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Ambient Assisted Living: Scoping Review of artificial intelligence Models, Domains, Technology, and Concerns

JOURNAL OF MEDICAL INTERNET RESEARCH 2022年第11期24卷 e36553页

作者： Jovanovic, Mladjan Mitrov, Goran Zdravevski, Eftim Lameski, Petre Colantonio, Sara Kampel, Martin Tellioglu, Hilda Florez-Revuelta, Francisco Singidunum Univ Dept Comp Sci Danijelova 32 Belgrade 11000 Serbia Univ St Cyril & Methodius Fac Comp Sci & Engn Skopje North Macedonia Natl Res Council Italy Inst Informat Sci & Technol Signals & Images Lab Pisa Italy Vienna Univ Technol Fac Informat Vienna Austria Univ Alicante Dept Comp Technol Alicante Spain

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in A.L systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assist

关键词： ambient assisted living AAL assisted living active living digital health digital well-being automated learning approach artificial intelligence algorithms human-centered AI review implications artificial intelligence mobile phone

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The Deep Neural Network Compilers for FPGAs: A Survey 3

The Deep Neural Network Compilers for FPGAs: A Survey

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Tian, Jing Shi, Tianjie Zhao, Yixuan Liu, Feiyang Xi'An Aeronautics Computing Technique Research Institute AVIC Xi'an China

ISBN: (纸本)9798350359961

In embedded systems, the low-latency, low-power consumption, and reconfigurable FPGA component undertakes the inference operations of a large number of computationally intensive tasks, such as artificial intelligence algorithms, making the deep neural network compiler for FPGAs a hot topic. This FPGA compiler can automatically optimize the model calculation diagrams and hardware structure design, and can achieve the end-To-end deployment of neural network models, efficiently and rapidly. This paper summarizes the general framework structure of the neural network compiler for FPGAs, focuses on the development of three representative compilers, compares the deployment performance of specific algorithms on the FPGA platform, summarizes the development advantages and limitations, and finally proposes its application prospects and important research directions in embedded systems, providing a theoretical basis for the rapid iteration and deployment of artificial intelligence algorithms in embedded computing systems. © 2023 IEEE.

关键词： algorithm deployment artificial intelligence algorithms compilers deep neural network FPGA

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Separation of color channels from conventional colonoscopy images improves deep neural network detection of polyps

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JOURNAL OF BIOMEDICAL OPTICS 2021年第1期26卷 015001页

作者： Lai, Lily L. Blakely, Andrew Invernizzi, Marta Lin, James Kidambi, Trilokesh Melstrom, Kurt A. Yu, Kevin Lu, Thomas City Hope Natl Med Ctr Dept Surg Duarte CA 91010 USA NCI Dept Surg Natl Inst Hlth Campus Bethesda MD 20892 USA City Hope Natl Med Ctr Div Gastroenterol Duarte CA USA Jet Prop Lab Pasadena CA USA

Significance: Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination. Aim: To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE). Approach: Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps. Results: The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images. Conclusions: The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

关键词： artificial intelligence algorithms deep learning polyp discrimination colorectal cancer narrow-band imaging color channel separation

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Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

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MATERIALS 2023年第8期16卷 3220-3220页

作者： Saldana, Manuel Galvez, Edelmira Navarra, Alessandro Toro, Norman Cisternas, Luis A. Univ Arturo Prat Fac Engn & Architecture Iquique 1110939 Chile Univ Antofagasta Dept Ingn Quim & Proc Minerales Antofagasta 1270300 Chile Univ Catolica Norte Dept Met & Min Engn Ave Angamos 0610 Antofagasta 1270709 Chile McGill Univ Dept Min & Mat Engn 3610 Univ St Montreal PQ H3A 0C5 Canada

Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process's productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.

