfolio optimization, financial information processing and trade execution strategies. Stock market prediction is one of the most popular and valuable area in finance. In this paper, we propose a novel architecture of Generative Adversarial Network (GAN) with the Multi-Layer Perceptron (MLP) as the discriminator and the Long Short-Term Memory (LSTM) as the generator for forecasting the closing price of stocks. The generator is built by LSTM to mine the data distributions of stocks from given data in stock market and generate data in the same distributions, whereas the discriminator designed by MLP aims to discriminate the real stock data and generated data. We choose the daily data on S&P 500 Index and several stocks in a wide range of trading days and try to predict the daily closing price. Experimental results show that our novel GAN can get a promising performance in the closing price prediction on the real data compared with other models in machine learning and deep learning.

Stock Market Prediction Based on Generative Adversarial Network.

In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles. 

Machine learning techniques and data for stock market forecasting: A literature review

Ultrasound assisted enzymatic pre-treatment of high fat content dairy wastewater.

A systematic review of stock market prediction using machine learning and statistical techniques

Wheat is one of the most common cereal crops in India. Aphids cause extensive damage to the whole wheat plant and lead to high yields loss. The aphid is transmitted on the summer day. Once the aphid is transmitted over the leave, the whole plant leave is damaged. Due to this, got damage whete plant and it also reduce the quality of wheat grain. Due to which the it is necessary to identify each aphid on wheat leaves. Only manual process is there to identify each aphid on wheat leave. Manually identification is a time-consuming and high laborious process. Therefore, the identification of wheat aphids through the Mask RCNN model can easily identify the location of each aphid in individual leave automatically. With the help of a Canon camera, a total of 6500 wheat images have been captured in the Punjab region with temperature 21–24° temperature. The Labelme software is used for the annotation of wheat leaves and wheat aphid. A total of 2300 and 1000 images have been randomly selected for training and testing purposes. The Mask scoring RCNN model is having a network capacity for learning the quality of predicted instance masks. Among all 1221 wheat leaves, a total number of 1021 wheat aphids have been found. The manually annotated ROI was compared to mask scoring ROI for wheat aphid identification and localization. Thus, the Mask scoring RCNN model has been achieved a high F1-score (96.66%) for wheat aphid detection in single wheat leave.

Recognizing Wheat Aphid Disease Using a Novel Parallel Real-Time Technique Based on Mask Scoring RCNN

In today’s modern era, parking remains a big problem for a lot of people. This problem consumes a person individual’s time by finding the right spot for parking. In the current research, the concept of an automatic parking system using the vehicle license plate or the number plate recognition is discussed. It will improve the process with much less hassle by removing human interaction. It will also lead to advancement in the security of vehicles evading the requirement of a slip or a magnetic card which is used to goes in and out for registering vehicles in a parking place. The researcher uses image processing algorithms to make an entry in the database of the parking automatically. AVNPR (Automatic Vehicle Number Plate Recognition) is used for the identification of the number of plates. Due to noise issues, deep algorithms like CNN (convolutional neural networks), RNN (recurrent neural networks) do not correctly recognize the miss-identification of the numbers in the vehicle plate. This problem is rectified by the authors by using the GAN (Generative adversarial networks) algorithm. GAN helps to create high-resolution images from a single low-resolution image. After applying the GAN, the classification of the vehicle plate is done through CNN. During experimentation, the proposed approach achieves 99.39% recognition accuracy for a vehicle number plate. Hence, the proposed system is suitable for identifying the numbers in the vehicle number plate automatically. Moreover proposed system compared with existing models, it has been found that it has achieved higher accuracy than the other models.

GAN-based synthetic data augmentation for increased CNN performance in Vehicle Number Plate Recognition

Powdery wheat (PW) is one of the most common wheat diseases in northern India. It is the most damaging wheat disease and it is prevalent in April to May season. Several methods of machine learning (ML) and Deep Learning (DL) methods are used to do wheat disease classification. The previous DL techniques have not achieved higher accuracy during PW wheat disease classification. In the current study, 450 wheat images are collected from primary and secondary sources. The normalization technique is used for preprocessing. These normalized preprocessed images are input to CNN. The normalized images increase the training and testing accuracy of CNN. Then, this pre-trained model is applied to the CIAGR images dataset via transfer learning method. During testing with images, CNN achieves 89.9% classification accuracy for PW wheat disease. After these pre-trained model is applied to CIAGR dataset images and achieves 86.5% classification accuracy. Moreover, the result shows that pre-trained NCNN model achieves higher accuracy during transfer learning.

Deepak Kumar

Papers

A systematic review of stock market prediction using machine learning and statistical techniques

Recognizing Wheat Aphid Disease Using a Novel Parallel Real-Time Technique Based on Mask Scoring RCNN

GAN-based synthetic data augmentation for increased CNN performance in Vehicle Number Plate Recognition

N-CNN Based Transfer Learning Method for Classification of Powdery Mildew Wheat Disease

Design and Construction of a Smart Wheelchair