An inexpensive and effective adsorbent was developed from waste tea leaves for the dynamic uptake of Pb(II). Characterization of the adsorbents showed a clear change between physico-chemical ...properties of activated tea waste and simply tea waste. The purpose of this work was to evaluate the potential of activated tea waste in continuous flow removal of Pb(II) ions from synthetic aqueous effluents. The performance of the system was evaluated to assess the effect of various process variables, viz., of bed height, hydraulic loading rate and initial feed concentration on breakthrough time and adsorption capacity. The shape of the breakthrough curves was determined for the adsorption of Pb(II) by varying different operating parameters like hydraulic loading rate (2.3–9.17
m
3/h
m
2), bed height (0.3–0.5
m) and feed concentration (2–10
mg/l). An attempt has also been made to model the data generated from column studies using the empirical relationship based on the Bohart–Adams model. There was an acceptable degree of agreement between the data for breakthrough time calculated from the Bohart–Adams model and the present experimental study with average absolute deviation of less than 5.0%. The activated tea waste in this study showed very good promise as compared with the other adsorbents available in the literature. The adsorbent could be suitable for repeated use (for more than four cycles) without noticeable loss of capacity.
This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature ...selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. The SVM, LR, bagging SVM and bagging LR models are applied on a dataset of 310 samples publicly available in Kaggle repository. The performance of classification of abnormal and normal spinal patients is evaluated in terms of a number of factors including training and testing accuracy, recall, and miss rate. The classifier models are also evaluated by optimizing the kernel parameters, and by using the results of receiver operating characteristic (ROC) and precision-recall curves. Results indicate that when 78% data are used for training, the observed training accuracies for SVM, LR, bagging SVM and bagging LR are 86.30%, 85.47%, 86.72% and 85.06%, respectively. On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.
Lung disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many ...image processing and machine learning models have been developed for this purpose. Different forms of existing deep learning techniques including convolutional neural network (CNN), vanilla neural network, visual geometry group based neural network (VGG), and capsule network are applied for lung disease prediction. The basic CNN has poor performance for rotated, tilted, or other abnormal image orientation. Therefore, we propose a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN. This new hybrid method is termed here as VGG Data STN with CNN (VDSNet). As implementation tools, Jupyter Notebook, Tensorflow, and Keras are used. The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered. For both full and sample datasets, VDSNet outperforms existing methods in terms of a number of metrics including precision, recall, F0.5 score and validation accuracy. For the case of full dataset, VDSNet exhibits a validation accuracy of 73%, while vanilla gray, vanilla RGB, hybrid CNN and VGG, and modified capsule network have accuracy values of 67.8%, 69%, 69.5% and 63.8%, respectively. When sample dataset rather than full dataset is used, VDSNet requires much lower training time at the expense of a slightly lower validation accuracy. Hence, the proposed VDSNet framework will simplify the detection of lung disease for experts as well as for doctors.
This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for ...COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.
This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) techniques in fighting against the effects of ...novel coronavirus disease (COVID-19).
The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets.
Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19.
Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.
A large stream of research has studied the performance of waste plastics impregnated concrete, reporting multiple benefits and advocating its use in construction works. But no study has reported the ...merits of bricks impregnated with waste plastics. The present paper reports the results of experiments done on bricks made up of varying percentages of waste thermoplastics (0 – 10% by weight) and sand (60 – 70% by weight), holding percentages of fly ash and ordinary Portland cement constant at 15% (by weight) each. Three types of waste thermoplastics were used, forming three separate batches of bricks. The plastics were polycarbonates, polystyrenes, and mixed plastics. The bricks were cured under water for 28 days. Some of the batches were baked at temperatures ranging from 90 °C to 110 °C for 2 hours in order to melt the plastics to form voids. The bricks made with the above-stated compositions were found to possess low thermal conductivity and adequately high compressive strength. The compressive strength of these bricks is observed to be more than 17 MPa, which lies within the upper half of the range of strengths specified for bricks in the IS 1077:1992 standard. The waste plastics impregnated bricks display high thermal resistance, a feature that can add economic value to the brick manufacturers, motivating them to establish the necessary logistics for collection and use of all types of waste thermoplastics. The paper also presents a regression model to predict the compressive strength of bricks at varying plastic contents. The study, thus, introduces a new strand of research on sustainable recycling of waste thermoplastics in the context of the circular economy.
