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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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Photon orbits and phase transition for Letelier AdS black holes immersed in perfect fluid dark matter

arXiv

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arXiv 2024年

作者： Sood, Ashima Ali, Md Sabir Singh, J.K. Ghosh, Sushant G. Department of Mathematics Netaji Subhas University of Technology New Delhi110078 India Centre for Theoretical Physics Jamia Millia Islamia New Delhi110025 India Department of Physics Mahishadal Raj College West Bengal Purba Medinipur721628 India Institute of Theoretical Physics Research Center of Gravitation Lanzhou University Lanzhou730000 China Key Laboratory of Quantum Theory and Applications of MoE Lanzhou University Lanzhou730000 China Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Province Lanzhou University Lanzhou730000 China Astrophysics and Cosmology Research Unit School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Private Bag X54001 Durban4000 South Africa

We obtain an exact solution of spherically symmetric Letelier AdS black holes immersed in perfect fluid dark matter (PFDM). Considering the cosmological constant as the positive pressure of the system and volume as its conjugate variable, we analyse the thermodynamics of our black holes in the extended phase space. Owing to the background clouds of strings parameter (a) and the parameter endowed with PFDM (β), we analyse the Hawking temperature, entropy and specific heat. We also investigate the relationship between the photon sphere radius and the phase transition for the Letelier AdS black holes immersed in PFDM. Through the analysis, we find with a particular condition, there are non-monotonic behaviours between the photon sphere radius, the impact parameter, the PFDM parameter, temperature, and pressure. We can regard both the changes of photon sphere radius and impact parameter before and after phase transition as the order parameter;their critical exponents near the critical point are equal to the same value 1/2, just like ordinary thermal systems. These indicate that a universal relation of gravity may exist near the critical point for a black hole thermodynamic system. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravitation

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Rabies vaccines: Journey from classical to modern era

Veterinary Vaccine

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Veterinary Vaccine 2025年第1期4卷

作者： Bushra Khan Nidhi Shrivastava Naheed Parveen Sheikh Pramod Kumar Singh Hem Chandra Jha Hamendra Singh Parmar School of Biotechnology Devi Ahilya University Takshashila Campus Khandwa Road Indore India College of Veterinary Science and Animal Husbandry Mhow NDVSU M.P. India Department of Biotechnology Netaji Subhas University Jamshedpur Jharkhand India Discipline of Biosciences and Biomedical Engineering Indian Institute of Technology Indore India

Rabies, caused by the neurotropic rabies virus, remains a significant public health concern worldwide. It remains a deadly zoonotic disease with a near 100 % fatality rate once clinical symptoms manifest, causing about 59,000 deaths annually, of which 59.6 % occur in Asia and 36.4 % in Africa. Dog-mediated rabies accounts for over 99 % of human cases. This review provides a comprehensive overview of rabies, covering its epidemiology, pathogenesis, Etiology, and developments in rabies vaccines. Once the virus enters the body through the bite of an infected animal it travels via peripheral nerves to the central nervous system, leading to fatal encephalitis if left untreated. Vaccination of domestic animals plays a pivotal role in preventing transmission to humans. Post-exposure prophylaxis (PEP) remains the cornerstone of rabies prevention in individuals exposed to potentially infected animals, comprising rabies vaccine and Rabies immunoglobulin administration. Advances in molecular virology have shed light on the pathogenesis of rabies, revealing the intricate interactions between the virus and the host immune system. Despite decades of research, treatment options for established rabies infection remain limited, emphasizing the importance of preventive measures. Experimental therapies, including monoclonal antibodies and novel antiviral agents, promise to improve outcomes in rabies patients. Regardless of the established efficacy of rabies vaccines, challenges remain in ensuring widespread accessibility and coverage, particularly in resource-limited regions. Strategies to enhance pre-exposure prophylaxis with affordable vaccine delivery are essential for achieving global rabies control and elimination goals, underscoring the need for sustained surveillance, vaccination, and public awareness efforts. Continued research into the virology and immunology of rabies is essential for the development of novel interventions to combat this deadly disease.

