This study presents a method of aspect-based sentiment analysis for customer reviews related to hotels. The considered hotel aspects are staff attentiveness, room cleanliness, value for money and convenience of location. The proposed method consists of two main components. The first component is used to assemble relevant sentences for each hotel aspect into relevant clusters of hotel aspects using BM25. We developed a corpus of keywords called the Keywords of Hotel Aspect (KoHA) Corpus, and the keywords of each aspect were used as queries to assemble relevant sentences of each hotel aspect into relevant clusters. Finally, customer review sentences in each cluster were classified into positive and negative classes using sentiment classifiers. Two algorithms, Support Vector Machines (SVM) with a linear and a RBF kernel, and Convolutional Neural Network (CNN) were applied to develop the sentiment classifier models. The model based on SVM with a linear kernel returned better results than other models with an AUC score of 0.87. Therefore, this model was chosen for the sentiment classification stage. The proposed method was evaluated using recall, precision and F1 with satisfactory results at 0.85, 0.87 and 0.86, respectively. Our proposed method provided an overview of customer feelings based on score, and also provided reasons why customers liked or disliked each aspect of the hotel. The best model from the proposed method was used to compare with a state-of-the-art model. The results show that our method increased recall, precision, and F1 scores by 2.44%, 2.50% and 1.84%, respectively.

One of the most significant current discussions in Emergency Medical Service (EMS) is when an accident occurs and someone is injured, or when patients with acute cardiac arrest patients need to call the ambulance for immediate assistance. A major problem with this kind of situation is ambulance or rescue do not know the exact location of the victim, waste time in searching for victims which sometimes causes could not save their life. Another problem found that sometime when the rescue came, there was no AED (Automated External Defibrillator) with them, which if the rescue knew where the AED is, can go to get the AED to save the patient's life in a timely manner. Therefore, the objectives of this research are to develop the emergency platform that monitors the location of the AEDs and control the opening/closing of the AED cabinets through the App. Also, the user can check the status of the AED through the App. This platform also specifies the exact location of the accident via the GPS system. Further statistical tests revealed that the location of the AED has 95% accuracy, can be controlled accurately 96%, with the average response time through the App 658 ms. The results indicate that this platform supports EMS services more effective.

This paper evaluates the QoS performance of multimedia applications over wireless and wired IPv6 environment in comparison with IPv4 using the same underlying hardware. Concerning the multimedia applications, this paper discusses video streaming and Internet telephony (VoIP) because both services will be common and widely used applications by the next generation of Internet users. Our measurement results show that throughput, latency and packet loss. All performance evaluation criteria specified previously will be calculated and compared to each other.