A review on value-addition to plastic waste towards achieving a circular economy.

Plastic and mixed plastic waste (PW) has received increased worldwide attention owing to its huge rate of production, high persistency in the environment, and unsustainable waste management practices. Therefore, sustainable PW management and upcycling approaches are imperative to achieve the objectives of the United Nations Sustainable Development Goals. Numerous recent studies have shown the application and feasibility of various PW conversion techniques to produce materials with better economic value.

Development of a Simple Electroless Method for Depositing Metallic Pt-Pd Nanoparticles over Wire Gauge Support for Removal of Hydrogen in a Nuclear Reactor.

Electroless noble metal deposition on the conducting substrate is widely used to obtain the desired film or coating on the substrate of interest. Wire-gauge-based Pt/Pd/Pt-Pd (individually, sequentially, and simultaneously deposited) catalysts have been developed using formaldehyde and sodium formate as reducing agents. Various surface pretreatment methods like SnCl + PdCl seeding, oxalic acid etching, and HCl activation (etching) have been employed to obtain the desired noble metal coating.

Generative adversarial networks based skin lesion segmentation.

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative network to generate accurate lesion masks.

An amalgamation of vision transformer with convolutional neural network for automatic lung tumor segmentation.

Lung cancer has the highest mortality rate. Its diagnosis and treatment analysis depends upon the accurate segmentation of the tumor. It becomes tedious if done manually as radiologists are overburdened with numerous medical imaging tests due to the increase in cancer patients and the COVID pandemic.

CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation.

Lung nodule segmentation plays a crucial role in early-stage lung cancer diagnosis, and early detection of lung cancer can improve the survival rate of the patients. The approaches based on convolutional neural networks (CNN) have outperformed the traditional image processing approaches in various computer vision applications, including medical image analysis. Although multiple techniques based on convolutional neural networks have provided state-of-the-art performances for medical image segmentation tasks, these techniques still have some challenges.

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge.

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations.

Hybrid hunt-based deep convolutional neural network for emotion recognition using EEG signals.

Emotion recognition from the electroencephalogram (EEG) signals is a recent trend as EEG generated directly from the human brain is considered an effective modality for recognizing emotions. Though there are many methods to address the challenge associated with the recognition, the research community still focuses on advanced methods, like deep learning and optimization, to acquire effective emotion recognition. Hence, this research focuses on developing a well-adapted emotion recognition model with the aid of an optimized deep convolutional neural network (Deep CNN).

HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images.

Automatic liver and tumor segmentation are essential steps to take decisive action in hepatic disease detection, deciding therapeutic planning, and post-treatment assessment. The computed tomography (CT) scan has become the choice of medical experts to diagnose hepatic anomalies. However, due to advancements in CT image acquisition protocol, CT scan data is growing and manual delineation of the liver and tumor from the CT volume becomes cumbersome and tedious for medical experts.

NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation.

Glaucoma is an ocular disease threatening irreversible vision loss. Primary screening of Glaucoma involves computation of optic cup (OC) to optic disc (OD) ratio that is widely accepted metric. Recent deep learning frameworks for OD and OC segmentation have shown promising results and ways to attain remarkable performance.

MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images.

Automatic liver and tumor segmentation play a significant role in clinical interpretation and treatment planning of hepatic diseases. To segment liver and tumor manually from the hundreds of computed tomography (CT) images is tedious and labor-intensive; thus, segmentation becomes expert dependent. In this paper, we proposed the multi-scale approach to improve the receptive field of Convolutional Neural Network (CNN) by representing multi-scale features that extract global and local features at a more granular level.

A novel approach to build accurate and diverse decision tree forest.

Decision tree is one of the best expressive classifiers in data mining. A decision tree is popular due to its simplicity and straightforward visualization capability for all types of datasets. Decision tree forest is an ensemble of decision trees.

Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning.

Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images.

Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton.

Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed.

Super-twisting control using higher order disturbance observer for control of SISO and MIMO coupled systems.

In this paper, accuracy of the existing disturbance observer (DO) for disturbance estimation is increased by increasing its order. Stability of the proposed higher order disturbance observer (HODO) is analysed. Furthermore, a new HODO based super-twisting control (STW) algorithm is presented for the system with the bounded matched disturbances.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains.

Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning.

Skull-stripping is an essential pre-processing step in computational neuro-imaging directly impacting subsequent analyses. Existing skull-stripping methods have primarily targeted non-pathologicallyaffected brains. Accordingly, they may perform suboptimally when applied on brain Magnetic Resonance Imaging (MRI) scans that have clearly discernible pathologies, such as brain tumors.

A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas.

Gliomas are the most common primary brain malignancies, with varying degrees of aggressiveness and prognosis. Understanding of tumor biology and intra-tumor heterogeneity is necessary for planning personalized therapy and predicting response to therapy. Accurate tumoral and intra-tumoral segmentation on MRI is the first step toward understanding the tumor biology through computational methods.

Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation.

Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule segmentation algorithm. In this paper, we proposed the Deep Deconvolutional Residual Network (DDRN) based approach for the lung nodule segmentation from the CT images.

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR.

Retinal image analysis for disease screening through local tetra patterns.

Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR) are the most prevalent diseases responsible for visual impairment in the world. This work investigates discrimination potential in the texture of color fundus images to distinguish between diseased and healthy cases by avoiding the prior lesion segmentation step. It presents a retinal background characterization approach and explores the potential of Local Tetra Patterns (LTrP) for texture classification of AMD, DR and Normal images.

Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna river basin, India.

Climate change has a significant effect on various hydrological processes in a large river basin. The assessment of these processes is also useful for water resource management and long-term sustainability of any hydrological project. In this study, an attempt is made to quantify the effects of climate change on streamflow and sediment concentration in the Purna river basin, India.

Algorithms for red lesion detection in Diabetic Retinopathy: A review.

Diabetic Retinopathy (DR) is an outcome of prolonged diabetes which directly or indirectly affect the human vision. DR is asymptomatic in its early stages and the late diagnosis lead to undeviating loss of vision. The computer aided diagnosis with the assistance of medical images helps in timely and accurate treatment.

A new continuous sliding mode control approach with actuator saturation for control of 2-DOF helicopter system.

The 2-degree of freedom (DOF) helicopter system is a typical higher-order, multi-variable, nonlinear and strong coupled control system. The helicopter dynamics also includes parametric uncertainties and is subject to unknown external disturbances. Such complicated system requires designing a sophisticated control algorithm that can handle these difficulties.

Tropical littoral ambient noise probability density function model based on sea surface temperature.

Ambient noise variability in tropical shallow water presents a critical challenge for sonar designers and operators due to site-specific sea surface fluctuations. Sea surface temperature (SST) is a direct measure of energy balance defining the local climate of the region and hence ambient noise characteristics. In this work, an ambient noise probability density function (pdf) model for a spectral band of 3-10 kHz has been designed based on the statistical distribution of SST and validated using real field data.