The detection and treatment of increasing air pollution due to technological developments represent some of the most important challenges facing the world today. Indeed, there has been a significant increase in levels of environmental pollution in recent years. The aim of the work presented herein is to design an intelligent predictor for the concentrations of air pollutants over the next 2 days based on deep learning techniques using a recurrent neural network (RNN). The best structure for its operation is then determined using a particle swarm optimization (PSO) algorithm. The new predictor based on intelligent computation relying on unsupervised learning, i.e., long short-term memory (LSTM) and optimization (i.e., PSO), is called the smart air quality prediction model (SAQPM). The main goal is to predict six the concentrations of six types of air pollution, viz. PM2.5 particulate matter, PM10, particulate matter, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). SAQPM consists of four stages. The first stage involves data collection from multiple stations (35 in this case). The second stage involves preprocessing of the data, including (a) separation of each station with an independent focus, (b) handle missing values, and (c) normalization of the dataset to the range of (0, 1) using the MinMaxScalar method. The third stage relates to building the predictor based on the LSTM method by identifying the best structure and parameter values (weight, bias, number of hidden layers, number of nodes in each hidden layer, and activation function) for the network using the functional PSO algorithm to achieve a goal. Thereafter, the dataset is split into training and testing parts based on the ten cross-validation principle. The training dataset is then used to build the predictor. In the fourth stage, evaluation results for each station are obtained by reading the concentration of each pollutant each hour for at most 30 days then taking the average of the symmetric mean absolute percentage error (SMAPE) for 25 days only.

A new method for prediction of air pollution based on intelligent computation

Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs. The source database used for model development and validation is the medical information mart for intensive care (MIMIC) III. We identified adult sepsis patients using the new sepsis definition Sepsis-3. A total of 86 predictor variables consisting of demographics, laboratory tests and comorbidities were used. We employed the least absolute shrinkage and selection operator (LASSO), random forest (RF), gradient boosting machine (GBM) and the traditional logistic regression (LR) method to develop prediction models. In addition, the prediction performance of the four developed models was evaluated and compared with that of an existent scoring tool – simplified acute physiology score (SAPS) II – using five different performance measures: the area under the receiver operating characteristic curve (AUROC), Brier score, sensitivity, specificity and calibration plot. The records of 16,688 sepsis patients in MIMIC III were used for model training and test. Amongst them, 2949 (17.7%) patients had in-hospital death. The average AUROCs of the LASSO, RF, GBM, LR and SAPS II models were 0.829, 0.829, 0.845, 0.833 and 0.77, respectively. The Brier scores of the LASSO, RF, GBM, LR and SAPS II models were 0.108, 0.109, 0.104, 0.107 and 0.146, respectively. The calibration plots showed that the GBM, LASSO and LR models had good calibration; the RF model underestimated high-risk patients; and SAPS II had the poorest calibration. The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.

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Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

Social development and technological advances have enabled the Internet of Vehicles (IoV) to combine the social factors to form a new intelligent transportation system: Social IoV (SIoV). The emergence of SIoV helps to find new traffic management solutions of the serious problems caused by the ever-increasing traffic flow. In this paper, we propose an algorithm called social vehicle route selection (SVRS) to reduce traffic congestion and achieve the purpose of traffic flow control. First, a social clustering method for SIoV is designed by utilizing both historical and current driving information. Then we use game evolution to calculate the optimal route for vehicles, and prove the vehicle route selection game is a potential game and its strategy selection converges to Nash equilibrium. Extensive simulations are carried out to evaluate the SVRS with several performance criteria. Our analysis and simulation results demonstrate that SVRS algorithm can achieve high performance in clustering the vehicles and reducing traffic congestion.

Vehicle Route Selection Based on Game Evolution in Social Internet of Vehicles

A deep learning approach for prediction of air quality index in a metropolitan city

Upgrading health reality is the responsibility of all, it is necessary to think about the design of a smart system based on modern technologies to reduce the time and effort exerted on the competent authorities in both health and environmental sectors furthermore making their work environment smart and easy to enable the creativity and innovation as well as to reduce the material costs granted by state of this case “environment.” The process to find a solution for the problem of a triangle as shown in Figure (1) with contradictory heads is a very important and difficult issue in the field of health and environment, these are: (to optimize time utilization, and minimize the human errors, that accompany this human effort as much as possible, and to reduce material costs). Therefore, the idea of Internet technology and the Intelligent Big Data Analysis was developed to design an integrate electronic system of hardware to be developed in different and specific locations to collect information on concentrations that cause air pollution. So, it was invested an idea of Internet of things technology and intelligent data analysis ("Internet of things" and "Intelligent Data Analysis") for the construction of an integrated system of Hardware entities and Software entities placed. The aim of this work is to build a programmable system capable of predicting the pollutant concentrations within the next 48 h called intelligent forecaster of concentrations caused air pollution (IFCsAP) and making the machine the primary source of information after these concentrations are collected and stored in real time. On this basis, we will rely on modern technologies to reach this goal. The proposed design is highly efficient, cost-effective and easy to use and can be deployed in all places (environment with concentrations of air pollution). The main objective of the proposed system is to issue periodic reports (within 48 h of the future) based on the information input from different stations in real time. These reports are issued based on the readings of sensors planted at each station. Each sensor has a measurement of one or more concentrations that cause air pollution. Designed system consists of three basic phases: the construction phase of an integrated electronic circuit consisting of several devices (Modern, LoRa, Waspmate Platform, Arduino, Five sensors).

