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40 results on '"Kumar, Harish"'

4. Human Disease Prognosis and Diagnosis Using Machine Learning

Author

Kumar, Sunil, Kumar, Harish, Agarwal, Rashi, Pathak, V. K., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep Kumar, editor, Kolekar, Maheshkumar H., editor, Tanwar, Sudeep, editor, Wierzchoń, Sławomir T., editor, and Bhatnagar, Raj K., editor

Published
2022
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5. Healthcare Assistant—A Tool to Predict Disease Using Machine Learning

Author

Joshi, Sujata, Kumar, Harish, Babu, Jagadish, Raju, Akhil, Nihaz, Mahammad, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Devendra Kumar, editor, Peng, Sheng-Lung, editor, Sharma, Rohit, editor, and Zaitsev, Dmitry A., editor

Published
2022
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9. Variable Selection Methods, Comparison and their Applications in Machine Learning: A Review.

Author

Thakur, Kirti, Kumar, Harish, and Snehmani

Subjects
MACHINE learning, FEATURE selection, TECHNOLOGICAL innovations, INFORMATION retrieval
Abstract

In the past decade, the availability of voluminous and high-dimensional datasets has immensely emerged with continuous technological innovations to foster new ways to gather and analyze data. Thus, feature selection has become a challenging task in areas of application like text classification, data analysis, prediction, information retrieval etc. Knowledge extraction using machine learning models usually depends on the quality and quantity of data that they operate on. Feature selection is one of the core concepts to identify and remove irrelevant as well as redundant information that may impact accuracy or have no impact on the results. Feature selection methods are discussed in this review paper along with practical limitations. Subsequently, the workflow to solve a feature selection problem is also elaborated with feature selection methods. In feature selection, many surveys and empirical assessments were performed in many areas like classification, prediction, regression, and clustering, respectively. [ABSTRACT FROM AUTHOR]

Published
2023

10. Light Weighted CNN Model to Detect DDoS Attack over Distributed Scenario.

Author

Kumar, Harish, Aoudni, Yassine, Ortiz, Geovanny Genaro Reivan, Jindal, Latika, Miah, Shahajan, and Tripathi, Rohit

Subjects
DENIAL of service attacks, DEEP learning, BLENDED learning, COMPUTER network protocols, MACHINE learning, 5G networks
Abstract

The minimal-degree distributed denial-of-service attack takes advantage of flaws in the adaptive mechanisms of network protocols, which could have a big impact on network service quality. It is very hard to find, has a low attack rate, and comes at a set time. Detection methods that have been used before have problems because they only use one type of detection and are not very good at identifying the object. In the end, a way to detect many sorts of minimal DDoS assaults that use deep hybrid learning is suggested. To construct multi-type limited DDoS threat data sets and mimic diverse sorts DDoS assaults and legitimate traffic in varying situations in the 5G setting, collect congestion at the networking entry and extract flow feature info are considered. From a statistical threshold and feature engineering point of view, these data sets show how many sorts of minimal DDoS assaults are there. This study aims to develop a deep hybrid learning-based multi-type low-rate DDoS attack detection solution for 5G networks which is the novel model that is recently deployed, and a hybrid deep learning algorithm was used to train the algorithm offline, and the algorithm's performance was compared to that of the LSTM-Light GBM and LSTM-RF algorithms. The CNN-RF revealing model was then used to detect minimal DDoS assaults at the gateway, so that multiple attacks could be detected at the same time. It can identify 4 sorts of low-rate DDoS assaults like Slow-Headers, Slow-Body, Slow-Read, and Shrew assaults, in a 120-second window. The false intercept rate is 11.03 percent. This means that 96.22 percent of traffic could be found. Using the strategy suggested can help cut down on the traffic concentration of minimal DDoS attacks at the net ingress. It can also be used in real-world situations. [ABSTRACT FROM AUTHOR]

Published
2022
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11. A computer vision-based system for real-time component identification from waste printed circuit boards.

Author

Sharma, Himanshu and Kumar, Harish

Subjects
*DEEP learning, *PRINTED circuits, *COMPUTER systems, *MACHINE learning, *ELECTRONIC equipment, *RECYCLING & the environment, *ELECTRONIC waste
Abstract

