Search results for: Arindam Roy

Authors: Arindam Chaudhuri

Abstract:

The research presents epsilon- hierarchical fuzzy twin support vector regression (epsilon-HFTSVR) based on epsilon-fuzzy twin support vector regression (epsilon-FTSVR) and epsilon-twin support vector regression (epsilon-TSVR). Epsilon-FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon-TSVR which takes care of uncertainty existing in forecasting problems. Epsilon-FTSVR determines a pair of epsilon-insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon-FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon-FTSVR is then reformulated as epsilon-HFTSVR consisting of a set of hierarchical layers each containing epsilon-FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon-HFTSVR has remarkable generalization performance with minimum training time.

Keywords: regression, epsilon-TSVR, epsilon-FTSVR, epsilon-HFTSVR

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Authors: Santu Dey, Arindam Bhattacharyya

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The object of the present paper is to study a quarter-symmetric metric connection in a Lorentzian α-Sasakian manifold. We study some curvature properties of Lorentzian α-Sasakian manifold with respect to quarter-symmetric metric connection. We investigate quasi-projectively at, Φ-symmetric, Φ-projectively at Lorentzian α-Sasakian manifolds with respect to quarter-symmetric metric connection. We also discuss Lorentzian α-Sasakian manifold admitting quartersymmetric metric connection satisfying P.S = 0, where P denote the projective curvature tensor with respect to quarter-symmetric metric connection.

Keywords: quarter-symmetric metric connection, Lorentzian alpha-Sasakian manifold, quasi-projectively flat Lorentzian alpha-Sasakian manifold, phi-symmetric manifold

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Authors: Arindam Pramanik, Parimal Karmakar

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We have explored the synergistic anti-cancer activity of copper ion and acetylacetone complex containing 1,3 diketone group (like curcumin) in metallorganic compound “Copper acetylacetonate” (CuAA). The cytotoxicity mechanism of CuAA complex was evaluated on various cancer cell lines in vitro. Among these, reactive oxygen species (ROS), glutathione level (GSH) in the cell was found to increase. Further mitochondrial membrane damage was observed. The fate of cell death was found to be induced by apoptosis. For application purpose, we have developed a novel biodegradable, non-toxic polymer-based nanoparticle which has hydrophobically modified core for loading of the CuAA. Folic acid is conjugated on the surface of the polymer (chitosan) nanoparticle for targeting to cancer cells for minimizing toxicity to normal cells in-vivo. Thus, this novel drug CuAA has an efficient anticancer activity which has been targeted specifically to cancer cells through polymer nanoparticle.

Keywords: anticancer, apoptosis, copper nanoparticle, targeted drug delivery

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Authors: Arindam Roy, Madhushree Das, Apurba Manna, Samir Maity

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In this research paper, an algorithmic approach is developed to solve a novel multi-objective four-dimensional traveling salesman problem (MO4DTSP) where different paths with various numbers of conveyances are available to travel between two cities. NSGA-II and Decomposition algorithms are modified to solve MO4DTSP in an IoT-based transport system. This IoT-based transport system can be widely observed, analyzed, and controlled by an extensive distribution of traffic networks consisting of various types of sensors and actuators. Due to urbanization, most of the cities are connected using an intelligent traffic management system. Practically, for a traveler, multiple routes and vehicles are available to travel between any two cities. Thus, the classical TSP is reformulated as multi-route and multi-vehicle i.e., 4DTSP. The proposed MO4DTSP is designed with traveling cost, time, and customer satisfaction as objectives. In reality, customer satisfaction is an important parameter that depends on travel costs and time reflects in the present model.

