Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

Ensemble based on Accuracy and Diversity Weighting for Evolving Data Streams

Ensemble classification is an actively researched paradigm that has received much attention due to increasing real-world applications. The crucial issue of ensemble learning is to construct a pool of base classifiers with accuracy and diversity. In this paper, unlike conventional data-streams oriented ensemble methods, we propose a novel Measure via both Accuracy and Diversity (MAD) instead of one of them to supervise ensemble learning. Based on MAD, a novel online ensemble method called Accuracy and Diversity weighted Ensemble (ADE) effectively handles concept drift in data streams. ADE mainly uses the following three steps to construct a concept-drift oriented ensemble: for the current data window, 1) a new base classifier is constructed based on the current concept when drift detect, 2) MAD is used to measure the performance of ensemble members, and 3) a newly built classifier replaces the worst base classifier. If the newly constructed classifier is the worst one, the replacement has not occurred. Comparing with the state-of-art algorithms, ADE exceeds the current best-related algorithm by 2.38% in average classification accuracy. Experimental results show that the proposed method can effectively adapt to different types of drifts.

Using Ensemble and TOPSIS with AHP for Classification and Selection of Web Services

Due to the increasing number of Web Services with the same functionality, selecting a Web Service that best serves the needs of the Web Client has become a tremendously challenging task. Present approaches use non-functional parameters of the Web Services but they do not consider any preprocessing of the set of functionally Similar Web Services. The lack of preprocessing results in increased use of computational resources due to unnecessary processing of Web Services that have a very low to no chance of satisfying the consumer’s requirements. In this paper, we propose an Ensemble classification method for preprocessing and a Web Service Selection method based on the Quality of Service (QoS) parameters. Once the most eligible Web Services are enumerated through classification, they are ranked using the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) method with Analytic Hierarchy Process (AHP) used for weight calculation. A prototype of the method is developed, and experiments are conducted on a real-world Web Services dataset. Results demonstrate the feasibility of the proposed method.

Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification for Lung Cancer Prediction

One of the major causes of cancer-related mortality worldwide is lung tumors. An earlier prediction of lung tumors is crucial since it may severely increase the death rates. For this reason, genomic profiles have been considered in many advanced microarray technology schemes. Amongst, an Improved Dragonfly optimization Algorithm (IDA) with Boosted Weighted Optimized Neural Network Ensemble Classification (BWONNEC) has been developed which extracts most suitable features and fine-tunes the weights related to the ensemble neural network classifiers. But, its major limitations are the number of learning factors in neural network and computational difficulty. Therefore in this article, a Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification (BWOCNNEC) algorithm is proposed to lessen the number of learning factors and computation cost of neural network. In this algorithm, the boosting weights are combined into the CNN depending on the least square fitness value. Then, the novel weight values are assigned to the features extracted by the IDA. Moreover, these weight values and the chosen features are processed in different CNN structures within the boosted classifier. Further, the best CNN structure in each iteration i.e., CNNs having the least weighted loss is selected and ensemble to predict and diagnose the lung tumors effectively. Finally, the investigational outcomes exhibit that the IDA-BWOCNNEC achieves better prediction efficiency than the existing algorithms.

Super RaSE: Super Random Subspace Ensemble Classification

We propose a new ensemble classification algorithm, named super random subspace ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the random subspace ensemble algorithm (RaSE). The RaSE method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success of RaSE largely depends on the proper choice of the base classifier, which is unfortunately unknown to us. In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. As a result, Super RaSE is more flexible and robust than RaSE. In addition to the vanilla Super RaSE, we also develop the iterative Super RaSE, which adaptively changes the base classifier distribution as well as the subspace distribution. We show that the Super RaSE algorithm and its iterative version perform competitively for a wide range of simulated data sets and two real data examples. The new Super RaSE algorithm and its iterative version are implemented in a new version of the R package RaSEn.

Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.

Financial Instrument Forecast with Artificial Intelligence

In ancient times, trade was carried out by barter. With the use of money and similar means, the concept of financial instruments emerged. Financial instruments are tools and documents used in the economy. Financial instruments can be foreign exchange rates, securities, crypto currency, index and funds. There are many methods used in financial instrument forecast. These methods include technical analysis methods, basic analysis methods, forecasts carried out using variables and formulas, time-series algorithms and artificial intelligence algorithms. Within the scope of this study, the importance of the use of artificial intelligence algorithms in the financial instrument forecast is studied. Since financial instruments are used as a means of investment and trade by all sections of the society, namely individuals, families, institutions, and states, it is highly important to know about their future. Financial instrument forecast can bring about profitability such as increased income welfare, more economical adjustment of maturities, creation of large finances, minimization of risks, spreading of ownership to the grassroots, and more balanced income distribution. Within the scope of this study, financial instrument forecast is carried out by applying a new methods of Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Autoregressive Integrated Moving Average (ARIMA) algorithms and Ensemble Classification Boosting Method. Financial instrument forecast is carried out by creating a network compromising LSTM and RNN algorithm, an LSTM layer, and an RNN output layer. With the ensemble classification boosting method, a new method that gives a more successful result compared to the other algorithm forecast results was applied. At the conclusion of the study, alternative algorithm forecast results were competed against each other and the algorithm that gave the most successful forecast was suggested. The success rate of the forecast results was increased by comparing the results with different time intervals and training data sets. Furthermore, a new method was developed using the ensemble classification boosting method, and this method yielded a more successful result than the most successful algorithm result.

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

Background Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions. Objective The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. Methods In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. Results Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. Conclusions Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.