关键词： SAG mill comminution processes artificial intelligence algorithms modeling optimization mineral processing

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Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms

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AGRICULTURAL WATER MANAGEMENT 2023年 280卷

作者： Di Nunno, Fabio Granata, Francesco Univ Cassino & Southern Lazio Dept Civil & Mech Engn DICEM Via Biasio 43 I-03043 Cassino Frosinone Italy

In years of increasing impact of climate change effects, a reliable characterization of the spatiotemporal evolutionary dynamics of evapotranspiration can enable a significant improvement in water resource management, especially as regards irrigation activities. Sicily, an insular region of Southern Italy, has exceptionally valuable agricultural production and high irrigation needs. In this study, the ETo reference evapotranspiration in Sicily was first evaluated on the basis of historical and future climate parameters, referring for future values to two climate scenarios characterized by different Representative Concentration Pathways: RCP 4.5 and RCP 8.5. Then, the Hierarchical algorithm was used to divide Sicily into three homogeneous regions, each characterized by specific ETo features. In addition, some Machine Learning (ML) algorithms were used to develop forecasting models based on only historical data. Support Vector Regression (SVR) was used to predict the future values of Tmin and Tmax, while an ensemble model based on Multilayer Perceptron (MLP) and M5P Regression Tree was developed for the ETo forecasting. Predictions made with the ensemble MLP-M5P model were compared with the ETo computed for the RCP 4.5 and RCP 8.5 future climate scenarios. During the forecast period, from 2001 to 2091, evapotranspiration increases were observed for all three clusters. For cluster C1, along the coast, percentage increases of 7.52%, 14.64% and 10.78%, were computed for RCP 4.5, RCP 8.5, and MLP-M5P, respectively, while, for cluster C3, in the inland, percentage increases were higher and equal to 8.12%, 16.71%, and 14.98%, respectively. The ensemble MLP-M5P model led to intermediate trends between RCP 4.5 and RCP 8.5, showing a high correlation with the latter (R2 between 0.93 and 0.98). The developed approach, based on both clustering and forecasting algorithms, provided a comprehensive analysis of the reference evapotranspiration, with the detection of the different ho

关键词： Climate change artificial intelligence algorithms Clustering Ensemble models Mediterranean climate Irrigation planning

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A mixing algorithm of ACO and ABC for solving path planning of mobile robot

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Li, Guangxin Liu, Chao Wu, Lei Xiao, Wensheng China Univ Petr East China Natl Engn Lab Offshore Geophys & Explorat Equipmen Qingdao 266580 Peoples R China China Univ Petr East China Sch Elect & Mech Engn Qingdao 266580 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore

Path planning is involved in many applications such as trajectory planning, mobile robotics, pipeline layout, etc. Researchers use artificial intelligence algorithms to solve path planning efficiently, among which the ant colony algorithm (ACO) is one of the common intelligent algorithms to solve path planning problems. However, the traditional ACO has defects such as low early search efficiency and easy to fall into local optimum, while the artificial bee colony algorithm (ABC) has high search efficiency. Therefore, an improved ant colony optimization artificial bee colony algorithm (IACO-IABC) is proposed in this study. IACO-IABC contains three mechanisms. First, the heuristic mechanism with directional information for the ACO is improved to enhance the efficiency of steering towards the target direction. Secondly, the novel neighborhood search mechanism of the employed bee and the onlooker bee in the ABC is presented to enhance the exploitation of optimal solutions. Then, the path optimization mechanism is introduced further to reduce the number of turn times in the planned path. To verify the performance of the IACO-IABC, a series of experiments are conducted with 10 different maps. The experiments compare nine variants of ACO and eight commonly used intelligent search algorithms, and the results show the advantages of the IACO-IABC in reducing the number of turn times and path lengths and enhancing the convergence speed of the algorithm. Compared to the best results of other algorithms, the average improvement percentages of the proposed algorithm in terms of the path turn times are 375%, 258.33%, 483.33%, 186.67%, 166.77% and 255.33%, further demonstrating the ability of IACO-IABC to obtain high-quality path planning result.

关键词： Path planning Mobile robot artificial intelligence algorithms Ant colony optimization artificial bee colony

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How Clinicians Perceive artificial intelligence-Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach

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JOURNAL OF MEDICAL INTERNET RESEARCH 2021年第12期23卷 e33540页

作者： Hah, Hyeyoung Goldin, Deana Shevit Florida Int Univ Coll Business Informat Syst & Business Analyt 11200 SW 8th St Miami FL 33199 USA Florida Int Univ Nicole Wertheim Coll Nursing & Hlth Sci Miami FL 33199 USA

