Bio-medical application on predicting systolic blood pressure using neural networks

This paper presents a new study based on artificial neural network, which is a typical technique for processing big data, for the prediction of systolic blood pressure by correlated factors (gender, serum cholesterol, fasting blood sugar and electrocardiography signal). Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the bio-medical prediction system. The database of raw data is divided into two parts: 80% for training the neural network and the remaining 20% for testing the performance. The experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This novel method of predicting systolic blood pressure contributes to giving early warnings to adults who may not take regular blood pressure measurements. Also, as it is known that an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff.published_or_final_versio

Advancing Road Safety: Pothole Detection Using Yolov8 And Wandb Deep Learning

Self-driving vehicles have emerged as a revolutionary breakthrough in modern transportation, promising unparalleled safety, efficiency, and convenience.  However, navigating through unpredictable road conditions, especially in the presence of potholes, remains a significant challenge that poses potential safety risks. This study introduces an innovative cloud-powered next-generation self-driving safety system that harnesses the power of AI, specifically YOLOv8 (You Only Look Once version 8), in conjunction with the wandb (Weights & Biases) deep learning platform. This integration enables pothole detection and advanced navigation, elevating the safety standards of autonomous driving. The selection of YOLOv8, a cutting-edge  object detection model, is by its exceptional accuracy and speed. YOLOv8 employs a singular neural network to predict object bounding boxes and class probabilities directly, allowing for rapid and precise object detection. This cloud-based architecture also supports continuous model updates and refinements, ensuring the system's adaptability to evolving road conditions and pothole variations. With potential applications extending beyond potholes, this system paves the way for safer and more reliable autonomous transportation, revolutionizing the landscape of self-driving technology

Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

The Challenges in Developing Smart Tourism: A Literature Review

Smart tourism is experiencing rapid development along with the information and communication technology (ICT) revolution. ICT has enabled companies engaged in tourism to be "smarter" in improving their performance and competitiveness. This study aims to determine the challenges in developing smart tourism. This study uses a literature review method. The data analyzed were obtained from several articles in accordance with the research theme. The selected articles are reputable articles that have been indexed by Scopus. After filtering the articles to be analyzed, 31 articles were selected according to the research theme raised. The results of this study indicate that apart from having many benefits, the development of smart tourism also has many challenges, such as, it requires a lot of money, many tourists and people are not aware of the new technology, reduced need for human resources, etc. These challenges must be considered by stakeholders so that the development of smart tourism can run smoothly and be accepted by the community

An Application Of Machine Learning With Boruta Feature Selection To Improve NO2 Pollution Prediction

Projecting and monitoring NO2 pollutants' concentration is perhaps an efficient and effective technique to lower people's exposure, reducing the negative impact caused by this harmful atmospheric substance. Many studies have been proposed to predict NO2 Machine learning (ML) algorithm using a diverse set of data, making the efficiency of such a model dependent on the data/feature used. This research installed and used data from 14 Internet of thing (IoT) emission sensors, combined with weather data from the UK meteorology department and traffic data from the department for transport for the corresponding time and location where the pollution sensors exist. This paper select relevant features from the united data/feature set using Boruta Algorithm. Six out of the many features were identified as valuable features in the NO2 ML model development. The identified features are Ambient humidity, Ambient pressure, Ambient temperature, Days of the week, two-wheeled vehicles(counts), cars/taxis(counts). These six features were used to develop different ML models compared with the same ML model developed using all united data/features. For most ML models implemented, there was a performance improvement when developed using the features selected with Boruta Algorithm

Autentikasi User Dengan Metode Single Sign-On Berbasis Windows Active Directory Pada PT. XYZ

