International Journal of Hybrid Intelligent Systems - Volume 16, issue 4

Authors: Souza, Luís Fabrício | Holanda, Gabriel | Silva, Francisco Hércules | Alves, Shara Shami | Filho, Pedro Pedrosa

Article Type: Research Article

Abstract: According to the World Health Organization, severe lung pathologies bring about 250,000 deaths each year, and by 2030 it will be the third leading cause of death in the world. The usage of (CT) Computed Tomography is a crucial tool to aid medical diagnosis. Several studies, based on the computer vision area, in association with the medical field, provide computational models through machine learning and deep learning. In this study, we created a new feature extractor that works as the Mask R-CNN kernel for lung image segmentation through transfer learning. Our approaches minimize the number of images used by CNN’s …training step, thereby also decreasing the number of interactions performed by the network. The model obtained results surpassing the standard results generated by Mask R-CNN, obtaining more than 99% about the metrics of real lung position on CT with our best model Mask + SVM, surpassing methods in the literature reaching 11 seconds for pulmonary segmentation. To present the effectiveness of our approach also in the generalization of models (methods capable of generalizing machine knowledge to other different databases), we carried out experiments also with various databases. The method was able, with only one training based on a single database, to segment CT lung images belonging to another lung database, generating excellent results getting 99% accuracy. Show more

Keywords: Lung segmentation, deep learning, Mask R-CNN, feature extractor, transfer learning

DOI: 10.3233/HIS-200287

Citation: International Journal of Hybrid Intelligent Systems, vol. 16, no. 4, pp. 189-205, 2020

Authors: Roberts, Ethan | Bassett, Bruce A. | Lochner, Michelle

Article Type: Research Article

Abstract: Statistical uncertainties are rarely incorporated into machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC …can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating an algorithm’s ability to detect anomalies. Show more

Keywords: Machine learning, anomalies, classification, novelty, Bayesian, unsupervised class detection

DOI: 10.3233/HIS-200282

Citation: International Journal of Hybrid Intelligent Systems, vol. 16, no. 4, pp. 207-222, 2020

Authors: R, Varsha | Nair, Meghna Manoj | Nair, Siddharth M. | Tyagi, Amit Kumar

Article Type: Research Article

Abstract: The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s …generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system. Show more

Keywords: Trust in future vehicles, privacy preserving techniques, Blockchain technology, recommender systems, authentication

DOI: 10.3233/HIS-200289

Citation: International Journal of Hybrid Intelligent Systems, vol. 16, no. 4, pp. 223-236, 2020

Authors: Gandhi, Saurav | Goyal, Ayush

Article Type: Research Article

Abstract: As the world’s population continues to grow in size and in age, it remains vital to develop streamlined and affordable technologies to address the needs of those who require companionship and care. The elderly and visually impaired remain a significant and ever-increasing segment of society, yet due to the cost of at-home health care, many of these individuals are unable to afford full-time caretakers to assist with everyday tasks. Although the luxury of at-home care remains elusive to many due to the associated cost, social robots are an important and meaningful way to overcome this issue. This research explores how …combining the principles of biomimicry and social robotics with the Internet of Things (IoT) and artificial intelligence (AI) can enhance assistive technology. This paper proposes a novel assistive device called the Jet-I-U, which aims to support visually impaired and elderly individuals with day-to-day tasks. The Jet-I-U mimics the behavioral and physical characteristics of a pet scarlet macaw and provides both information and companionship to this demographic. The device emulates a scarlet macaw’s morphology and movement through laser-cut features and various biomimetic components. The intelligence and behavioral characteristics of the macaw are modeled by the application of IoT Edge platforms, AI, and Machine Learning (ML). By utilizing powerful off-the-shelf microcontrollers like the NVIDIA Jetson Nano and Raspberry Pi, this device is both affordable and practical. Experimental analysis was performed to evaluate Jet-I-U’s performance based on several criteria. The results demonstrated the versatility and applicability of the proposed solution as a companion to the visually impaired and elderly. Show more

Keywords: Assistive technology, biomimicry, artificial intelligence, visually impaired, social robots, internet of things

DOI: 10.3233/HIS-200290

Citation: International Journal of Hybrid Intelligent Systems, vol. 16, no. 4, pp. 237-251, 2020