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Wyszukujesz frazę "Harikumar, P. S. P" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Treatment of Heavy Metals From Water by Electro-Phytoremediation Technique
Autorzy:
Harikumar, P. S. P
Megha, T.
Powiązania:
https://bibliotekanauki.pl/articles/124037.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
phytoremediation
Eichhornia crassipes
bioconcentration factor
translocation abilit
Opis:
The performance of electrically stimulated phytoremediation in the removal of lead, cadmium and copper was assessed in this study. A combination of phyto and electro remediation was attempted in this study for the remediation of the metals from water. Three tanks were setup with different operating conditions for this experiment: control A (only phytoremediation system), control B (only electro remediation) and treatment (combination of phyto and electro remediation). The electrically enhanced phytoremediation system and electro remediation system were operated 2h/day at voltages of 4V for 25 days continuously. In this experiment, the Eichhornia crassipes, an able phytoremediator exhibited efficient and fast removal of heavy metals from synthetic solution in electro assisted phytoremediation system. The electrically enhanced phytoremediation using aluminum sheet electrodes showed better and effective removal of Cd, Pb and Cu than aluminum rod electrodes. A more favorable and moderate increase of pH was noticed in electrically stimulated phytoremediation system. Eichhornia crassipes has tremendous potential to reduce maximum amount of cadmium (within 15 days), lead (within 15 days) and copper (within 10 days) under electrically stimulated condition. Under electrified condition, maximum amount of Cd and Cu was accumulated in the aerial parts of Eichhornia crassipes but maximum concentration of Pb was attained by roots. This indicates the high heavy metal accumulation capacity of Eichhornia crassipes under electrified conditions. The results showed that 4V voltage is probably suitable to stimulate the Eichhornia crassipes to synthesize more chlorophyll and voltage can improve growth and ability to resist adverse circumstances by promoting chlorophyll synthesis. Eichhornia crassipes stimulated by an electric field has grown better and assimilated more metal. Bioconcentration factor (BCF) an index of hyperaccumulation, indicates that electrically stimulated Eichhornia crassipes is a good hyper accumulator of Cd (BCF = 1118.18) and Cu (BCF = 1152.47) and a moderate accumulator of Pb (BCF = 932.26). Translocation ability (TA) ratio indicates that Eichhornia crassipes have the ability to translocate more amounts of Pb, Cd and Cu to its upper portion under electrified condition. The results imply that the electro-phytoremediation technique seems to be promising in the treatment of wastewater contaminated with heavy metals.
Źródło:
Journal of Ecological Engineering; 2017, 18, 5; 18-26
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applicability of artificial intelligence in smart healthcare systems for automatic detection of Parkinson’s Disease
Autorzy:
Pallathadka, Harikumar
Padminivalli V., S.J.R.K.
Vasavi, M.
Nancy, P.
Naved, Mohd
Kumar, Harish
Ray, Samrat
Powiązania:
https://bibliotekanauki.pl/articles/38709253.pdf
Data publikacji:
2024
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
Parkinson’s disease
detection
machine learning
relief algorithm
LDA algorithm
SVM-RBF
accuracy
sensitivity
specificity
choroba Parkinsona
wykrywanie
nauczanie maszynowe
algorytm ulgi
Algorytm LDA
dokładność
wrażliwość
specyficzność
Opis:
Parkinson’s disease is associated with memory loss, anxiety, and depression in the brain. Problems such as poor balance and difficulty during walking can be observed in addition to symptoms of impaired posture and rigidity. The field dedicated to making computers capable of learning autonomously, without having to be explicitly programmed, is known as machine learning. An approach to the diagnosis of Parkinson’s disease, which is based on artificial intelligence, is discussed in this article. The input for this system is provided through photographic examples of Parkinson’s disease patient handwriting. Received photos are preprocessed using the relief feature option to begin the process. This is helpful in the process of selecting characteristics for the identification of Parkinson’s disease. After that, the linear discriminant analysis (LDA) algorithm is employed to reduce the dimensions, bringing down the total number of dimensions that are present in the input data. The photos are then classified via radial basis function-support vector machine (SVM-RBF), k-nearest neighbors (KNN), and naive Bayes algorithms, respectively.
Źródło:
Computer Assisted Methods in Engineering and Science; 2024, 31, 2; 175-185
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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