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International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Kaushal Kumar Rai Bhavya Gupta Pardeep Shokeen Pinaki Chakraborty Division of Computer Engineering Netaji Subhas University of Technology New Delhi India

Our first-hand experience with the recruiting process of companies helped us realize that existing systems judge coding responses solely on the basis of test cases passed and often additional manual involvement is needed to determine the quality of code. We aim to build an open source code base to automatically grade coding responses. This work will help save the tedious effort on the part of Subject Matter Experts (SMEs) by proving useful for grading responses in MOOCs (Massive Open Online Courses) as well as coding rounds in the recruitment process. We have built our work on foundations introduced in extant research in the area. We have achieved better precision (89%) in addition to better Pearson Correlation Coefficient (0.981) as compared to existing methods using the bag of words technique to calculate the distance vector. Various machine learning models were explored to achieve optimal results. These results and observations provide a promise of successful application of this technique in other areas requiring automated grading.

关键词： Support vector machines Feature extraction Encoding Kernel Machine learning Linear regression Programming

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RETRACTED: Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction

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Expert Systems 2023年第12期41卷

作者： Vaishali Baviskar Madhushi Verma Pradeep Chatterjee Gaurav Singal Thippa Reddy Gadekallu GH Raisoni Institute of Engineering and Technology Pune India Bennett University Greater Noida India TML Business Services Ltd. Pune India Netaji Subhas University of Technology Delhi India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Zhongda Group Jiaxing China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Development Lovely Professional University Phagwara India College of Information Science and Engineering Jiaxing University Jiaxing China

Recently, machine learning methods have been successfully used for the prediction of cardiovascular disease. Early diagnosis and prediction is necessary for giving effective treatment to avoid higher mortality rates. Several classification algorithms have been developed recently which satisfy the need, but show limited accuracy while predicting the heart disease. Hence, the focus of this study is on early prediction of heart disease and to improve the accuracy of prediction using benchmark heart disease datasets such as UCI Cleveland dataset and Heart disease clinical dataset by implementing effective classification and optimization algorithms. Optimization algorithms generally exhibit the benefit of dealing with complex non-linear issues with better adaptability and flexibility. The Emperor penguin optimization algorithm, which can select the best features for classification has been utilized in this study to improve the efficiency, minimize reconstruction errors, and increase the quality of heart disease classification. Further, the newly developed stacked sparse convolutional neural network based auto encoder (SSC-AE) classification algorithm has been employed for significant feature classification with higher robustness and efficacy. Accuracy, Area Under Curve (AUC), and F1 score are some of the measures used to compare the outcomes of several machine learning algorithms to those of the proposed model in this study. The results show that the proposed model, SSC-AE, is superior to other classification models.

关键词： Emperor penguin optimizer heart disease heart disease clinical dataset stacked sparse CNN based auto encoder UCI Cleveland dataset

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Performance investigation of MLR optical WDM network based on ITU-T conforming fibers in the presence of SRS, XPM and FWM

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International Journal of Information technology (Singapore) 2019年第2期11卷 213-227页

作者： Bajpai, Rochak Sengar, Sujata Iyer, Sridhar Singh, Shree Prakash Division of ECE Netaji Subhas Institute of Technology Sector-3 Dwarka 110078 New Delhi India Department of ECE Jain College of Engineering T S. Nagar Hunchanatti Cross Belagavi 590014 KA India

This article investigates the performance degradation of a mixed line rate (MLR) optical wavelength division multiplexing network in the presence of the combined FWM, XPM and SRS effects. For performance evaluation a novel mathematical model is developed which evaluates the results in terms of quality-factor. Further, the simulations are performed (i) based on the optical frequency grid defined by the ITU-T Recommendation G.692, and (ii) considering the ITU-T compliant optical fibers viz., G.652, G. 652D, G. 653, G. 654, G.655, and LEAF. The obtained results show that among the various considered fibers and modulation format configuration cases, irrespective of spacing between the channels (i) SMF (G.652) and DSF (G.653) fibers provide the best and the worst performances, respectively, and (ii) a combination of the central channel transmitting at 40 Gbps using the duo-binary modulation format with adjacent channels transmitting at 10 Gbps using OOK modulation format, provides the best performance. However, with increase in spacing between the channels, the performance is enhanced owing to mitigation of the deleterious non-linear effects. Overall, the results clearly show that choice of the data rate of both, the central channel and its adjacent channel has a major effect on the MLR network performance. © 2018, Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Cross phase modulation Four wave mixing Mixed line rate Physical layer impairments Stimulated Raman scattering Wavelength division multiplexing