With an exponential increase in consumers' need for electronic products, the world is facing an ever-increasing economic and environmental threat of electronic waste (e-waste). To minimize their adverse effects, e-waste recycling is one of the pivotal factors that can help in minimizing the environmental pollution andto increase recovery of valuable materials. For instance, Printed Circuit Boards (PCBs), while they have several valuable elements, they are hazardous too; and therefore, they form a large chunk of e-waste being generated today. Thus, in recycling PCBs, Electronic Components (ECs) are segregated at first, and separately processed for recovering key elements that could be re-used. However, in the current recycling process, especially in developing nations, humans manually screen ECs, which goes on to affect their health. It also causes losses of valuable materials. Therefore, automated solutions need to be adopted for both to classify and to segregate ECs from waste PCBs. The study proposes a robust EC identification system based on computer vision and deep learning algorithms (YOLOv3) to automate sorting process which would help in further processing. The study uses a publicly available dataset, and a PCB dataset which reflect challenging recycling environments like lighting conditions, cast shadows, orientations, viewpoints, and different cameras/resolutions. The outcome of YOLOv3 detection model based on training of both datasets presents satisfactory classification accuracy and capability of real-time competent identification, which in turn, could help in automatically segregating ECs, while leading towards effective e-waste recycling. • Methodology to automatically identify and classify electronic components from Waste Printed Circuits Boards. • Use of Computer Vision algorithm to detect Integrated Circuits in real-time. • Comparative performance analysis of the trained model using two distinct datasets. • Robust model with consistent performance regarding the recycling conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Progressive Machine Learning Approach with WebAstro for Web Usage Mining.

Author

Kumar, Harish, Anuradha, Solanki, A.K., and Singh, Krishna Kant

Subjects
WEB analytics, MACHINE learning, INFORMATION retrieval, WEB browsing, DATA mining, WEB databases, MOTION analysis
Abstract

Information retrieval is a scorching today when the flare-up of data on the Internet increasingly day by day. Due to the infinite growth and availability of the Internet; numbers of educational sites are amplified. Huge Expansion in knowledge puts a special effect on the education system. This helps in upgrading higher education. Interest in Web usage mining has grown swiftly in its short history, both in exploring interest and research. Web usage mining is a type of data mining that extracts the server log information after users browse web pages. For extracting and retrieval information machine learning approaches are designed to recognize useful information from text documents automatically. The main aim of this paper is to cram weblogs using the clustering technique with WEBASTRO tool for predicting next user movement and user behaviour analysis. This paper presents an ideal approach for web mining and predicting their behaviour for the next visit and automatic web site modification. [ABSTRACT FROM AUTHOR]

Published
2020
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13. Bifunctional robots inducing targeted protein degradation.

Author

Sobhia, M Elizabeth, Kumar, Harish, and Kumari, Sonia

Subjects
*MACHINE learning, *PROTEOLYSIS, *UBIQUITINATION, *ARTIFICIAL intelligence, *ROBOTS, *NEURODEGENERATION
Abstract

The gaining importance of Targeted Protein Degradation (TPD) and PROTACs (PROteolysis-TArgeting Chimeras) have drawn the scientific community's attention. PROTACs are considered bifunctional robots owing to their avidity for the protein of interest (POI) and E3-ligase, which induce the ubiquitination of POI. These molecules are based on event-driven pharmacology and are applicable in different conditions such as oncology, antiviral, neurodegenerative disease, acne etc., offering tremendous scope to researchers. In this review, primarily, we attempted to compile the recent works available in the literature on PROTACs for various targeted proteins. We summarized the design and development strategies with a focus on molecular information of protein residues and linker design. Rationalization of the ternary complex formation using Artificial Intelligence including machine & deep learning models and traditionally followed computational tools are also included in this study. Moreover, details describing the optimization of PROTACs chemistry and pharmacokinetic properties are added. Advanced PROTAC designs and targeting complex proteins, is summed up to cover the wide spectrum. [Display omitted] • This review summarises the design and development aspects of PROTACs that require an extensive understanding of cooperativity and receptivity of ternary complex. • The rational PROTAC design using Artificial Intelligence methodologies such as machine & deep learning models and with the use of modernistic computational tools is elaborated. • Optimization of PROTACs chemistry, accommodation of physicochemical parameters and advanced PROTAC designs are highlighted. [ABSTRACT FROM AUTHOR]

Published
2023
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14. A Reference Dataset for Network Traffic Activity Based Intrusion Detection System.

Author

Singh, Raman, Kumar, Harish, and Singla, R. K.