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•Review of the present state of waste plastics and recycling process are presented.•Making bricks out of waste plastics is explored.•Mechanical properties are satisfactory up to 10% of plastic content.•Recycling waste thermoplastics in construction materials is recommended.
Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and ...reduce misinterpretations in lung disease diagnosis. Existing literature indicates that more research is needed to correctly classify lung diseases in the presence of multiple classes for different radiographic imaging datasets. As a result, this paper proposes RVCNet, a hybrid deep neural network framework for predicting lung diseases from an X-ray dataset of multiple classes. This framework is developed based on the ideas of three deep learning techniques: ResNet101V2, VGG19, and a basic CNN model. In the feature extraction phase of this new hybrid architecture, hyperparameter fine-tuning is used. Additional layers, such as batch normalization, dropout, and a few dense layers, are applied in the classification phase. The proposed method is applied to a dataset of COVID-19, non-COVID lung infections, viral pneumonia, and normal patients' X-ray images. The experiments take into account 2262 training and 252 testing images. Results show that with the Nadam optimizer, the proposed algorithm has an overall classification accuracy, AUC, precision, recall, and F1-score of 91.27%, 92.31%, 90.48%, 98.30%, and 94.23%, respectively. Finally, these results are compared with some recent deep-learning models. For this four-class dataset, the proposed RVCNet has a classification accuracy of 91.27%, which is better than ResNet101V2, VGG19, VGG19 over CNN, and other stand-alone models. Finally, the application of the GRAD-CAM approach clearly interprets the classification of images by the RVCNet framework.
Microplastics (MPs) pollution has become one of the most severe environmental concerns today. MPs persist in the environment and cause adverse effects in organisms. This review aims to present a ...state-of-the-art overview of MPs in the aquatic environment. Personal care products, synthetic clothing, air-blasting facilities and drilling fluids from gas-oil industries, raw plastic powders from plastic manufacturing industries, waste plastic products and wastewater treatment plants act as the major sources of MPs. For MPs analysis, pyrolysis-gas chromatography–mass spectrometry (Py-GC–MS), Py-MS methods, Raman spectroscopy, and FT-IR spectroscopy are regarded as the most promising methods for MPs identification and quantification. Due to the large surface area to volume ratio, crystallinity, hydrophobicity and functional groups, MPs can interact with various contaminants such as heavy metals, antibiotics and persistent organic contaminants. Among different physical and biological treatment technologies, the MPs removal performance decreases as membrane bioreactor (> 99%) > activated sludge process (~98%) > rapid sand filtration (~97.1%) > dissolved air floatation (~95%) > electrocoagulation (> 90%) > constructed wetlands (88%). Chemical treatment methods such as coagulation, magnetic separations, Fenton, photo-Fenton and photocatalytic degradation also show moderate to high efficiency of MP removal. Hybrid treatment technologies show the highest removal efficacies of MPs. Finally, future research directions for MPs are elaborated.
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•MPs act as a significant vector of various organic and inorganic pollutants.•Best physical removal methods are UF, dynamic membrane filtration and rapid sand filtration.•Best chemical treatment methods are EC, sol-gel-agglomeration and electro-Fenton.•Constructed wetlands and MBRs are efficient biological treatment technologies.•Hybrid treatment technologies provide the highest MPs removal.
Heavy metals released into the water bodies and on land surfaces by industries are highly toxic and carcinogenic in nature. These heavy metals create serious threats to all the flora and fauna due to ...their bioaccumulatory and biomagnifying nature at various levels of food chain. Existing conventional technologies for heavy metal removal are witnessing a downfall due to high operational cost and generation of huge quantity of chemical sludge. Adsorption by various adsorbents appears to be a potential alternative of conventional technologies. Its low cost, high efficiency, and possibility of adsorbent regeneration for reuse and recovery of metal ions for various purposes have allured the scientists to work on this technique. The present review compiles the exhaustive information available on the utilization of bacteria, algae, fungi, endophytes, aquatic plants, and agrowastes as source of adsorbent in adsorption process for removal of heavy metals from aquatic medium. During the last few years, a lot of work has been conducted on development of adsorbents after modification with various chemical and physical techniques. Adsorption of heavy metal ions is a complex process affected by operating conditions. As evident from the literature, Langmuir and Freundlich are the most widely used isotherm models, while pseudo first and second order are popularly studied kinetic models. Further, more researches are required in continuous column system and its practical application in wastewater treatment.
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