A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the ...patient’s health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.
In the current study we prepared cost-effective adsorbents based on ajwa date pits to remove Cu(II) ions from aqueous medium. Adsorbents were studied using scanning electron microscopy (SEM), FT-IR, ...and Brunauer-Emmett-Teller (BET) methods to characterize the surface functionalities, morphology, pore size, and particle size. The concentration of Cu(II) ions in the studied samples was determined by atomic adsorption spectrometry technique (AAS). Adsorption method was performed sequentially in a batch system followed by optimization by studying the numerous conditions, for instance the initial amounts of Cu(II) ions, dosages of the adsorbent, contact time, and pH of the solution. The ideal pH observed for maximum adsorption capacity was ~6.5. Langmuir and Freundlich isotherm models correctly predicted the investigation results, with the maximum monolayer adsorption capacities for Cu(II) ions at 328 K being 1428.57 mg/g (treated ajwa date pits, TADP) and 1111.1 mg/g for as produced ajwa date pits (ADP). It was revealed that TADP possess greater adsorption capability than ADP. Recovery investigations revealed that the saturated adsorbents eluted the maximum metal with 0.1 M HCl. Cu(II) ions adsorption was observed to be reduced by 80-89% after the second regeneration cycle. For the raw and chemically processed ajwa date pits adsorbent, the Langmuir model performed significantly better than the Freundlich model. The results demonstrated that the adsorbent made from ajwa date pits could be an economical and environmentally friendly alternative for removing Cu(II) ion pollutant from aqueous media.
A gap coupled hexagonal split ring resonator (GCHSRR) based metamaterial is presented in this paper for S-band and X-band microwave applications with absorptance. This gap coupled hexagonal split ...ring resonator is the amendment of the typical split-ring resonator (SRR). Three interconnected hexagonal split ring resonators are applied with a stripline to increase the electrical length and coupling effect of the GCHSRR. SRR has an impact on the extraction of effective parameters such as permittivity, permeability and refractive index. The dimension of the proposed GCHSRR unit cell is 10×10 mm2, which is printed on low-cost FR4 material. The transmission frequency of the proposed GCHSRR unit cell ranges from 3.42 GHz to 3.73 GHz and 11.27 GHz to 11.91 GHz, which makes the metamaterial applicable for S-band and X-band microwave applications. The GCHSRR unit cell has a double negative regime of 7.92 GHz to 9.78 GHz with an effective negative refractive index regime of 6.30 GHz to 10.22 GHz and 11.97 GHz to 12.61 GHz. The effective medium ratio is 8.4, which implies the novelty of the proposed design. Moreover, the GCHSRR has high absorption peaks of 99%, 98%, and 81% at 4.27 GHz, 5.42 GHz, and 12.40 GHz, respectively. An 18×20 GCHSRR array structure is also designed and studied. The effective parameters and the effective medium ratio with a high absorptance make the proposed GCHSRR based metamaterial suitable for practical microwave applications.
Traditional waste management system operates based on daily schedule which is highly inefficient and costly. The existing recycle bin has also proved its ineffectiveness in the public as people do ...not recycle their waste properly. With the development of Internet of Things (IoT) and Artificial Intelligence (AI), the traditional waste management system can be replaced with smart sensors embedded into the system to perform real time monitoring and allow for better waste management. The aim of this research is to develop a smart waste management system using LoRa communication protocol and TensorFlow based deep learning model. LoRa sends the sensor data and Tensorflow performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper, and general waste compartment which are controlled by the servo motors. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This object detection model is trained with images of waste to generate a frozen inference graph used for object detection which is done through a camera connected to the Raspberry Pi 3 Model B+ as the main processing unit. Ultrasonic sensor is embedded into each waste compartment to monitor the filling level of the waste. GPS module is integrated to monitor the location and real time of the bin. LoRa communication protocol is used to transmit data about the location, real time and filling level of the bin. RFID module is embedded for the purpose of waste management personnel identification.