Background: With the rapid development of artificial intelligence (AI) and related technologies, AI algorithms are being embedded into various health information technologies that assist clinicians in clinical decision making. Objective: This study aimed to explore how clinicians perceive AI assistance in diagnostic decision making and suggest the paths forward for AI-human teaming for clinical decision making in health care. Methods: This study used a mixed methods approach, utilizing hierarchical linear modeling and sentiment analysis through natural language understanding techniques. Results: A total of 114 clinicians participated in online simulation surveys in 2020 and 2021. These clinicians studied family medicine and used AI algorithms to aid in patient diagnosis. Their overall sentiment toward AI-assisted diagnosis was positive and comparable with diagnoses made without the assistance of AI. However, AI-guided decision making was not congruent with the way clinicians typically made decisions in diagnosing illnesses. In a quantitative survey, clinicians reported perceiving current AI assistance as not likely to enhance diagnostic capability and negatively influenced their overall performance (beta=-0.421, P=.02). Instead, clinicians' diagnostic capabilities tended to be associated with well-known parameters, such as education, age, and daily habit of technology use on social media platforms. Conclusions: This study elucidated clinicians' current perceptions and sentiments toward AI-enabled diagnosis. Although the sentiment was positive, the current form of AI assistance may not be linked with efficient decision making, as AI algorithms are not well aligned with subjective human reasoning in clinical diagnosis. Developers and policy makers in health could gather behavioral data from clinicians in various disciplines to help align AI algorithms with the unique subjective patterns of reasoning that humans employ in clinical diagnosis.

关键词： artificial intelligence algorithms AI diagnostic capability virtual care multilevel modeling human-AI teaming natural language understanding

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Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors

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LABORATORY INVESTIGATION 2023年第12期103卷 100257页

作者： Vazzano, Jennifer Johansson, Dorota Hu, Kun Euren, Kristian Elfwing, Stefan Parwani, Anil Zhou, Ming Ohio State Univ Wexner Med Ctr Dept Radiol Columbus OH 43210 USA Inify Labs AB Stockholm Sweden Tufts Univ Tufts Med Ctr Sch Med Dept Pathol Boston MA 02111 USA

Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histo-pathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm's annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity

关键词： artificial intelligence algorithms digital pathology preanalytical factors prostate cancer whole slide imaging

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Leveraging artificial intelligence to Improve Voice Disorder Identification Through the Use of a Reliable Mobile App

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IEEE ACCESS 2019年 7卷 124048-124054页

作者： Verde, Laura De Pietro, Giuseppe Alrashoud, Mubarak Ghoneim, Ahmed Al-Mutib, Khaled N. Sannino, Giovanna CNR Inst High Performance Comp & Networking ICAR I-80131 Naples Italy King Saud Univ CCIS Dept Software Engn SWE Riyadh 11543 Saudi Arabia

The evolution of the Internet of Things, cloud computing and wireless communication has contributed to an advance in the interconnectivity, efficiency and data accessibility in smart cities, improving environmental sustainability, quality of life and well-being, knowledge and intellectual capital. In this scenario, the satisfaction of security and privacy requirements to preserve data integrity, confidentiality and authentication is of fundamental importance. In particular, this is essential in the healthcare sector, where health-related data are considered sensitive information able to reveal confidential details about the subject. In this regard, to limit the possibility of security attacks or privacy violations, we present a reliable mobile voice disorder detection system capable of distinguishing between healthy and pathological voices by using a machine learning algorithm. This latter is totally embedded in the mobile application, so it is able to classify the voice without the necessity of transmitting user data to or storing user data on any server. A Boosted Trees algorithm was used as the classifier, opportunely trained and validated on a dataset composed of 2003 voices. The most frequently considered acoustic parameters constituted the inputs of the classifier, estimated and analyzed in real time by the mobile application.

关键词： Voice disorders smart healthcare monitoring artificial intelligence algorithms smart cities security privacy

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Hybrid artificial intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

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IEEE ACCESS 2019年 7卷 162530-162548页

作者： Topal, Ibrahim Ucar, Muhammed Kursad Natl Def Univ Dept Comp Technol TR-77720 Yalova Turkey Natl Def Univ Vocat Sch TR-77720 Yalova Turkey Sakarya Univ Fac Engn Dept Elect & Elect Engn TR-54050 Sakarya Turkey

Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as "Turkey Tourism Year" in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.7

关键词： Marketing tourism travel user profile turkey tourism year Chinese tourism tripadvisor artificial intelligence algorithms ensemble classification

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