To connect to the company network, a WPA2-PSK-based security system is in place, requiring users to input the company Wi-Fi password. One of the issues that arises is the difficulty in identifying the status of users attempting to access the network. Apart from security concerns regarding the network, the use of this security key allows non-employees to connect to the network using personal devices. As a solution to enhance the existing authentication system, security systems like RADIUS can be utilized. This system operates to mitigate threats to the network security. The process undertaken through the implementation of this NDLC method commences with identifying and designing network security authentication, progressing through the implementation phase until the design can be regularly utilized. With the introduction of a user authentication system employing a single sign-on method based on Windows Active Directory at XYZ Inc., users will find it easier to connect to the wireless network. With WPA2-Enterprise, access to the network will be restricted.Untuk terhubung ke dalam jaringan perusahaan terdapat sistem keamanan berbasis WPA2-PSK sehingga user diwajibkan memasukan password wifi perusahaan. Salah satu isu yang muncul adalah kesulitan dalam mengidentifikasi status user yang berupaya mengakses jaringan. Selain permasalahan dari segi keamanan jaringan, dengan pengunaan key security tersebut user selain karyawan dapat terhubung ke jaringan menggunakan perangkat pribadi. Sebagai salah satu solusi untuk memperbaiki sistem autentikasi yang ada pada saat ini, terdapat sistem keamanan yang dapat dimanfaatkan seperti RADIUS. Sistem dijalankan untuk menghindari ancaman pada sistem keamanan jaringan. Proses yang dilakukan dengan menerapkan metode NDLC ini dimulai dengan mengidentifikasi dan melakukan perancangan autentikasi keamanan pada jaringan sampai ke tahap implementasi rancangan tersebut hingga dapat digunakan secara rutin. Dengan adanya sistem autentikasi user dengan metode single sign-on berbasis Windows active directory pada PT. XYZ user akan lebih mudah untuk terhubung ke dalam jaringan wireless. Dengan WPA2 - Enterprise akses untuk masuk ke dalam jaringan akan terbatas

PROV-TE: A Provenance-Driven Diagnostic Framework for Task Eviction in Data Centers

Cloud Computing allows users to control substantial computing power for complex data processing, generating huge and complex data. However, the virtual resources requested by users are rarely utilized to their full capacities. To mitigate this, providers often perform over-commitment to maximize profit, which can result in node overloading and consequent task eviction. This paper presents a novel framework that mines the huge and growing historical usage data generated by Cloud data centers to identify the causes of overloads. Provenance modelling is applied to add contextual meaning to the data, and the PROV-TE diagnostic framework provides algorithms to efficiently identify the causality of task eviction. Using simulation to reflect real world scenarios, our results demonstrate a precision and recall of the diagnostic algorithms of 83% and 90% respectively. This demonstrates a high level of accuracy of the identification of causes

Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

Era big data menjadi sebuah fenomena yang menarik untuk di bahas oleh kalangan peneliti dan pengembang perangkat lunak, pengembangan aplikasi dan konsep pengelolaan data semakin banyak varian dan dukungan menjadikan kerangka big data dapat masuk kesetiap lini kehidupan, data yang tersusun baik secara singkronus maupun asingkronus, melibatkan mesin dan manusia dalam pengumpulan data menjadikan teknologi ini semakin sejalan dengan konsep Revolusi Industri 4.0 Dalam berbagai kajian di sajikan konsep dan kerangka kerja Big Data, dari kajian tersebut beberapa peneliti menyajikan lapisan dalam arsitektur Big Data, di mana masing masing lapisan memberi input bagi lapisan lain untuk dapat di olah menjadi bentuk yang siap saji di masyarakat, lapisan yang tediri dari pengumpulan data, penyimpanan data, pemrosesan data serta Analisa data, sehingga pada lapisan aplikasi penggunaan data dapat lebih maksimal di rasakan oleh pengguna. Dalam makalah ini di sajikan beberapa bahan studi literature yang di rangkum untuk mendapatkan penjelasan mengenai lapisan arsitektur Big Data yang dapat di kembagkan dan di terapkan pada bidang bidang penelitian lain