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Multi-objective optimisation of electrical discharge machining for Inconel 825 using Taguchi-fuzzy approach

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International Journal of Information technology (Singapore) 2019年第1期11卷 97-105页

作者： Payal, Himanshu Maheshwari, Sachin Bharti, Pushpendra S. Sharma, Satish Kumar Division of Manufacturing Processes and Automation Engineering Netaji Subhas Institute of Technology New Delhi 110078 India U.S.I.C.T Guru Gobind Singh Indraprastha University New Delhi 110078 India Mechanical Engineering Department Thapar Institute of Engineering and Technology Patiala 147004 Punjab India

This paper employs Taguchi-fuzzy approach for parametric optimisation of electric discharge machining with multiple performance measures. In this work, seven input parameters (one of two levels and six of three levels) and two performance measures have been considered and the experiments are designed using Taguchi’s L₃₆ orthogonal array. A fuzzy model is formed using mamdani inference system and optimal combination of process parameters has been obtained on basis of multi-performance fuzzy index (MPFI) value calculated using different shapes of membership functions (MF) viz. triangular, trapezoidal and gaussian. Gaussian MF is found to provide better results as compared to triangular and trapezoidal MF. ANOVA analysis has also been carried out on MPFI to find out percentage contribution. It is shown with the help of confirmatory experiments that MRR and SR are improved by 103.25 and 32.11% respectively by employing the proposed approach. © 2018, Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： ANOVA EDM Metal removal rate Multi-objective optimization Surface roughness Taguchi-fuzzy

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Comparative study of mechanical properties of dry and water aged jute/flax/epoxy hybrid composite

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Materials Today: Proceedings 2020年 25卷 857-861页

作者： Vijay Chaudhary Rohit Sahu Ankit Manral Furkan Ahmad Mechanical Engineering Department Amity School of Engineering Amity University Noida India G. L. Bajaj Institute of Technology & Management Greater Noida India MPAE Division Netaji Subhas University of Technology (Formerly NSIT University of Delhi) Sec-3 Dwarka New Delhi India

Present study focuses on the manufacturing and mechanical characterization of hybrid jute/flax/epoxy composite in dry and one year water aged condition. Mechanical characterization includes the tensile, flexural, impact and hardness testing of the developed jute/flax/epoxy hybrid composite. Experimental findings clearly showed the percentage reduction in the corresponding property of the specimen after one year of water immersion as compared to dry specimen. Hybrid jute/flax/epoxy composite showed 37.74% reduction in tensile strength, 33.6% reduction in flexural strength and 2.08% reduction in hardness.

关键词： Mechanical characterization Water aged Natural fiber Epoxy Hybrid composites

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Modified binary dragonfly algorithm for feature selection in human papillomavirus-mediated disease treatment

Modified binary dragonfly algorithm for feature selection in...

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2018 International Conference On Communication, Computing and Internet of Things, IC3IoT 2018

作者： Sawhney, Ramit Jain, Roopal Department of Computer Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (纸本)9781538624586

Diseased caused through the rapid mediation of Human Papillomavirus (HPV) have surged in the recent decades. While there are a large amount of treatment methods, medical data is often voluminous, high dimensional and often has redundancy which make selection of a particular method difficult. Wrapper feature selection methods aim to extract a subset of features to improve computability as well as classification accuracy. To address this, we propose a modification to a relatively new evolutionary computation technique, the Binary Dragonfly algorithm (BDFA), by incorporating a penalty function for optimal feature selection. This wrapper based method using BDFA and Random forest classifier is employed on two treatment methods, Immunotherapy and Cryotherapy, showing an increase in both classification accuracy as well as feature reduction as compared to fuzzy rule based systems, genetic algorithms and random forest classifiers. © 2018 IEEE.