Keywords: multi-objective four-dimensional traveling salesman problem (MO4DTSP), decomposition, NSGA-II, IoT-based transport system, customer satisfaction

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Authors: Rajiv Kumar, Amit Sachan, Arindam Mukherjee

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Despite the transparency and benefits of e-government, and its potential to serve citizens better, there is low diffusion and adoption of e-government services in India. Limited access to computer and internet, lack of computer and internet skills, low trust in technology, and risk associated in using e-government services are major hindrances in e-government adoption in India. Despite a large number of citizens belonging to the non-adopter category, the government has made some services mandatory to be accessed online where citizens have no other choice. Also despite the digital divide, a large number of citizens prefer online access to government services. In such cases intermediaries like common service centers, internet café and services agents’ roles are significant for accessing e-government services. Hence research is needed to explore this. The study aims to investigate the role of intermediaries in online access to public services by citizens. Qualitative research methodology using semi-structured interview was used. The results show that intermediaries play an important role in bridging the digital divide. The study also highlights on what circumstances citizens are taking help of these intermediaries. The study then highlights its limitations and discusses scope for future study.

Keywords: adoption, digital divide, e-government, India, intermediaries

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Authors: Deepti Mishra, Arindam Modak, K. K. Pant, Xiu Song Zhao

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The promotional effect of mixed ceria-zirconia oxides (CZO) over the Mo/HZSM-5 catalyst for methane dehydroaromatization (MDA) reaction was studied. The surface and structural properties of the synthesized catalyst were characterized using a range of spectroscopic and microscopic techniques, and the correlation between catalytic properties and its performance for MDA reaction is discussed. The impregnation of CZO solid solution on Mo/HZSM-5 was observed to give an excellent catalytic performance and improved benzene formation rate (4.5 μmol/gcat. s) as compared to the conventional Mo/HZSM-5 (3.1 μmol/gcat. s) catalyst. In addition, a significant reduction in coke formation was observed in the CZO-modified Mo/HZSM-5 catalyst. The prevailing comprehension for higher catalytic activity could be because of the redox properties of CZO deposited Mo/HZSM-5, which acts as a selective oxygen supplier and performs hydrogen combustion during the reaction, which is indirectly probed by O₂-TPD and H₂-TPR analysis. The selective hydrogen combustion prevents the over-oxidation of aromatic species formed during the reaction while the generated steam helps in reducing the amount of coke generated in the MDA reaction. Thus, the advantage of CZO incorporated Mo/HZSM-5 is manifested as it promotes the reaction equilibrium to shift towards the formation of benzene which is favourable for MDA reaction.

Keywords: Mo/HZSM-5, ceria-zirconia (CZO), in-situ combustion, methane dehydroaromatization

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: Rajiv Kumar, Amit Sachan, Arindam Mukherjee

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Government agencies around the world, including India, are incorporating digital technologies and processes into their day-to-day operations to become more efficient. Despite low internet penetration (around 34.8% of total population) in India, Government of India has made some public services mandatory to access online (e.g. passport, tax filing).This is insisting citizens to access mandatory public services online. However, due to digital divide, all citizens do not have equal access to internet. In light of this, it is an interesting topic to explore how citizens are able to access mandatory online public services. It is important to understand how citizens are adopting these mandatory e-government services and how the adoption behavior of these mandatory e-government services is different or similar to adoption behavior of non-mandatory e-government services. The purpose of this research is to investigate the factors that influence adoption of mandatory and non-mandatory e-government services in India. A quantitative technique is employed in this study. A conceptual model has been proposed by integrating the influencing factors to adopt e-government services from previous studies. The proposed conceptual model highlights a comprehensive set of potential factors influencing the adoption of e-government services. The proposed model has been validated by keeping in view the local context of Indian society. Online and paper based survey was administered, collected data was analyzed and results have been discussed. A total of 463 valid responses were received and further the responses were analyzed. The research reveals that the influencing factors to adopt e-government services are not same for both mandatory and non-mandatory e-government services. There are some factors that influence adoption of both mandatory and non-mandatory e-government services but there are some which are relevant for either of mandatory and non-mandatory e-government services. The research findings may help government or concerned agencies in successfully implementing e-government services.