Intelligent forecaster of concentrations (PM2.5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP)

Intelligent analysis of prediction data mining techniques is widely used to support optimising future decision-making in many different fields including healthcare and medical diagnoses. These techniques include Chi-squared Automatic Interaction Detection (CHAID), Exchange Chi-squared Automatic Interaction Detection (ECHAID), Random Forest Regression and Classification (RFRC), Multivariate Adaptive Regression Splines (MARS), and Boosted Tree Classifiers and Regression (BTCR). This paper presents the general properties, summary, advantages, and disadvantages of each one. Most importantly, the analysis depends upon the parameters that have been used for building a prediction model for each one. Besides, classifying those techniques according to their main and secondary parameters is another task. Furthermore, the presence and absence of parameters are also compared in order to identify the better sharing of those parameters among the techniques. As a result, the techniques with no randomness and mathematical basis are the most powerful and fast compared with the others.

Evaluation prediction techniques to achievement an optimal biomedical analysis

The main idea of this work is generated from studying the main challenges in the healthcare field. The goal is to identify patients who will be admitted to the hospital within the next year by using historical claims data including Information about patient and analysis it to solve the problem. But this work is dealing with very huge databases. Therefore, the new challenge is occurred the time required for executing the prediction methods and analysis, it bases on five error predicate measures, it including (Maximum error, RMSE, MSE, MAE and MAPE). As a result, cloud is suggested as a tool to solve the problem of analysing the predictions of a huge health care database. A new predictor is proposed to determine how many days in the next year a patient will spend in the hospital. In this work, we attempt to satisfy the idea that explains in Fig. 1. The main challenge here, how we can build the predictor that satisfies the gold triangle. The traversal of the T-Graph) cloud computing “i.e., Speed execution”, data mining algorithm “i.e., Abilities to deal with very huge databases” and Predication techniques” i.e., Ability to plan of the next years. 
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Fig. 1.
The idea and objective of this proposal


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Fig. 2.
Flow chart of research work activities

A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers

As vehicles have become intelligent objects, creating social friendships among them is justifiable. Internet of Vehicles (IoVs) and Vehicular Ad-hoc NETworks (VANETs) are associated terms that are recently highlighted to improve the transportation systems. Intelligent vehicles that can communicate with each other are the main component of vehicular networks (VNs). Indeed, they have resources for that intelligence: CPU, transceiver, sensors and memory.  On the other side, social networks (SNs) are also brought to light to mainly study the human relationships. In this context, gathering these vehicles in social groups is reasonable to share their information by bridging VN and SN in a multidisciplinary research direction called vehicular social networks (VSNs).This paper will present a model to group vehicles in two types of social groups. Casual groups and permanent or formal groups will be discussed to increase social ties among vehicles that have the same interests. The results will be analyzed to clarify the most important factors that affect such these relationships.

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Grouping vehicles in Vehicular Social Networks

Localization in WSNs provides critical data for decisions when collecting data from wide area spread sensors. Using embedded GPS unit on sensor board is not a cost-effective solution Therefore; researchers have paid more attention to locate sensors in WSNs without using many GPSs units. Several algorithms have been suggested for this purpose. However, they are different in terms of cost and accuracy. This paper is going to suggest a new range-based algorithm called Triple Mobile Anchors for Localization (TMAL). This algorithm is based on three mobile sensors that form a mobile triangle to locate unknown sensors. As other approaches, it uses Receive Signal Strength indicator (RSSI). Besides, the algorithm assumes that the mobile anchors are chargeable to avoid the energy issue. Based on a method known as the Relative Side Coordinates (RSC) the simulation results emphasis of the proposed algorithm compare by RELMA-method1 and RELMA-method 2, the TMAL has good accuracy and low cost. It shows how to control the accuracy based on the area of the mobile triangle.

Triple Mobile Anchors Approach for Localization in WSN

Wireless sensor networks (WSNs), which are associated with the Internet of Things (IoT), represent useful networks in assisting monitoring, tracking and sensing different environmental activities. Sensors play an essential role in designing and applying any WSN. Due to the vast advances in communication and networking technology, there are needs to develop, build and apply various smart or intelligent (unmanned) service networks. The concept of IoT refers to equipping real objects with communication and computing facilities that enables collaboration with each other in real life applications. IoT inclines toward the process of controlling, communicating, cost-saving and automation. At present, this era will be the IoT era due to its numerous vital applications. This paper aims to review the status of the IoT and its applications’ requirements. It also aims to survey the role of the sensors in this context. The paper provides a good overview of the important characteristics and applications of the WSNs and IoT. This work represents a suitable guide for researchers who are interested in such fields.

Muhammed Abaid Mahdi

Papers

Evaluation prediction techniques to achievement an optimal biomedical analysis

A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers

Grouping vehicles in Vehicular Social Networks

Triple Mobile Anchors Approach for Localization in WSN

A Contribution to the Role of the Wireless Sensors in the IoT Era