WEIGHTED COMPOSITION OPERATORS ON WEIGHTED SPACES OF VECTOR-VALUED ANALYTIC FUNCTIONS

Manhas, Jasbir Singh Korean Mathematical Society 2008 대한수학회지 Vol.45 No.5

Let V be an arbitrary system of weights on an open connected subset G of ${\mathbb{C}}^N(N{\geq}1)$ and let B (E) be the Banach algebra of all bounded linear operators on a Banach space E. Let $HV_b$ (G, E) and $HV_0$ (G, E) be the weighted locally convex spaces of vector-valued analytic functions. In this paper, we characterize self-analytic mappings ${\phi}:G{\rightarrow}G$ and operator-valued analytic mappings ${\Psi}:G{\rightarrow}B(E)$ which generate weighted composition operators and invertible weighted composition operators on the spaces $HV_b$ (G, E) and $HV_0$ (G, E) for different systems of weights V on G. Also, we obtained compact weighted composition operators on these spaces for some nice classes of weights.

A novel orange-red emitting Ba2Ca(BO3)2:Sm3+ phosphor to fill the amber gap in LEDs: Synthesis, structural and luminescence characterizations

M. Manhas,Vinay Kumar,Vivek K. Singh,J. Sharma,Ram Prakash,Vishal Sharma,A.K. Bedyal,H.C. Swart 한국물리학회 2017 Current Applied Physics Vol.17 No.11

The present paper reports on the structural and luminescent properties of un-doped and Sm3þ doped Ba2Ca(BO3)2 phosphors synthesized by the conventional solid state method. For structural characterizations, the X-ray diffraction, FTIR spectroscopy and Rietveld refinement method were used. The FTIR spectrum was composed of basic BO3 and BO4 structural units of borates. The Sm3þ doped phosphors under 402 nm (6H5/2/4L13/2) excitation, showed an orange red emission corresponding to the 601 nm (4G5/2 / 6H7/2) transition of the Sm3þ ion. An increase in the PL emission intensity was observed up to 2 mol % with the increase in Sm3þ ions concentration. The critical distance between the Sm3þ e Sm3þ ions were found to be 24.36 Å. Moreover, the phosphors decaytime and optical bandgap at different concentration of Sm3þ ion also have been discussed in details. All the results show that Ba2Ca(BO3)2:Sm3þ phosphor may be used with a near ultraviolet (n-UV) chip to fill the amber gap in light emitting diodes (LEDs).

Weighted composition operators on weighted spaces of vector-valued analytic functions

Jasbir Singh Manhas 대한수학회 2008 대한수학회지 Vol.45 No.5

Let V be an arbitrary system of weights on an open connected subset G of CN (N ≥ 1) and let B (E) be the Banach algebra of all bounded linear operators on a Banach space E. Let HVb (G,E) and HV0 (G,E) be the weighted locally convex spaces of vector-valued analytic functions. In this paper, we characterize self-analytic mappings φ : G → G and operator-valued analytic mappings Ψ : G → B (E) which generate weighted composition operators and invertible weighted composition operators on the spaces HVb (G,E) and HV0 (G,E) for different systems of weights V on G. Also, we obtained compact weighted composition operators on these spaces for some nice classes of weights. Let V be an arbitrary system of weights on an open connected subset G of CN (N ≥ 1) and let B (E) be the Banach algebra of all bounded linear operators on a Banach space E. Let HVb (G,E) and HV0 (G,E) be the weighted locally convex spaces of vector-valued analytic functions. In this paper, we characterize self-analytic mappings φ : G → G and operator-valued analytic mappings Ψ : G → B (E) which generate weighted composition operators and invertible weighted composition operators on the spaces HVb (G,E) and HV0 (G,E) for different systems of weights V on G. Also, we obtained compact weighted composition operators on these spaces for some nice classes of weights.

Comparative Study to Measure the Performance of Commonly Used Machine Learning Algorithms in Diagnosis of Alzheimer's Disease

kumar, Neeraj,manhas, Jatinder,sharma, Vinod Korea Multimedia Society 2019 The journal of multimedia information system Vol.6 No.2

In machine learning, the performance of the system depends upon the nature of input data. The efficiency of the system improves when the behavior of the input data changes from un-normalized to normalized form. This paper experimentally demonstrated the performance of KNN, SVM, LDA and NB on Alzheimer's dataset. The dataset undertaken for the study consisted of 3 classes, i.e. Demented, Converted and Non-Demented. Analysis shows that LDA and NB gave an accuracy of 89.83% and 88.19% respectively in both the cases whereas the accuracy of KNN and SVM improved from 46.87% to 82.80% and 53.40% to 88.75% respectively when input data changed from un-normalized to normalized state. From the above results it was observed that KNN and SVM show significant improvement in classification accuracy on normalized data as compared to un-normalized data, whereas LDA and NB reflect no such change in their performance.