Keywords: adoption, e-government, India, mandatory, non-mandatory

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Authors: Arindam Chowdhury, Andres Tremante, Mohammadtaghi Moravej, Bodhisatta Hajra, Ioannis Zisis, Peter Irwin

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Extreme wind events, such as hurricanes, have caused significant damage to buildings, resulting in losses worth millions of dollars. The roof of a building is most vulnerable to wind-induced damage due to the high suctions experienced by the roof in extreme wind conditions. Wind turbines fitted to buildings can help generate energy, but to our knowledge, their application to wind load mitigation is not well known. This paper presents results from an experimental study to assess the effect of small wind turbines (developed and patented by the first and second authors) on the wind loads on a low rise building roof. The tests were carried out for an open terrain at the Wall of Wind (WOW) experimental facility at Florida International University (FIU), Miami, Florida, USA, for three cases – bare roof, roof fitted with wind turbines placed closer to the roof edges, and roof with wind turbines placed away from the roof edges. Results clearly indicate that the presence of the wind turbines reduced the mean and peak pressure coefficients (less suction) on the roof when compared to the bare deck case. Furthermore, the peak pressure coefficients were found to be lower (less suction) when the wind turbines were placed closer to the roof, than away from the roof. Flow visualization studies using smoke and gravel clearly showed that the presence of the turbines disrupted the formation of vortices formed by cornering winds, thereby reducing roof suctions and preventing lift off of roof coverings. This study shows that the wind turbines besides generating wind energy, can be used for mitigating wind induced damage to the building roof. Future research must be directed towards understanding the effect of these wind turbines on other roof geometries (e.g. hip/gable) in different terrain conditions.

Keywords: wall of wind, wind loads, wind turbine, building

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: Arindam Chaudhuri

Abstract:

Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Arindam Roy

Abstract:

Public policy is an intriguing terrain, which involves complex interplay of administrative, social political and economic components. There is hardly any fit-for all formulation of public policy as Lindbloom has aptly categorized it as a science of muddling through. In fact, policies are both temporally and contextually determined as one the proponents of policy sciences Harold D Lasswell has underscored it in his ‘contextual-configurative analysis’ as early as 1950s. Though, a lot of theoretical efforts have been made to make sense of this intricate dynamics of policy making, at the end of the day the applied area of public policy negates any such uniform, planned and systematic formulation. However, our policy makers seem to have learnt very little of that. Until recently, policy making was deemed as an absolutely specialized exercise to be conducted by a cadre of professionally trained seasoned mandarin. Attributes like homogeneity, impartiality, efficiency, and neutrality were considered as the watchwords of delivering common goods. Citizen or clientele was conceptualized as universal political or economic construct, to be taken care of uniformly. Moreover, policy makers usually have the proclivity to put anything into straightjacket, and to ignore the nuances therein. Hence, least attention has been given to the ground level reality, especially the socio-cultural milieu where the policy is supposed to be applied. Consequently, a substantial amount of public money goes in vain as the intended beneficiaries remain indifferent to the delivery of public policies. The present paper in the light of Reproductive Health Care policy in rural West Bengal has tried to underscore the criticality of socio-cultural factors in public health delivery. Indian health sector has traversed a long way. From a near non-existent at the time of independence, the Indian state has gradually built a country-wide network of health infrastructure. Yet it has to make a major breakthrough in terms of coverage and penetration of the health services in the rural areas. Several factors are held responsible for such state of things. These include lack of proper infrastructure, medicine, communication, ambulatory services, doctors, nursing services and trained birth attendants. Policy makers have underlined the importance of supply side in policy formulation and implementation. The successive policy documents concerning health delivery bear the testimony of it. The present paper seeks to interrogate the supply-side oriented explanations for the failure of the delivery of health services. Instead, it identified demand side to find out the answer. The state-led and bureaucratically engineered public health measures fail to engender demands as these measures mostly ignore socio-cultural nuances of health and well-being. Hence, the hiatus between supply side and demand side leads to huge wastage of revenue as health infrastructure, medicine and instruments remain unutilized in most cases. Therefore, taking proper cognizance of these factors could have streamlined the delivery of public health.

Keywords: context, policy, socio-cultural factor, uniformity

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