Machine learning for Internet of Things data analysis: A survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.Comment: Digital Communications and Networks (2017

The Effect of Combined Herbal Capsule on Glycemic Indices and Lipid Profile in Patients with Type 2 Diabetes Mellitus: A Randomized Controlled Clinical Trial

Objectives: The present study aimed to investigate the potential effects of the combined herbal capsule (CHC), as a nutritional supplement, on glycemic indices (GIs) and lipid profile (LP) of patients with type 2 diabetes mellitus (T2DM). Methods: Following a randomized, single-blind, placebo-controlled clinical trial, the current study was conducted on 80 cases with T2DM who were randomly assigned into two groups of treatment (CHCs; n = 40) and control (placebo; n = 40). Both groups received the intervention (500 mg capsules) twice a day for three months, without changes in the previous dose of oral anti-hyperglycemic drugs. The GI and LP levels were measured before the intervention and three months later to investigate the potential efficacy of the interventions. Results: For those in the intervention group, the mean GI i.e., fasting blood sugar, two hours postprandial (2hpp), and HbAlc] was significantly different after 3 months (P 0.05). The HDL-C level was also significantly improved in the intervention group compared to the control group (P < 0.05). Conclusions: This study demonstrated that receiving CHCs could improve GI and LP levels (TG, LDL-C, and HDL-C, except for TC), which indicates its potential to control T2DM. Moreover, no significant side effect was observed in the intervention group. It can be argued that the use of CHCs, as adjuvant therapy, in combination with conventional hypoglycemic and lipid-lowering drugs, as well as following a modified lifestyle, not only can significantly enhance glycemic control but also may prevent T2DM complications

A Comparative Analysis of Clinical Characteristics and Laboratory Findings of COVID-19 between Intensive Care Unit and Non-Intensive Care Unit Pediatric Patients: A Multicenter, Retrospective, Observational Study from Iranian Network for Research in Viral

Introduction: To date, little is known about the clinical features of pediatric COVID-19 patients admitted to intensive care units (ICUs).&nbsp;Objective: Herein, we aimed to describe the differences in demographic characteristics, laboratory findings, clinical presentations, and outcomes of Iranian pediatric COVID-19 patients admitted to ICU versus those in non-ICU settings.&nbsp;Methods: This multicenter investigation involved 15 general and pediatrics hospitals and included cases with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on positive real-time reverse transcription polymerase chain reaction (RT-PCR) admitted to these centers between March and May 2020, during the initial peak of the COVID-19 pandemic in Iran.&nbsp;Results: Overall, 166 patients were included, 61 (36.7%) of whom required ICU admission. The highest number of admitted cases to ICU were in the age group of 1–5 years old. Malignancy and heart diseases were the most frequent underlying conditions. Dyspnea was the major symptom for ICU-admitted patients. There were significant decreases in PH, HCO3 and base excess, as well as increases in creatinine, creatine phosphokinase (CPK), lactate dehydrogenase (LDH), and potassium levels between ICU-admitted and non-ICU patients. Acute respiratory distress syndrome (ARDS), shock, and acute cardiac injury were the most common features among ICU-admitted patients. The mortality rate in the ICU-admitted patients was substantially higher than non-ICU cases (45.9% vs. 1.9%, respectively; p&lt;0.001).&nbsp;Conclusions: Underlying diseases were the major risk factors for the increased ICU admissions and mortality rates in pediatric COVID-19 patients. There were few paraclinical parameters that could differentiate between pediatrics in terms of prognosis and serious outcomes of COVID-19. Healthcare providers should consider children as a high-risk group, especially those with underlying medical conditions

