[Retracted] Processing and Characterization of Novel Bio‐Waste Hybrid Brick Composites for Pollution Control

Kumar, Naresh; Gaur, Piyush; Kaliappan, S.; Natrayan, L.; Socrates, S.; Patil, Pravin P.; Thanappan, Subash

doi:10.1155/2022/3127135

Cited by 26 publications

(4 citation statements)

References 41 publications

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“…Figure 3(c) shows the sintering temperature and weight fraction of boron carbide with surface roughness. From Figure 3(c), it is revealed that the optimal surface roughness was highly attained at the medium level of sintering temperature and weight fraction at 8% B 4 C [30]. Therefore, this surface roughness was highly accumulated in the medium level weight fraction at 8% of boron carbide, low level of compaction pressure 300 MPa, and medium level of temperature 480 °C.…”

Section: Contour Plot Analysis Of Machining Responsesmentioning

confidence: 91%

Artificial Intelligence Optimization of Turning Parameters of Nanoparticle‐Reinforced P/M Alloy Tool

Muniappan

Jayaraja

Vignesh

et al. 2022

Journal of Nanomaterials

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In this research, the powder metallurgy- (P/M-) based metal matrix composites were prepared to compose the machinability characteristics. Therefore, the Al2024 and boron carbide (B4C) were the base and strengthened reinforcements. During the powder metallurgy process, weight fractions of boron carbide are 4, 8, and 12%; the compaction pressure is 300 to 400 MPa, and the sintering temperature is 420 to 540°C, respectively. These parameters were planned with Taguchi L9 array for achieving the proper design. After the processing, composite specimens were utilized to conduct the turning process. For all the nine specimens, depth of cut, speed, and feed rates were maintained constant with optimal parameters. The surface roughness and material removal rate responses are successfully achieved in the optimal turning parameters. Then, the artificial neural network (ANN) model was implemented to analyze the predicted values with back propagation algorithm. In this ANN, three input layers, 6 and 4 hidden layers, and two outputs were created as per the design. Finally, the minimized surface roughness and maximized material removal rate were achieved at the process parameters like 8 wt. % of boron carbide, 300 MPa of compaction pressure, and 480°C of sintering temperature. All the predicted values are slightly maximum than the experimental values.

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Section: Contour Plot Analysis Of Machining Responsesmentioning

confidence: 91%

Artificial Intelligence Optimization of Turning Parameters of Nanoparticle‐Reinforced P/M Alloy Tool

Muniappan

Jayaraja

Vignesh

et al. 2022

Journal of Nanomaterials

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“…In particular, CNNs are popular for the automation of lung nodule detection, imaging classified pattern analysis, and estimation of cancer risk. Furthermore, machine learning-based transfer learning is used to adjust pre-trained CNNs to be applicable to the analysis of medical images and, therefore, develop a more stable and effective diagnosis system [15]- [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning based Early-detection of Lung Cancer Using CT Scan for the early Identification and Classification

Jampani Chandra Sekhar

2024

jes

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The current research is aimed at studying the application of machine learning algorithms for the early prediction and detection of lung cancer taking the images of CT scans and MR scans. In this respect, the dataset from kaggle includes 3200 images from diverse sources, which are pre-processed and applied for the process of feature extraction, using traditional methods and deep learning systems, which include Convolutional Neural Networks, VGG-16, VGG-19, and RNN, while after the training the systems are evaluated in accordance with precision, recall, f1 score, and accuracy. As a result, it is evidenced that the best model is VGG-19 with the highest accuracy of 97.86%, while follows VGG-19 the VGG-16 the CNN model, and the RNN, implying effective implications for clinical practice. Certainly, the results of the research help to create a non-invasive, effective, and fast tool, using by clinicians in their practice. The use of machine learning algorithms for the process of early prediction and detection would be helpful for the timely treatment of patients and personalized treatment plans. In such a way, based on the current research, it could be summarized that the best models are constantly developed and gradually implemented in practice. However, more significant collaboration between the researchers, clinicians, and the industry is needed to have the full implementation of applied methods in practice, having fast results and timely actions taken by clinicians.

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“…The addition of particles changes the other significant properties of the matrix, while fibre addition of fibres contributes primarily to the load‐bearing properties. The effectiveness of particle addition and its significant contribution to polymer matrix composites have been supported by more testimonials in recent decades (Mahalingam et al 2022 and Kumar et al 2022). Biopolymers, including cellulose, chitosan, chitin, and lignin, are notable biopolymers among ceramic and hard bioparticles that can strengthen the matrix material at the microscopic level and enhance its properties.…”

Section: Introductionmentioning

confidence: 95%

Characterisation of microcrystalline cellulose from waste green pea pod sheath and its sunn hemp fibre‐polyester composite: A step towards greener manufacturing

Alshahrani,

Arun Prakash

2024

Physiologia Plantarum

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In this study, a microcrystalline cellulosic biopolymer (MCB) made from Pisum Sativum (green pea) pod waste via a modified thermo‐chemical process, and Crotalaria juncea (sunn hemp) fibre was used to create environment‐friendly polyester composites. The main objective was to synthesise the MCB from domestic waste and characterize how the addition of hemp alters the strength of the polymeric composite. The MCB was synthesized using green pea pods. The sunn hemp fibres were used in mat form in this study. The composites were made using the hand layup method and post‐cured at 110°C for 2 h. The results showed that the addition of sunn hemp fibre and MCB increased the mechanical properties. Similarly, the highest observed fatigue life count for the composite designated PSC3 (2.0 vol.% MCB) was 30862 at 30% ultimate tensile stress. Similarly, the composite PSC3 had the maximum penetration resistance with an absorbed energy of 14.2 J. Moreover, the addition of silane‐treated cellulose and fibre provided an improved storage modulus of 6.8 GPa for the PSC3 composite, confirming the improved fibre‐matrix fibre interaction and increased toughness. The scanning electron microscope images revealed improved fibre‐matrix correlation. These eco‐friendly composites with better load‐bearing capabilities could be preferred in automated door panels, defence toolboxes, and home interior decoration applications.

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[Retracted] Processing and Characterization of Novel Bio‐Waste Hybrid Brick Composites for Pollution Control

Cited by 26 publications

References 41 publications

Artificial Intelligence Optimization of Turning Parameters of Nanoparticle‐Reinforced P/M Alloy Tool

Artificial Intelligence Optimization of Turning Parameters of Nanoparticle‐Reinforced P/M Alloy Tool

Machine Learning based Early-detection of Lung Cancer Using CT Scan for the early Identification and Classification

Characterisation of microcrystalline cellulose from waste green pea pod sheath and its sunn hemp fibre‐polyester composite: A step towards greener manufacturing

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