Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public’s views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company’s stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology.

Respiratory rate (RR) is an important vital sign marker of health, and it is often neglected due to a lack of unobtrusive sensors for objective and convenient measurement. The respiratory modulations present in simple photoplethysmogram (PPG) have been useful to derive RR using signal processing, waveform fiducial markers, and hand-crafted rules. An endto-end deep learning approach based on residual network (ResNet) architecture is proposed to estimate RR using PPG. This approach takes time-series PPG data as input, learns the rules through the training process that involved an additional synthetic PPG dataset generated to overcome the insufficient data problem of deep learning, and provides RR estimation as outputs. The inclusion of a synthetic dataset for training improved the performance of the deep learning model by 34%. The final mean absolute error performance of the deep learning approach for RR estimation was 2.5±0.6 brpm using 5-fold cross-validation in two widely used public PPG datasets (n=95) with reliable RR references. The deep learning model achieved comparable performance to that of a classical method, which was also implemented for comparison. With large real-world data and reference ground truth, deep learning can be valuable for RR or other vital sign monitoring using PPG and other physiological signals.

The paper investigates the performance of DWT (Discrete Wavelet Transform) and CWT (Continuous Wavelet Transform) techniques based on On – Off keying modulation (OOK) for reduced BER with increase in SNR in optical wireless system. In this work, the effects of Inter-symbol Interference (ISI) and light interference on Artificial Neural Network (ANN) receiver are analyzed and reported. In proposed optical wireless system the On- Off Keying (OOK) modulation is used at receiver to analyze BER and SNR. It is further concluded that error prospect in the received signal relates to the noise level, which is given in terms of obtained SNR value.

INTRODUCTION: As an essential part of life, the use of the Internet has increased exponentially. This rising Internet bandwidth speed has made video data transmission a more popular and modern form of information exchange. For classification of video date files there is a requirement of human efforts.Also for reducing the rate of clutter in video data on Internet, a suitable automatic video classification method is required. OBJECTIVES: In this work, we tried to find a successful model for video classification. METHODS: To make a successful model we use different schemes of visual and audio data analysis. On the other hand we choose some music, traffic and sports videos for different analysis. The model is based on Hidden Markov model (HMM) and Artificial neural network (ANN) classifiers.In order to gather the final results, we developed an "enhanced ANN-HMM based" model. RESULTS: Our approach attained an average of 90% success rate among all three classification classes. CONCLUSION: In aim of this work is to categorize and caption the videos automatically.Here we proposed an enhanced HMM-ANN based classification of video recordings with the aid of audio visual feature extraction.

No abstract