Investigating rendering speed and download rate of three-dimension (3D) mobile map intended for navigation aid using genetic algorithm

Prior studies have shown that rendering 3D map dataset in mobile device in a wireless network depends on the download speed. Crucial to that is the mobile device computing resource capabilities. Now it has become possible with a wireless network to render large and detailed 3D map of cities in mobile devices at interactive rates of over 30 frame rate per second (fps). The information in 3D map is generally limited and lack interaction when it’s not rendered at interactive rate; on the other hand, with high download rate 3D map is able to produce a realistic scene for navigation aid. Unfortunately, in most mobile navigation aid that uses a 3D map over a wireless network could not serve the needs of interaction, because it suffers from low rendering speed. This paper investigates the trade-off between rendering speed and download rate of the 3D mobile map using genetic algorithm (GA). The reason of using GA is because it takes larger problem space than other algorithms for optimization, which is well suited for establishing fast 3D map rendering speed on-the-fly to the mobile device that requires useful solutions for optimization. Regardless of mobile device’s computing resources, our finding from GA suggest that download rate and rendering speed are mutually exclusive. Thus, manipulated static aerial photo-realistic images instead of 3D map are well-suited for navigation aid

Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

SnapShare: AI Trained Mobile App to Share Snaps Automatically

These days people take more than 1 million group or selfie photos per day. This goes very hectic for a mobile owner to identify photos of each individual and send them their photos separately. Sharing photos create extra burden for mobile owners. There are fewer applications available (i.e., 23Snaps, Cluster, Path, letmesee) to share photos with small circle of friends. Unfortunately, these developed apps require user’s interaction to identify individuals in the photo. This study proposes a SnapShare mobile application that uses Face Recognition Algorithms to classify individuals in the photos and automatically shares photos with recognized individuals. SnapShare basically uses Deep learning (DL) and Machine Learning (ML) techniques for Face Recognition from the captured images. Based on the results, the developed system achieves the standard performance accuracy (i.e., >90%). The aim of the SnapShare is to create comfort for mobile owners and people visible in-group photo to share and access photo automatically. Furthermore, SnapShare also facilitates user to back up their photo gallery on server storage

Klasifikasi Sentimen Ulasan Pengguna Aplikasi PeduliLindungi di Google Play Menggunakan Algoritma Support Vector Machine dengan Seleksi Fitur Chi-Square

Upaya pemerintah untuk mengurangi penyebaran wabah virus corona yang semakin meluas hampir di setiap negara di dunia termasuk di Indonesia telah banyak dilakukan. Salah satu upaya yang telah dilakukan dengan memanfaatkan teknologi yang ada pada saat ini adalah membuat sebuah aplikasi bernama PeduliLindungi. Aplikasi ini bertujuan untuk melakukan tracing dan monitoring lokasi penyebaran virus corona sehingga dapat menurunkan kasus corona di Indonesia. Banyak ulasan yang diberikan oleh masyarakat terhadap aplikasi ini baik yang berupa kritik maupun kepuasan. Namun, untuk mengetahui seluruh ulasan yang diberikan tidak mudah. Oleh sebab itu, penelitian dilakukan dengan tujuan untuk mengetahui hasil sentimen masyarakat terhadap aplikasi PeduliLindungi. Analisis sentimen yang dilakukan dengan mengklasifikasikan ulasan menjadi ulasan positif dan ulasan negatif menggunakan algoritma Support Vector Machine dengan seleksi fitur chi-square. Pengumpulan data ulasan dilakukan dengan melakukan scrapping di google play dengan menggunakan bahasa pemrograman Python. Hasil dari klasifikasi sentimen terhadap aplikasi PeduliLindungi menghasilkan performa yang baik dan menghasilkan nilai akurasi sebesar 93%, recall sebesar 86%, precision sebesar 98%, specificity sebesar 98% dan f1-score sebesar 92%

Sentiment analysis of impact of technology on employment from text on twitter

Various studies are in progress to analyze the content created by the users on social media due to its influence and the social ripple effect. The content created on social media has pieces of information and the user’s sentiments about social issues. This study aims to analyze people’s sentiments about the impact of technology on employment and advancements in technologies and build a machine learning classifier to classify the sentiments. People are getting nervous, depressed, and even doing suicides due to unemployment; hence, it is essential to explore this relatively new area of research. The study has two main objectives 1) to preprocess text collected from Twitter concerning the impact of technology on employment and analyze its sentiment, 2) to evaluate the performance of machine learning Naïve Bayes (NB) classifier on the text. To achieve this, a methodology is proposed that includes 1) data collection and preprocessing 2) analyze sentiment, 3) building machine learning classifier and 4) compare the performance of NB and support vector machine (SVM). NB and SVM achieved 87.18% and 82.05% accuracy, respectively. The study found that 65% of people hold negative sentiment regarding the impact of technology on employment and technological advancements; hence, people must acquire new skills to minimize the effect of structural unemployment

Klasifikasi kinerja karyawan berbasis Support Vector Machine menggunakan Screen Monitor Duration

Banyaknya karyawan pada suatu perusahaan akan membuat pemilik perusahaan kesulitan untuk melakukan monitoring dan mengukur kinerja seluruh karyawannya terlebih saat karyawan bekerja dari rumah. Untuk mengatasi masalah tersebut, maka diperlukan adanya pemanfaatan teknologi yang telah berkembang pada saat ini dalam mengatasi masalah monitoring dan mengukur kinerja karyawan. Dalam memecahkan masalah tersebut aplikasi screen monitor duration berbasis Support Vector Machine (SVM) dapat membantu para pemilik perusahaan dalam menjawab masalah. Berdasarkan hasil penelitian yang telah dilakukan, secara garis besar SVM digunakan untuk mengklasifikasikan kinerja karyawan ke dalam kategori produktif atau tidak produktif melalui aplikasi yang dikembangkan. Kemudian metode SVM ini dibandingkan dengan metode Support Vector Regression (SVR) untuk mengetahui metode terbaik dalam klasifikasi. Untuk mengetahui hasil akhir ditentukan bahwa klasifikasi terbaik adalah menggunakan metode SVM dibuktikan dengan nilai pengujian Root Mean Square Error (RMSE) sebesar 0.4299697 untuk SVM dan sebesar 0.7159644 untuk SVR. Dengan begitu penelitian ini dapat menjawab masalah utama tentang bagaimana cara mengontrol dan monitoring para karyawan, sehingga para pemilik perusahaan dapat mengetahui kinerja karyawan

A Support Vector Machine Classification of Computational Capabilities of 3D Map on Mobile Device for Navigation Aid

3D map for mobile devices provide more realistic view of an environment and serves as better navigation aid. Previous research studies shows differences in 3D maps effect on acquiring of spatial knowledge. This is attributed to the differences in mobile device computational capabilities. Crucial to this, is the time it takes for 3D map dataset to be rendered for a required complete navigation task. Different findings suggest different approach on solving the problem of time require for both in-core (inside mobile) and out-core (remote) rendering of 3D dataset. Unfortunately, studies on analytical techniques required to shows the impact of computational resources required for the use of 3D map on mobile device were neglected by the research communities. This paper uses Support Vector Machine (SVM) to analytically classify mobile device computational capabilities required for 3D map that will be suitable for use as navigation aid. Fifty different Smart phones were categorized on the bases of their Graphical Processing Unit (GPU), display resolution, memory and size. The result of the proposed classification shows high accuracy</jats:p