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In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.

This review examines the performance of latest highly capable nano materials in water purification. Easy synthesis and valuable nano-particles have plentiful applications in water disinfection. In recent times the existing researches explain an increasing applicability of (Zinc oxide, Copper, Silver, Titanium oxide & Iron oxide) nano-particles in the water disinfection. Environmental (i.e. water/air filter), industries (i.e. textile, animal husbandry, food packaging) and Biomedical (i.e. medical devices, hospitals) are some of different sector uses those nano-particles. Bacteria and protozoa are among the most typical pathogens of aforementioned untreated water, with gastroenteritis, urinary tract infection, intra-abdominal infections, and etc. harmful effects on human beings in developing countries. In this review, the aforementioned types of nano-particles as a disinfectant for E-coli removal from water have been included in detail.

The whole world is fighting as one against a deadly virus. COVID-19 cases are upon us in waves, with subsequent waves turning out to be worse than the previous one. Scores of human lives are lost while the post-COVID-19 complications are on a rise. Monitoring the behaviour of people in public places and offices is necessary to mitigate the transmission of COVID-19 among humans. In this work, a low-cost, lightweight two-stage face mask detection model is proposed. In the first stage, the model checks if a face mask is worn. In the second stage, it detects if the mask is worn appropriately, by classifying and labelling them. The proposed models are trained to detect faces with and without masks for varied inputs such as images, recorded videos, and live streaming videos where it can efficiently detect multiple faces at once. The efficacy of the proposed approach is tested against conventional datasets as well as our proposed dataset, which includes no masks, surgical masks, and nonsurgical masks. In this work, multiple CNN models like MobileNetV2, ResNet50V2, and InceptionV3 have been considered for training and are evaluated based on transfer learning. We further rely on MobileNetV2 as the backbone model since it has an accuracy of 98.44%.

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). Solar thermal collectors based on supercritical CO2 have now been shown in this paper. Since the results show that even though the solar energy is low, the CO2 temperature, pressure, and supercritical stress remain constant, this is distinct from conventional liquid-based solar collectors.

In mobile ad hoc networks, nodes are connected and disconnected regularly; since every mobile node travels autonomously, those nodes are dispersed not uniformly. Connection damage has straight consequences on the network characteristics. For that motivation, various techniques are quick and efficient damage of connection identification using alert packet is difficult for that time to identify next connection in a mobile network. It increases end to end delay and reduces the network lifetime. The present effective connection alteration rate- (ECAR-) based communication scheme depends on the details of neighbouring nodes to survey the connection between the connection alteration rate and the hello time gap in terms of entire transmission rate. Nevertheless, the hello time gap tracking algorithm is constructed to increase transmission rate displaying a stable choice of connection alteration rate, still although node velocity alters. It reduces end to end delay and increases the network lifetime. The hello time gap tracking algorithm is constructed in the network, and it obtains effective connection among unstable mobile nodes. Experimental output indicates that the connection alteration rate in general enhances the hello time gap. It provides the effective connection changing rate for analyzing different metrics which are network lifetime. throughput, and detection efficiency that are improved in the proposed ECAR scheme.

In a mobile network, nodes are placed in infrequent manner, which are moved along network in abruptly. Communication flaw between sender node and accepting node in path, the node having restricted energy and restricted transmission rate. It does not provide perfect route for communication among mobile nodes. It increases the packet drop rate and minimizes the lifetime of network. This work has proposed enhanced path routing with buffer allocation (IPBA) scheme which is implemented to obtain better communication; it protects the node from packet loss, and the buffer is used to maintain the temporary details of nodes and data packets that are ready to broadcast and receive. The coupling node selection algorithm is constructed to offer the path which frequently communicates data packets in normal case; the two efficient nodes are coupled with each other, and this type of nodes is selected to perform communication. It reduces the packet loss rate and increases network lifetime. End-to-end delay, communication overhead, throughput, network lifetime, packet loss, and energy consumption are the parameters considered for performance evaluations.

Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as melanoma, nevus, psoriasis, and seborrheic keratosis.