Evaluation of Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means based ANFIS System in Diagnosis of Alzheimer

Kour, Haneet,Manhas, Jatinder,Sharma, Vinod Korea Multimedia Society 2019 The journal of multimedia information system Vol.6 No.2

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

DIFFERENCES OF DIFFERENTIAL OPERATORS BETWEEN WEIGHTED-TYPE SPACES

Al Ghafri, Mohammed Said,Manhas, Jasbir Singh Korean Mathematical Society 2021 대한수학회논문집 Vol.36 No.3

Let 𝓗(𝔻) be the space of analytic functions on the unit disc 𝔻. Let 𝜓 = (𝜓_j)ⁿ_j=0 and 𝚽 = (𝚽_j)ⁿ_j=0 be such that 𝜓_j, 𝚽_j ∈ 𝓗(𝔻). The linear differential operator is defined by T_𝜓(f) = ∑ⁿ_j=0 𝜓_jf^(j), f ∈ 𝓗(𝔻). We characterize the boundedness and compactness of the difference operator (T_𝜓 - T_𝚽)(f) = ∑ⁿ_j=0 (𝜓_j - 𝚽_j) f^(j) between weighted-type spaces of analytic functions. As applications, we obtained boundedness and compactness of the difference of multiplication operators between weighted-type and Bloch-type spaces. Also, we give examples of unbounded (non compact) differential operators such that their difference is bounded (compact).

Improved Classification of Cancerous Histopathology Images using Color Channel Separation and Deep Learning

Gupta, Rachit Kumar,Manhas, Jatinder Korea Multimedia Society 2021 The journal of multimedia information system Vol.8 No.3

Oral cancer is ranked second most diagnosed cancer among Indian population and ranked sixth all around the world. Oral cancer is one of the deadliest cancers with high mortality rate and very less 5-year survival rates even after treatment. It becomes necessary to detect oral malignancies as early as possible so that timely treatment may be given to patient and increase the survival chances. In recent years deep learning based frameworks have been proposed by many researchers that can detect malignancies from medical images. In this paper we have proposed a deep learning-based framework which detects oral cancer from histopathology images very efficiently. We have designed our model to split the color channels and extract deep features from these individual channels rather than single combined channel with the help of Efficient NET B3. These features from different channels are fused by using feature fusion module designed as a layer and placed before dense layers of Efficient NET. The experiments were performed on our own dataset collected from hospitals. We also performed experiments of BreakHis, and ICML datasets to evaluate our model. The results produced by our model are very good as compared to previously reported results.

A Comparison Study on Tourists’ Perceptions of and Intentions to Visit Different Cultural Regions - The Case of Tibet -

노희경,조상희,Parikshat Singh Manhas,김연종,박창수,장재협 한국사진지리학회 2019 한국사진지리학회지 Vol.29 No.1

The purpose of this study was to examine differences in the tourists’ perceptions of and their intentions to visit different cultural regions as a tourist destination by nationality. For the study, Tibet was designated as a cultural region for the tourist destination. A questionnaire was administered to 438 people who were living in six countries such as China, India, South Korea, Laos, Mongolia, and the U.S.A. The analyses were conducted using SPSS 21.0. The descriptive analysis, factor analysis, reliability analysis, one-way ANOVA and multiple regression analysis were applied to analyze the data. The results prove the existence of differences in the perceived images for the different cultural regions by nationality. The lack of information was the main factor impeding their intentions to visit different cultural regions, followed by cultural backwardness, politics and economic issues, and inconvenience.

Navigated Unicompartmental Knee Arthroplasty: A Different Perspective

Rajesh Malhotra,Saurabh Gupta,Vivek Gupta,Vikrant Manhas 대한정형외과학회 2021 Clinics in Orthopedic Surgery Vol.13 No.4

Background: Anteromedial osteoarthritis is a recognized indication for unicompartmental knee arthroplasty (UKA). Favorable postoperative outcomes largely depend on proper patient selection, correct implant positioning, and limb alignment. Computer navigation has a proven value over conventional systems in reducing mechanical errors in total knee arthroplasty (TKA). However, the lack of strong evidence impedes the universal use of computer navigation technology in UKA. Therefore, this study was proposed to investigate the accuracy of component positioning and limb alignment in computer navigated UKA and to observe the role of navigation in proper patient selection. Methods: A total of 50 knees (38 patients) underwent computer navigated UKA between 2016 and 2018. All operations were performed by the senior surgeon using the same navigation system and implant type. The navigation system was used as a tool to aid patient selection: knees with preoperative residual varus > 5° on valgus stress and hyperextension > 10° were switched to navigated TKA. We measured the accuracy of component placement in sagittal and coronal planes on postoperative radiographs. Functional outcomes were also evaluated at the final follow-up (a minimum of 16 months). Results: Nine patients had tibia vara and 14 patients had preoperative hyperextension deformity. We observed coronal outliers for the tibial component in 12% knees and for the femoral component in 10% knees. We also observed sagittal outliers for the tibial component in 14% knees and for the femoral component in 6% knees. There was a significant improvement in the functional score at the final follow-up. On multiple linear regression, no difference was found in functional scores of knees with or without tibia vara (p = 0.16) and with or without hyperextension (p = 0.25). Conclusions: Our study further validates the role of computer navigation in desirable implant positioning and limb alignment. We encourage use of computer-assisted navigation as a tool for patient selection, as it allows intraoperative dynamic goniometry and provides real-time kinematic behavior of the knee to obviate pitfalls such as significant residual varus angulation and hyperextension that predispose early failure of UKA.

Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium

Gupta, Rachit Kumar,Kaur, Mandeep,Manhas, Jatinder Korea Multimedia Society 2019 The journal of multimedia information system Vol.6 No.2

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.