Medicine Consumption Forecasting in Hospitals using Long Short-Term Memory Model

Background and Objectives In recent years, medicine supply chain management has become more significant, especially after the Covid-19 pandemic. The most important issue is supply chain cost control. If the drug inventory is not properly managed, it will lead to issues such as the lack of inventory of certain drugs, provision of excess inventory, increased costs, and, finally, patient dissatisfaction. Materials and Methods In this study, an attempt has been made to predict and manage the pharmaceutical needs of hospitals using an efficient deep-learning algorithm. The drug consumption data for ten years of Besat General Hospital in Hamedan are extracted from the HIS database. As a case study, the accuracy of the predictive model is evaluated, especially for cefazolin. We use a deep model to analyze the medical time-series data efficiently. This model consists of a Long Short-Term Memory network, which can sufficiently recognize the change history in time-series prediction applications. The proposed model with many adjustable parameters in the deep architecture will bring good performance to overcome the complexities of the learning problem. Results Using the deep learning method can increase robustness by reducing the effects of complexity and uncertainty in medical data. The average forecasting error for the proposed method is 0.043, and the measured values for RMSE, MAE, and R2  are 0.335, 0.260, and 0.851, respectively. Conclusion A comprehensive comparison between some other predictive methods and the implemented model shows the outperformance of the proposed approach. Additionally, the evaluation results indicate the efficiency of the proposed approach

Lettres d'Iran. Images architecturales et urbaines au défi du COVID-19

Architectural and urban pictures to challenge Covid-19 – The arrival of the Coronavirus in Iran confronts a highly centralized state, weakened by US sanctions, and an imaginative civil society to react and take initiatives. The transformations of traditional Iranian habitat are also not favorable for protection against contagion. Conversely, the crisis is accelerating changes in urban mobility (bicycle-sharing system) and transitions to the digital society.L’arrivée du Coronavirus en Iran confronte un État très centralisé, affaibli par les sanctions américaines, et une société civile imaginative pour réagir et prendre des initiatives. Les transformations de l’habitat traditionnel iranien ne sont pas non plus favorables pour la protection contre la contagion. Inversement, la crise accélère les mutations de la mobilité urbaine (vélo partagé) et les transitions vers la société numérique.Abbasi Naderpoor Mohammadreza, Khalvandi Rezvan, Abbasi Naderpour Marzieh, Abbasi Naderpoor Razieh. Lettres d'Iran. Images architecturales et urbaines au défi du COVID-19. In: Villes en parallèle, n°49-50,2020. Matériaux pour la ville de demain. pp. 290-302

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

A quality annotated corpus is essential to research. Despite the re- cent focus of the Web science community on cyberbullying research, the community lacks standard benchmarks. This paper provides both a quality annotated corpus and an o ensive words lexicon capturing di erent types of harassment content: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual, and (v) political1. We rst crawled data from Twitter using this content-tailored o ensive lexicon. As mere presence of an o ensive word is not a reliable indicator of harassment, human judges annotated tweets for the presence of harassment. Our corpus consists of 25,000 annotated tweets for the ve types of harassment content and is available on the Git repository2

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

A quality annotated corpus is essential to research. Despite the re- cent focus of the Web science community on cyberbullying research, the community lacks standard benchmarks. This paper provides both a quality annotated corpus and an o ensive words lexicon capturing di erent types of harassment content: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual, and (v) political1. We rst crawled data from Twitter using this content-tailored o ensive lexicon. As mere presence of an o ensive word is not a reliable indicator of harassment, human judges annotated tweets for the presence of harassment. Our corpus consists of 25,000 annotated tweets for the ve types of harassment content and is available on the Git repository2

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

A quality annotated corpus is essential to research. Despite the re- cent focus of the Web science community on cyberbullying research, the community lacks standard benchmarks. This paper provides both a quality annotated corpus and an o ensive words lexicon capturing di erent types of harassment content: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual, and (v) political1. We rst crawled data from Twitter using this content-tailored o ensive lexicon. As mere presence of an o ensive word is not a reliable indicator of harassment, human judges annotated tweets for the presence of harassment. Our corpus consists of 25,000 annotated tweets for the ve types of harassment content and is available on the Git repository2