Internet of Things (IoT) based healthcare system is now at the top peak because of its potentialities among all other IoT applications. Supporting sensors integrated with IoT healthcare can ...effectively analyze and gather the patients’ physical health data that has made the IoT based healthcare ubiquitously acceptable. A set of challenges including the continuous presence of the healthcare professionals and staff as well as the proper amenities in remote areas during emergency situations need to be addressed for developing a flexible IoT based healthcare system. Besides that, the human entered data are not as reliable as automated generated data. The development of the IoT based health monitoring system allows a personalized treatment in certain circumstances that helps to reduce the healthcare cost and wastage with a continuous improving outcome. We present an IoT based health monitoring system using the MySignals development shield with (Low power long range) LoRa wireless network system. Electrocardiogram (ECG) sensor, body temperature sensor, pulse rate, and oxygen saturation sensor have been used with MySignals and LoRa. Evaluating the performances and effectiveness of the sensors and wireless platform devices are also analyzed in this paper by applying physiological data analysis methodology and statistical analysis. MySignals enables the stated sensors to gather physical data. The aim is to transmit the gathered data from MySignals to a personal computer by implementing a wireless system with LoRa. The results show that MySignals is successfully interfaced with the ECG, temperature, oxygen saturation, and pulse rate sensors. The communication with the hyper-terminal program using LoRa has been implemented and an IoT based healthcare system is being developed in MySignals platform with the expected results getting from the sensors.
A metamaterial (MTM) loaded compact three-dimensional antenna is presented for the portable, low-cost, non-invasive microwave head imaging system. The antenna has two slotted dipole elements with ...finite arrays of MTM unit cell and a folded parasitic patch that attains directional radiation patterns with 80% of fractional bandwidth. The operating frequency of the antenna is 1.95-4.5 GHz. The optimization of MTM unit cell is performed to increase the operational bandwidth, realized gain, and efficiency of the antenna within the frequency regime. It is also explored to improve radiation efficiency and gain when placed to head proximity. One-dimensional mathematical modelling is analyzed to precisely estimate the power distribution that validates the performance of the proposed antenna. To verify the imaging capability of the proposed system, an array of 9 antennas and a realistic three-dimensional tissue-emulating experimental semi-solid head phantom are fabricated and measured. The backscattered signal is collected from different antenna positions and processed by the updated Iterative Correction of Coherence Factor Delay-Multiply-and-Sum beamforming algorithm to reconstruct the hemorrhage images. The reconstructed images in simulation and experimental environment demonstrate the feasibility of the proposed system as a portable platform to successfully detect and locate the hemorrhages inside the brain.
The aim of the research was to prepare low-cost adsorbents, including raw date pits and chemically treated date pits, and to apply these materials to investigate the adsorption behavior of Cr(III) ...and Cd(II) ions from wastewater. The prepared materials were characterized using SEM, FT-IR and BET surface analysis techniques for investigating the surface morphology, particle size, pore size and surface functionalities of the materials. A series of adsorption processes was conducted in a batch system and optimized by investigating various parameters such as solution pH, contact time, initial metal concentrations and adsorbent dosage. The optimum pH for achieving maximum adsorption capacity was found to be approximately 7.8. The determination of metal ions was conducted using atomic adsorption spectrometry. The experimental results were fitted using isotherm Langmuir and Freundlich equations, and maximum monolayer adsorption capacities for Cr(III) and Cd(II) at 323 K were 1428.5 and 1302.0 mg/g (treated majdool date pits adsorbent) and 1228.5 and 1182.0 mg/g (treated sagai date pits adsorbent), respectively. It was found that the adsorption capacity of H2O2-treated date pits was higher than that of untreated DP. Recovery studies showed maximal metal elution with 0.1 M HCl for all the adsorbents. An 83.3–88.2% and 81.8–86.8% drop in Cr(III) and Cd(II) adsorption, respectively, were found after the five regeneration cycles. The results showed that the Langmuir model gave slightly better results than the Freundlich model for the untreated and treated date pits. Hence, the results demonstrated that the prepared materials could be a low-cost and eco-friendly choice for the remediation of Cr(III) and Cd(II) contaminants from an aqueous solution.
