International Journal of Ambient Computing and Intelligence 

About: International Journal of Ambient Computing and Intelligence is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Computer science & Ambient intelligence. It has an ISSN identifier of 1941-6237. Over the lifetime, 283 publications have been published receiving 3580 citations. The journal is also known as: Ambient computing and intelligence & JACI.

Cloud Computing Security

Abstract: In this paper, the authors focus on Cloud Computing, which is a distributed architecture that centralizes server resources on quite a scalable platform so as to provide on demand' computing resources and services The authors outline what cloud computing is, the various cloud deployment models and the main security risks and issues that are currently present within the cloud computing industry.

An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

Abstract: This article describes ALZ-MAS; an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. The system makes use of several context-aware technologies that allow it to automatically obtain information from users and the environment in an evenly distributed way, focusing on the characteristics of ubiquity, awareness, intelligence, mobility, etc., all of which are concepts defined by Ambient Intelligence.

Improving Privacy and Security of User Data in Location Based Services

Abstract: Location Based Services (LBS) expose user data to malicious attacks. Approaches, evolved, so far, for preserving privacy and security, suffer from one or more anomalies, and hence the problem of securing LBS data is far from being resolved. In particular, accuracy of results vs. privacy degree, privacy vs. performance, and trust between users are open problems. In this article, we present a novel approach by integration of peer-to-peer (P2P) with the caching technique and dummies from real queries. Our approach increases efficiency, leads to improved performance, and provides solutions to many problems that have existed in the past. In addition, we offer an improved way of managing cache. Simulation demonstrates superiority of our approach over earlier ones dealing with both the ratio of privacy and that of performance.

A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images: A Decision Support System for Medical Renal Diseases

Abstract: Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature vector and GLCM concatenated feature vector. The present work explores the potential of five texture feature vectors computed using GLCM statistics exhaustively for differential diagnosis between normal and MRD images using SVM classifier. The result of the study indicates that GLCM range feature vector computed with d = 1 yields the highest overall classification accuracy of 85.7% with individual classification accuracy values of 93.3% and 77.9% for normal and MRD classes respectively.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Abstract: Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.