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- 저자 : Amit Malik (검색결과 4 건)
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Amit Singla,Rajesh Malhotra,Vijay Kumar,Chandra Lekha,G. Karthikeyan,Vishwas Malik 대한정형외과학회 2015 Clinics in Orthopedic Surgery Vol.7 No.2
Background: Total knee arthroplasty (TKA) is associated with considerable blood loss. Computer-assisted surgery (CAS) is different from conventional TKA as it avoids opening the intramedullary canal. Hence, CAS should be associated with less blood loss. Methods: Fifty-seven patients were randomized into two groups of CAS and conventional TKA. In conventional group intramedullary femoral and extramedullary tibial jigs were used whereas in CAS group imageless navigation system was used. All surgeries were done under tourniquet. Total and hidden blood loss was calculated in both groups and compared. Results: The mean total blood loss was 980 mL in conventional group and 970 mL in CAS group with median of 1,067 mL (range, 59 to 1,791 mL) in conventional group and 863 mL (range, 111 to 2,032 mL) in CAS group. There was no significant difference in total blood loss between the two groups (p = 0.811). We have found significant hidden blood loss in both techniques, which is 54.8% of the total loss in the conventional technique and 59.5% in the computer-assisted navigation technique. Conclusions: There is no significant difference in total and hidden blood loss in the TKA in CAS and conventional TKA. However, there is significant hidden blood loss in both techniques. There was no relation of tourniquet time with blood loss.
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Performance Enhancement of Gate-Annealed AlGaN/GaN HEMTs
Somna S. Mahajan,Amit Malik,Robert Laishram,Seema Vinayak 한국물리학회 2017 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.70 No.5
The electrical performances of unannealed and post gate-annealed AlGaN/GaN High Electron Mobility Transistors (HEMTs) were analyzed. A considerable improvement in HEMT parameters such as the drain source current (Ids), transconductance (gm), gate reverse leakage current (Ir) and off-state breakdown voltage (Vboff ) were observed in optimally post gate-annealed HEMT devices. The improvement in the device parameters was correlated with the combined effects of an improved electron mobility and the removal of interface inhomogenity in the gated region as a result of gate annealing. The gate-annealed HEMTs, thus, delivered an output power of 5 W/mm at the S and the C bands.
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Khurma Ruba Abu,Alhenawi Esraa,Braik Malik,Hashim Fatma A,Chhabra Amit,Castillo Pedro A 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature Selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, i.e., they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, SO is employed to put the evolutionary algorithms’ preservation of the best premise into practice. This is accomplished by utilizing three alternative selection systems – tournament, proportional, and linear – to improve the exploration phase. These are used in exploration to allow solutions to be found more thoroughly and in relation to a chosen solution than at random. These are Tournament Logarithmic Snake Optimizer (TLSO), Proportional Logarithmic Snake Optimizer, and Linear Order Logarithmic Snake Optimizer. A number of 22 reference medical datasets were used in experiments. The findings indicate that, among 86% of the datasets, TLSO attained the best accuracy, and among 82% of the datasets, the best feature reduction. In terms of the standard deviation, the TLSO also attained noteworthy reliability and stability. On the basis of running duration, it is, nonetheless, quite effective.
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