Subjects
INTRUSION detection systems (Computer security), TRAFFIC engineering, MACHINE learning, NETWORK routing protocols, TRAINING
Abstract

The network traffic dataset is a crucial part of anomaly based intrusion detection systems (IDSs). These IDSs train themselves to learn normal and anomalous activities. Properly labeled dataset is used for the training purpose. For the activities based IDSs, proper network traffic activity labeled dataset is the first requirement, however non-availability of such datasets is bottlenecked in the field of IDS research. In this experiment, a synthetic dataset "Panjab University - Intrusion Dataset (PU-IDS)" is created. The purpose of this study is to provide the researchers a reference dataset for the performance evaluation of network traffic activity based IDSs. University of New Brunswick Network Security Laboratory - Knowledge Disscovery in Databases (NSL-KDD) is a benchmark dataset for anomaly detection but it does not contain activity based labeling. So basic characteristics of this dataset are taken for the generation of the new synthetic dataset with various activities based labels. The dataset is first categorized as per protocol and service. Thereafter, as per minimum & maximum values of attributes, activity profiles are synthetically generated. This paper also discusses various statistical characteristics of PU-IDS. The total number of 198533 instances along with 273 of activity profiles are created. This dataset also contain different 98 protocol_service profiles. [ABSTRACT FROM AUTHOR]

Published
2015
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22. Soft Metrology : Concept and Challenges from Uncertainty Estimation

Author

Vallejo, Marcela, Bahamón, Nelson, Rossi, Laura, Delgado-Trejos, Edilson, Garg, Naveen, Section editor, Varshney, Ansul, Section editor, Jaiswal, S. K, Section editor, Nagla, K. S, Section editor, Yadav, Sanjay, Section editor, Aswal, Dinesh K., editor, Yadav, Sanjay, editor, Takatsuji, Toshiyuki, editor, Rachakonda, Prem, editor, and Kumar, Harish, editor

Published
2023
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24. Random Forest Algorithm for Forest Fire Prediction

Author

Fan, Gaolun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yadav, Sanjay, editor, Kumar, Harish, editor, Kankar, Pavan Kumar, editor, Dai, Wanyang, editor, and Huang, Fenghua, editor

Published
2023
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25. The Study for the Influencing Factors of the Box Office and Prediction Based on Machine Learning Models

Author

Chen, Shengyi, Ni, Shuyu, Zhang, Zhihan, Zhang, Zibo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yadav, Sanjay, editor, Kumar, Harish, editor, Kankar, Pavan Kumar, editor, Dai, Wanyang, editor, and Huang, Fenghua, editor

Published
2023
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26. Attendance Management System Using Modern Face Recognition and Gesture Recognition Using Deep Learning

Author

Ekka, Mahaba Ullas, Mze, Omar ALi, Singh, Tarun, Raghava, N. S., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Singari, Ranganath M., editor, Jain, Prashant Kumar, editor, and Kumar, Harish, editor

Published
2023
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39. Automatic Plastic Sorting Machine Using Audio Wave Signal

Author

Devendra Kumar, S. M., Prashanth, S., Medhashree, Rani, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Singari, Ranganath M., editor, Mathiyazhagan, Kaliyan, editor, and Kumar, Harish, editor

Published
2021
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40. Optimal design of CH4 pyrolysis in a commercial CVD reactor using support vector machines and Nelder-Mead algorithm.

Author

Gupta, Anand, Mudgal, Abhisek, Shinde, Vijay M., Kumar, Harish, and Prasad, N. Eswara

Subjects
*SUPPORT vector machines, *SUPERVISED learning, *CHEMICAL vapor deposition, *PYROLYSIS, *ALGORITHMS, *WALLS
Abstract

• Development of comprehensive 3D CFD model for commercial hot wall reactor. • Understanding of the critical parameters affecting the film performance. • Correlation of growth patterns and operating conditions using support vector machine. • Optimal design using machine learning combined with Nelder-Mead algorithms. Chemical vapour deposition (CVD) of pyrocarbon (PyC) effectively fabricates advanced carbon materials. Controlling the nanotexture of PyC is critical for the desired application. Reactor operating conditions, including temperature, pressure, inlet flow rate, reactant concentration, govern thickness, and film uniformity. The optimal film performance can be obtained by selecting appropriate process conditions. However, the optimisation of the CVD reactor is challenging due to the highly nonlinear and multi-variable nature of the process. In this study, the support vector machine, a robust supervised learning algorithm and Nelder-Mead algorithm are coupled to optimise the CH 4 pyrolysis in a commercial CVD reactor. To this end, a comprehensive CFD model for CH 4 pyrolysis in a commercial CVD reactor is first constructed. The model accuracy is improved by considering temperature-dependent transport properties of gas mixture and incorporating the detailed gas and surface chemistry (14 species and 32 reactions). The deposition rate and film uniformity are then obtained using a parametric study. Subsequently, the support vector machine (SVM) is employed to deduce the correlation between the PyC growth rate/film uniformity and operating variables. It has been found that the accuracy of SVM is better than the linear regression model. Finally, SVM coupled with the Nelder-Mead algorithm is proposed to optimise the CVD process parameters to maximise the PyC film quality. [ABSTRACT FROM AUTHOR]

Published
2022
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