Benthic quality assessment in a naturally- and human-stressed tropical estuary

Accurate remaining useful life (RUL) prediction has gained increasing attention in modern maintenance management. Considering the data privacy requirements of distributed multi-client collaborative training and the phenomenon of domain drift, how to accomplish the RUL prediction for distributed federation under cross-domain conditions needs in-depth research. In this context, the paper constructs a multi-hop graph pooling adversarial network based on distributed federated learning (MHGPAN-DFL) for the RUL prediction. In particular, this paper designs a multi-hop graph pooling adversarial network, which can decrease domain differences through adversarial transfer while achieving global modeling for input data. Furthermore, this paper designs a predictive model consistency strategy based on distributed federated learning. It dynamically assigns model weights to promote the generalization ability based on ensuring the privacy and security of local data in each client. This study confirms the efficacy of the proposed approach adopting the NASA aircraft turbofan engine dataset, and the bearing degradation dataset provided by Xi’an Jiaotong University. 

Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective

Machine learning has numerous advantages, especially in the rapid digitization of the manufacturing industry that combines data from manufacturing processes and quality measures. Predictive quality allows manufacturers to make informed predictions about the quality of their products by analyzing data gathered during production. The quality of the machining, the total cost and the computation time need to be improved using contemporary production processes. With this concern, a series of experiments were carried out on Bohler steel both in dry, Minimum Quantity Lubrication (MQL) and nano-MQL conditions in varying quantities to explore the tool wear. In comparison to dry conditions, the utilization of MQL in machining processes demonstrates significantly enhanced efficacy in mitigating flank wear. The reduction in flank wear ranges from around 5% to 20% to 25%, contingent upon the application of MQL on the flank face, rake face, or both faces simultaneously. After that, the results of the tests were evaluated with the models of machine learning (ML) to determine which environment was optimal for cutting under both real and artificial circumstances. 

Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models Deep Learning based Image Processing

Phytoplankton studies in early 1970s have shown the annual dominance of diatoms and a seasonal abundance of Trichodesmium in the lower reaches of the Cochin backwaters (CBW) and adjacent coastal Arabian Sea during the pre-summer monsoon period (February to May). Surprisingly, more recent literature shows a complete absence of Trichodesmium in the CBW after 1975 even though their seasonal occurrence in the adjacent coastal Arabian Sea continued without much change. In order to understand this important ecological feature, we analyzed the long-term water quality data (1965–2005) from the lower reaches of the CBW. The analyses have shown that salinity did not undergo any major change in the lower reaches over the years and values remained >30 throughout the period. In contrast, a tremendous increase was well marked in levels of nitrate (NO3) and phosphate (PO4) in the CBW after 1975 (av. 15 and 3.5 μM, respectively) compared with the period before (av. 2 and 0.9 μM, respectively). Monthly time series data collected in 2004–2005 period from the lower reaches of the CBW and coastal Arabian Sea have clearly shown that the physical characteristics like salinity, temperature, water column stability, and transparency in both regions are very similar during the pre-summer monsoon period. In contrast, the nutrient level in the CBW is several folds higher (NO3, 8; PO4, 4; SiO4, 10; and NH4, 19 μM) than the adjacent coastal Arabian Sea (NO3, 0.7; PO4, 0.5; SiO4, 0.9; and NH4, 0.6 μM). The historic and fresh time series data evidences a close coupling between enriched levels of nutrients and the absence of Trichodesmium in the Cochin backwaters

/pdf/impact-of-eutrophication-on-the-occurrence-of-trichodesmium-2872mf10ym.pdf

Impact of eutrophication on the occurrence of Trichodesmium in the Cochin backwaters, the largest estuary along the west coast of India

Cochin backwater system was studied with regard to dissolved silicate (DSi) to understand its seasonal distribution and behaviour during estuarine mixing. Silicate had a linear relationship with <alinity during the high river discharge period. During premonsoon period. a sharp decrease of silicate with distance in the middle regions between salinity range of 4 to 22 X 10·' in the estuary was observed. River discharge was found to modulate both estuarine residence time and the DSi supply. The substantial removal of DSi (approximately 50 %) during premonsoon was attributed 10 enhanced biological utilization. caused by the development of null zones in the middle area< of the estuary.

Chemical Oceanographic Studies of the Coastal Waters of Cochin

Abstract Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications. 

https://link.springer.com/content/pdf/10.1007/s10845-023-02074-8.pdf

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

Distribution of Benthic In fauna in the Cochin Backwaters In Relation To Environmental Parameters

Various conditions that affect the muscles, blood arteries, heart, valves, or internal electrical pathways that regulate muscle contraction are referred to as "heart diseases." This work propose a new heart disease prediction model. The three main steps of the suggested framework are feature extraction, proposed dimensionality reduction, and proposed optimal ensemble-based heart disease prediction. Here, the most crucial components of the collected input data were extracted first. For accurate prediction, a significant number of features have been retrieved. Using Normalized Mutual Information induced Principal Component Analysis (NM-PCA), a novel feature dimension reduction technique has been proposed to overcome these problems. The dimensionally reduced features obtained by NM-PCA are simultaneously trained by the SVM, RF, and KNN classifiers. The improved optimized RNN generates the final prediction result. This optimized RNN is trained using the relevant outputs from SVM, RF, and KNN. Deer Updated Moth flame Optimization is used to adjust the weight function of the optimized RNN since it makes the ultimate judgment (DUMFO). After utilizing DUMFO, the RNN's prediction accuracy has greatly increased. The Deer Hunting Optimization Algorithm (DHO) and the Moth Flame Optimization (MFO) algorithms are combined to generate the DUMFO. Finally, using both positive and negative criteria, the performance of the suggested methodology is compared to existing, methodologies. The accuracy of the projected model is 47.3%, 45.2%, 57.8%, 36.8%, 26.3%, 14.7%, 16.8%, 5.12% and 3.15% superior to existing models like DBN, NN, CNN, LSTM, PM-LU, GA, SMO+EC, SSO+EC, BOA+EC, WOA+EC, MFO+EC and DHOA+EC, respectively. 

A metaheuristic-enabled training system for ensemble classification technique for heart disease prediction

DeepDrive: A braking decision making approach using optimized GAN and Deep CNN for advanced driver assistance systems

Agriculture is the chief food division in India. Mainly most of the peoples are using the rice for food, even in world wide. But the diseases of rice plant threatening the food part. The appropriate recognition and initial prevention of rice leaf diseases are important for cultivating production. Novel method of image recognition with deep learning algorithm will prevent this disaster. The paper presents a deep model architecture to notice such diseases through the leaf of the rice. The model that has formed is trained to analyse diseased leaf and thereafter identify the type of disease from the rice leaf. The paper is planned to use InceptionV3 with transfer learning concept. The outcomes of this paper show that the transfer learning concept for rice leaf disease image recognition presents a speedy and reasonable detection. The accuracy outcomes in the identification of diseases exposed that the presented model is capable and can greatly influence the efficient identification of the diseases.

Deep learning based Rice plant Disease Identification with Transfer learning

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