This paper presents the preparation and measurement of tissue-mimicking head phantom and its validation with the iteratively corrected coherence factor delay-multiply-and-sum (IC-CF-DMAS) algorithm ...for brain stroke detection. The phantom elements are fabricated by using different chemical mixtures that imitate the electrical properties of real head tissues (CSF, dura, gray matter, white matter, and blood/stroke) over the frequency band of 1-4 GHz. The electrical properties are measured using the open-ended dielectric coaxial probe connected to a vector network analyzer. Individual phantom elements are placed step by step in a three-dimensional skull. The IC-CF-DMAS image reconstruction algorithm is later applied to the phantom to evaluate the effectiveness of detecting stroke. The phantom elements are preserved and measured multiple times in a week to validate the overall performance over time. The electrical properties of the developed phantom emulate the similar properties of real head tissue. Moreover, the system can also effectively detect the stroke from the developed phantom. The experimental results demonstrate that the developed tissue-mimicking head phantom is time-stable, and it shows a good agreement with the theoretical results in detecting and reconstructing the stroke images that could be used in investigating as a supplement to the real head tissue.
Internet of Things (IoT) based application requires integration with the wireless communication technology to make the application data readily available. In this paper, a modified meander shape ...microstrip patch antenna has been proposed for IoT applications at 2.4 GHz ISM (Industrial, Scientific and Medical) band. The dimension of the antenna is 40x10x1.6 mm3. The antenna design is comprised of an inverse S-shape meander line connected with a slotted rectangular box. A capacitive load (C-load) and parasitic patch with the shaped ground are applied to the design. Investigations show that the antenna designed with an inverse S-shape patch and connecting rectangular box in the microstrip line has a higher efficiency and gain compare to the conventional meander shape antenna. The C-load is applied to the feed line to match the impedance. Moreover, parametric studies are carried out to investigate the flexibility of the antenna. Results show that, the gain and efficiency can be improved through adjusting the rectangular box with applying parasitic element and the shaped ground. The parasitic element has high impact on the bandwidth of the antenna of 12.5%. The finalized antenna has a peak gain of -0.256 dBi (measured) and 1.347 dBi (Simulated) with 79% radiation efficiency at 2.4 GHz. To prove the efficiency and eligibility in IoT applications, the measurement of the power delivered and received by the antenna at 2.4 GHz is performed and compared with the results of a dipole antenna. The antenna is integrated with 2.4 GHz radio frequency module and IoT sensors to validate the performance. The antenna novelty relies on the size compactness with high fractional bandwidth that is validated through the IoT application environment.
A complementary split-ring resonator (CSRR) based metamaterial is designed and investigated in this paper for microwave applications with effective medium ratio. This CSRR is the modification of the ...conventional split-ring resonator. Three square split-ring resonators (SSRR) are interconnected with a strip line as well as two splits in three square split-ring resonators are applied to increase the electrical length and coupling effect. The CSRR has an impact on the extraction of effective permittivity, permeability, and refractive index. The dimension of the designed unit cell is 5.5 × 5.5 mm2 that is printed on FR4 material. An 18 × 20 array structure is also investigated in this paper. The unit cell has a double negative region of 6.34–7.39 GHz and 8.20–9.98 GHz with effective negative refractive index region of 4.23–7.32 GHz and 7.40–10.0 GHz. The effective medium ratio is 8.0, which implies the effectiveness and compactness of the proposed CSSR design. The compact size, effective parameters, and effective medium ratio rate represent the suitability of the proposed metamaterial for practical C band microwave applications.
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