Volume 9 | Issue 11 |

 Abstract

Investing in the stock market is complex and challenging for people because prices are changing every second. For that, they have to update with the latest financial news. But nowadays, understanding financial information or news is hard. To solve this problem, this paper proposes a model of stock price forecasting based on financial news sentiment. The model is using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Using the latest research in Natural Language Processing, the model analyzes the sentiment of financial news, and then autoregressive integrated moving average model (ARIMA) forecasts the result. For this model, data is coming from Twitter and Yahoo API which after preprocessing going through a model.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Data analytics, Sentiment Analysis, News Articles ,LSTM-RNN, Arima, Deep-Learning.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Brain tumors are the most common and aggressive disease, leading to a very short life expectancy at its highest degree. Therefore, treatment planning is a critical step in improving the quality of life of patients. Generally, various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound are used to evaluate the tumor in a brain, lung, liver, breast, prostate, etc. Especially in this work, MRI scans are used to diagnose tumors in the brain. However, the sheer amount of data generated by MRI images frustrates the manual classification of tumors versus non-tumors at any given time. But it has some limitations (that is, precise quantitative measurements are provided for a limited number of images). Therefore, automatic and reliable classification schemes are essential to prevent the mortality rate of humans. The automatic classification of brain tumors is a very challenging task in the great spatial and structural variability of the surrounding region of the brain tumor. In this work, we propose the automatic detection of brain tumors using the classification of tumor, Che convolutional neural network (CNN). If a tumor is detected, the system classifies the tumor and tells the patient what stage of the cancer they are likely to have.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MRI, brain NN, feature extraction, classification

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Machine Learning appears as one of the key features to derive information from corporate operating datasets. Machine Learning in medical health care evolving as an enormous research field for delivering deeper understanding on medical data. Most methods of machine learning depend on several features defining the behavior of the algorithm, influencing the output, and thus the complexity of the resulting models either directly or indirectly. Many Machine Learning methods are utilized within the past to detect diseases. Prior detection of disease or low immune can help person to reinforce immune so on guard our body from harmful germs and viruses. Process is completed with the assistance of things like RBC counts, WBC, HB, MCV, MCH, MCHC, Neutrophils and platelet count. Observing and identifying of immune isn't an easy task to do manually because it takes a lot of time and effort. So it's easier to predict immune with an automatic processing and Machine Learning System. Different types of knowledge processing and Machine Learning algorithms are available to make best algorithm like rectilinear regression, Logistic regression and KNN algorithm. Various machine learning algorithm are often used to predict, so that the immune can often predicted easily and clearly because accuracy is vital in machine learning algorithm. This paper presents a study of various methods of predicting human immune.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine learning, KNN, Immune, Logistic regression

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The latest developments in Deep Learning based Machine Translation and Computer Vision based Object Detection have led to high accuracy Image Captioning models. Although these models are very accurate, they tend to rely on the use of expensive computational power making it difficult to use these models in real-time applications like processing the video streams in real time and extracting the information. In this paper, we carefully follow some of the heuristic strategies and core ideas of Image Captioning and its common methods and present our simple sequence to a sequence based implementation with a remarkable transformation and efficiency such as using beam search instead of greedy search that allows us to implement these on low-end hardware. The proposed system compares the results calculated using a variety of metrics with high-quality models and analyses the reasons behind the model trained on the MS-COCO dataset that are lacking due to trade-off between computation speed and quality. In this proposed system, Restful API endpoint will be created to be used on any device with an internet connection such as a mobile phone, IoT devices, clock, etc, this endpoint used to sent an image to the model running on remote server which in response will generate and sent an caption describing the objects and their relationship with each other in image in a natural language.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Sapio Enumerator based on video is an important field in Computer Vision. When Sapio Enumerator is implemented it may accompany by some problems. This paper proposed a method that may mix some existing technologies to overcome some problems. The Sapio Enumerator requires more powerful processing since it deals with real-time video, so the proposed method converts a color image into binary to minimize data of an image. Also, the image may contain a noise, the proposed method uses Erosion and Dilation processes to erase the noises. Reducing development time is an important term in Software Engineering to build an application. The method depends on existing packages for reducing development time. The proposed method is implemented in the Python programming language.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Android application, image processing, cloud storage, motion detection, people detection, computer vision, pixel.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Version Control Systems; Distributed Version Control Systems; Centralized Version Control Systems; Software Development; Collaborative Development.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web personalization, Search Engines, User interests.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The improvements in the internet infrastructure and the increased affordability has led to an increase in the number of users on this platform. This has put a large impact on the services being offered on this platform especially on the e-commerce websites. These websites cater to the individual and the increased number of users has led to an increase in customer data. This data is highly valuable as it can allow for the effective prediction of customer behavior. Therefore, these predictions can allow for effective and accurate product recommendations based on the interests and the behavior of the customer. To achieve this approach, this research article analyzes a collection of related works based on the paradigm of product recommendation. After a thorough analysis, an improved product recommendation system is devised through the effective implementation of Natural Language Processing and machine learning algorithms. The proposed methodology performs preprocessing, Bag of Words, and TF-IDF along with Fuzzy Artificial Neural Networks and Collaborative Filtering to achieve an effective Product Recommendation system. This approach will be expanded further in the upcoming researches.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Natural Language Processing, Fuzzy Artificial Neural Networks and Collaborative Filtering

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A wide range of conveniences for the users have been enabled in the recent past of wireless technology. The hungry for wireless connectivity has increased much larger than the businessfield and has entered the consumer market. At present, the wireless WPAN and LAN technology cannot meet theneeds of the future connectivity of such a host of emerging electronic devices that need higher bandwidth. Ultra wide band wireless communication offers a completely different approach to wireless communication. It is a cost effective technology which brings the convenience and potential of wireless communication to high speed interconnects in devices. It is designed for short range, person area networks and is the leading technology for getting people from wires. Globally the interest in this technology is huge and has been described as one of the most promising technologies of our times.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

UWB,Wireless Technology, Positioning System.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Every country is having its economic source. The Indian economy depends on agricultural productivity. This technique is used to improve the performance of existing technology or to develop and design new technology for the growth of plants. The proposed agenda consists of four parts. They are Image preprocessing, Segmentation of the leaf, feature extraction, and classification of diseases. The first disease region is found using the segmentation technique, then both color and texture features are extracted. Finally, the classification technique is used to detect the type of leaf disease. The proposed system can effectively detect and categorize the examined disease with an accuracy of 80.05%.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Leaf disease detection, Image segmentation, masking, feature extraction, gradient boosting classification

  License

Creative Commons Attribution 4.0 and The Open Definition