Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

Design And Implementation Of An Autonomous Wireless Sensor-Based Smart Home

The Smart home has gained widespread attentions due to its flexible integration into everyday life. This next generation of green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. It can integrate diversified physical sensed information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. Although smart homes are gaining popularity due to their energy saving and better living benefits, there is no standardized design for smart homes. In this thesis, a smart home design is put forward that can classify and predict the state of the home utilizing historical data of the home. A wireless sensor network was setup in a home to gather and send data to a sink node. The collected data was utilized to train and test a classification model achieving high accuracy with Support Vector Machine (SVM). SVM was further utilized as a predictor of future home states. Based on the data collection, classification and prediction models, a system was designed that can learn, run with minimal human supervision and detect anomalies in a home. The aforementioned attributes make the system an asset for senior care scenarios

Towards Forklift Safety in a Warehouse: An Approach Based on the Automatic Analysis of Resource Flows

Warehouse management is a discipline that has gained importance in recent decades. In the era of the Digital Revolution and Industry 5.0, to enable a company to attain a competitive advantage, it is necessary to identify smart improvement tools that help search for warehouse problems and solutions. A good tool to highlight issues related to layout and resource flows is the spaghetti chart which, besides being used to minimize waste according to lean philosophy, can also be used to assess warehouse safety and reliability and improve the plant sustainability. This article shows how to exploit “smart spaghetti” (spaghetti chart automatically generated by smart tracking devices) to conceive improvements in the layout and work organization of a warehouse, reducing the risk of collision between forklifts and improving the operators’ safety. The methodology involves automatically mapping the spaghetti charts (searching for critical areas where the risk of collision is high) and identifying interventions to be carried out to avoid near misses. “Smart spaghetti” constitutes a valuable decision support tool to identify potential improvements in the system through changes in the layout or in the way activities are performed. This work shows an application of the proposed technique in a pharmaceutical warehouse

Sentiment Analysis of Customer Feedback in Online Food Ordering Services

Background: E-commerce websites have been established expressly as useful online communication platforms, which is rather significant. Through them, users can easily perform online transactions such as shopping or ordering food and sharing their experiences or feedback. Objectives: Customers\u27 views and sentiments are also analyzed by businesses to assess consumer behavior or a point of view on certain products or services. Methods/Approach: This research proposes a method to extract customers\u27 opinions and analyse sentiment based on a collected dataset, including 236,867 online Vietnamese reviews published from 2011 to 2020 on foody.vn and diadiemanuong.com. Then, machine learning models were applied and assessed to choose the optimal model. Results: The proposed approach has an accuracy of up to 91.5 percent, according to experimental study findings. Conclusions: The research results can help enterprise managers and service providers get insight into customers\u27 satisfaction with their products or services and understand their feelings so that they can make adjustments and correct business decisions. It also helps food e-commerce managers ensure a better e-commerce service design and delivery