scholarly journals Survey Paper on Recommendation System for Tourist Reviews using Aspect Based Sentiment Classification

2021 ◽  
Vol 9 (VIII) ◽  
pp. 821-824
Author(s):  
Kande Trupti V
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Web Sites ◽  
Recommendation System ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Classification Methods ◽  
Survey Paper ◽  
Tourism Sector

The tourism and travel sector is trying to provide different facility using a large amount of data collected from different tourism web sites. The tourist easily retrieves to reviews, evidence of different tourists and accesses them properly. It helps tourists have made the planning of visit to tourism place. So that, a major challenge faced by tourism sector is to utilize the accumulate information for detecting tourist preferences. Unfortunately, some user's comments are irrelevant and complex for understanding and long-winded these become hard for recommendation. Aspect based sentiment classification methods have shown promise in overcome the issue. In existing not much work on aspect based sentiment with classification. Here in this paper represents a framework of aspect based sentiment classification recommendation system that will not only identify the aspects very efficiently but can perform classification task with high accuracy using machine learning algorithms. This framework helps tourists to find better tourist spot, hotels, restaurant and resort in a city, and here performance has been evaluated by conducting experiments on Yelp and foursquare real-time datasets.

scholarly journals Real Time Efficient Accident Predictor System using Machine Learning Techniques (kNN, RF, LR, DT)

2020 ◽  
Vol 10 (2) ◽  
pp. 108-111
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Learning Techniques

Real time crash predictor system is determining frequency of crashes and also severity of crashes. Nowadays machine learning based methods are used to predict the total number of crashes. In this project, prediction accuracy of machine learning algorithms like Decision tree (DT), K-nearest neighbors (KNN), Random forest (RF), Logistic Regression (LR) are evaluated. Performance analysis of these classification methods are evaluated in terms of accuracy. Dataset included for this project is obtained from 49 states of US and 27 states of India which contains 2.25 million US accident crash records and 1.16 million crash records respectively. Results prove that classification accuracy obtained from Random Forest (RF) is96% compared to other classification methods.

scholarly journals Tourism web app with Aspect Based Sentiment Classification Framework for Tourist Review

2021 ◽  
Vol 9 (11) ◽  
pp. 1452-1455
Author(s):  
Prof S. S. Khartad
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
High Accuracy ◽  
Sentiment Classification ◽  
Sources Of Information ◽  
Tourist Destinations ◽  
Classification Framework ◽  
Classification Tasks ◽  
Web App ◽  
Real World Datasets

Abstract: According to studies, current tourism recommendation systems make false recommendations that do not live up to tourist expectations. Among The majority of these systems are inefficient, which is one of the main causes of the problem. A recommendation system that incorporates user feedback element.Tourist reviews are sources of information for travellers interested in learning more about tourist destinations. Regrettably, some reviews are irrelevant, resulting in noisy statistics. Sentiment categorization algorithms based on aspects have showed potential in reducing noise. We proposed a framework for sentiment classification based on aspects that can not only detect aspects quickly but also execute classification tasks with high accuracy. The framework has been deployed to assists travellers in finding the best restaurant or lodging in a city, and its performance has been evaluated with outstanding results using real-world datasets. Keywords: Pre-processing, Classifier algorithm, Feature extraction NLP, Tourism Strategy,Machine Learning, Tourist Reviews, Aspect Based Sentiment Analysis etc.

scholarly journals False Positive RFID Detection Using Classification Models

2019 ◽  
Vol 9 (6) ◽  
pp. 1154 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Bohan Yoon ◽  
Jongtae Rhee
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Real Time ◽  
Outlier Detection ◽  
Radio Frequency Identification ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Automated Identification ◽  
Rfid Data

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

Personnel Real Time Tracking in Hazardous Areas Using Wearable Technologies and Machine Learning

2021 ◽  
Author(s):  
Tareq Aziz AL-Qutami ◽  
Fatin Awina Awis
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Communication Systems ◽  
Average Distance ◽  
Location Estimation ◽  
Machine Learning Algorithms ◽  
Contact Tracing ◽  
Localization Algorithm ◽  
Positioning Systems ◽  
Wearable Technologies

Abstract Real-time location information is essential in the hazardous process and construction areas for safety and emergency management, security, search and rescue, and even productivity tracking. It's also crucial during pandemics such as the COVID-19 pandemic for contact tracing to isolate those who came to the proximity of infected individuals. While global positioning systems (GPS), can address the demand for location awareness in outdoor environments, another accurate location estimation technology for indoor environments where GPS doesn't perform well is required. This paper presents the development and deployment of an end-to-end cost-effective real-time personnel location system suitable for both indoor and outdoor hazardous and safe areas. It leverages on facility wireless communication systems, wearable technologies such as smart helmets and wearable tags, and machine learning. Personnel carries the client device which collects location-related information and sends it to the localization algorithm in the cloud. When the personnel moves, the tracking dashboard shows client location in real-time. The proposed localization algorithm relies on wireless signal fingerprinting and machine learning algorithms to estimate the location. The machine learning algorithm is a mix of clustering and classification that was designed to scale well with bigger target areas and is suitable for cloud deployment. The system was tested in both office and industrial process environments using consumer-grade handphones and intrinsically safe wearable devices. It achieved an average distance error of less than 2 meters in 3D space.

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