关键词： Genetic algorithms

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RETRACTED ARTICLE: On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems

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Arabian Journal for Science and engineering 2021年第3期48卷 4139-4139页

作者： Srivastava, Vishal Srivastava, Smriti Chaudhary, Gopal Guzmán-Guzmán, Xiomarah García-Díaz, Vicente Netaji Subhas Institute of Technology New Delhi India Netaji Subhas University of Technology (Erstwhile NSIT) New Delhi India BharatiVidyapeeth’s College of Engineering Paschim Vihar India Department of Computer Science University of Oviedo Oviedo Spain

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PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?

arXiv

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arXiv 2022年

作者： Huaulmé, Arnaud Harada, Kanako Nguyen, Quang-Minh Park, Bogyu Hong, Seungbum Choi, Min-Kook Peven, Michael Li, Yunshuang Long, Yonghao Dou, Qi Kumar, Satyadwyoom Lalithkumar, Seenivasan Hongliang, Ren Matsuzaki, Hiroki Ishikawa, Yuto Harai, Yuriko Kondo, Satoshi Mitsuishi, Mamoru Jannin, Pierre Univ Rennes INSERM LTSI UMR 1099 RennesF35000 France Department of Mechanical Engineering The University of Tokyo Tokyo113-8656 Japan VisionAI hutom Seoul Korea Republic of Johns Hopkins University Baltimore United States Zhejiang University Hangzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Netaji Subhas University of Technology Delhi India National University of Singapore Singapore Singapore The Chinese University of Hong Kong Hong Kong National Cancer Center Japan East Hospital Tokyo104-0045 Japan Muroran Institute of Technology Hokkaido Japan

Background and Objective: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the "PEg TRAnsfer Workflow recognition" (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value. Methods: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score. Results: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks). Conclusion: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, o

关键词： Modal analysis

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Simultaneous ethanol and hydrogen production by fermentation from Bon bogori (Ziziphus rugosa)

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Renewable Energy Focus 2018年 26卷 71-80页

作者： Sasmal, Soumya Goud, Vaibhav V. Mohanty, Kaustubha Division of Biological Sciences and Engineering Netaji Subhas Institute of Technology New Delhi110078 India Department of Chemical Engineering Indian Institute of Technology Guwahati Guwahati78103 India

Lignocellulosic ethanol is one of the utmost promising and happening technological options available to moderate greenhouse gas emissions from transportation sector. Present study used ultrasound-assisted lime pretreated lignocellulosic biomass of Bon bogori for saccharification to obtain fermentable sugar. The saccharification process yielded 6.89% sugar. The fermentation study was conducted using wastewater sludge as a source of microorganisms to produce bio-ethanol and bio-hydrogen. The study includes optimization of fermentation process using Taguchi methodology (multi response). Four important parameters such as type of reactors (stirred tank and fluidized bed reactor), temperature, pH of operation and organic loading ratio were considered for optimization process. The optimum production of both ethanol and bio-hydrogen were found to be 0.21 mol, and 30.8 ± 0.5-mmol respectively. © 2018 Elsevier Ltd

关键词： Saccharification

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Feature Selection for Cryotherapy and Immunotherapy Treatment Methods Based on Gravitational Search Algorithm

Feature Selection for Cryotherapy and Immunotherapy Treatmen...

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2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018

作者： Jain, Roopal Sawhney, Ramit Mathur, Puneet Department of Computer Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (纸本)9781538637012

Feature selection has been an active area of research which aims at identifying the most optimal subset of features that improves classification accuracy. Medical datasets contain numerous input features and require highly accurate predictions to facilitate better diagnosis. To address this problem of feature selection in medical data, an enhanced binary version of Gravitational Search Algorithm (GSA) is proposed which is based on law of gravity and attraction of masses. The proposed algorithm combines speed of Random Forest Classifier and optimization behavior of GSA. In this paper, a comprehensive review of our wrapper based proposed algorithm on Immunotherapy and Cryotherapy datasets for warts treatment using Random Forest Classifier to predict the response of the treatment is provided. The experimental results of the study show significant improvement in the accuracy of prediction. © 2018 IEEE.

关键词： Classification (of information)

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