关键词： Rabies vaccines Post-exposure prophylaxis (PEP) Pre-exposure prophylaxis (PrEP) Dog mediated Zoonotic

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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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Cyberbullying: A Risk of CyberAddiction

Cyberbullying: A Risk of CyberAddiction

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2018 IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018

作者： Sarna, Geetika Bhatia, M.P.S. Computer Engineering Department Netaji Subhas Institute of Technology New Delhi India

ISBN: (纸本)9781538641194

With the advancement in the digital technologies, the internet has become major break-through and has been universally acceptable technology by the people of all the ages. They use the internet for the purpose of entertainment, shopping, gossiping, and playing games and also for harming the other users in the form of promoting fraud, cyberbullying and sometimes terrorism. However, overuse of internet which is often called cyberaddiction results in some negative implications such as health disorders, poor social relationships, poor academic performance, cyberbullying and so on. For this paper, we have surveyed people using questionnaire on the subject related to internet usage and cyberbullying. As a result, this paper shows the analysis of internet usage, cyberbullying and its relationship. Cyberbullying is the most important concept to be considered now a days for the researchers as it is directly linked to the lives of the youth of the world. Earlier research did not focus on the importance of social media, however, this paper discusses linkage of the overuse of social media with cyberbullying. © 2018 IEEE.

关键词： Surveys

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Handling smurfing through big data 50th

Handling smurfing through big data

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50th Annual Convention of computer Society of India : Big Data Analytics, CSI 2015

作者： Chadha, Akshay Kaur, Preeti Department of Computer Engineering Netaji Subhas Institute of Technology New Delhi India

ISBN: (纸本)9789811066191

Money laundering is a worrying term for every country’s economy these days. Leading economists of all major developed and developing economies are concerned to devise methods to prevent it. The economy of a country is weakened by the impact of money laundering. Networks created between various banks in different countries facilitate online money transfer, which is turning the process of money laundering into digital money laundering. This promotes money launderers to perform wired transactions from anywhere. People involved in the process of money laundering are efficiently using online banking as their weapon. Evading the anti-money laundering agencies is becoming easier for them because of having online bank accounts. Such people are misusing technology. Therefore, it is restricting one’s own country’s economic progress. But with the help of recently developed technologies, we are able to prevent such illegal activities. Scrutinizing all the transactions and investigating them manually at financial intelligence units are cumbersome tasks because petabytes of transactions are taking place each day. Advanced technologies like Big Data enable us to detect the suspicious customers possibly involved in money laundering. In this paper, we have proposed a methodology using big data to detect smurfing;based on which, suspicious people involved in money laundering may be identified and appropriate action can be taken against them. © 2018, Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Identification of Suspicious Patterns in Social Network using Zipf's Law

Identification of Suspicious Patterns in Social Network usin...

引用

2018 IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018

作者： Sarna, Geetika Bhatia, M.P.S. Computer Engineering Department Netaji Subhas Institute of Technology New Delhi India

ISBN: (纸本)9781538641194

Scale-Free social network is universally popular among the users of almost all the ages. This scale-free network follows the Power Law that expresses the distribution of data in the form of body and tail. Tail can be expressed in two types: heavy and long. Heavy tail also called Pareto distribution means rare or infrequent occurrences of events with high values whereas long tail which is used for Zipf's distributionshows many occurrences of events with small values. In this paper, we employed the concept of long tail to identify the skeptical pattern in the social network as security is the main concern for everyone and become an open research area. Earlier papers on Zipf's Law worked on the concept of constructing the dictionaries or thesaurus on the basis of the frequency of keywords. However, this paper deals with the suspicious pattern on the basis of the frequency of keywords. © 2018 IEEE.

关键词： Pareto principle

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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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Handwriting recognition using Deep Learning in Keras

Handwriting recognition using Deep Learning in Keras

引用

2018 IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018

作者： Arora, Shefali Bhatia, M.P.S. Division of Computer Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (纸本)9781538641194

Nowadays, deep learning is playing an important role in the domain of image classification. In this paper, a Python library known as Keras, is used for classification of MNIST dataset, a database with images of handwritten images. Two architectures - feed forward neural networks and convolutional neural networks are used for feature extraction and training of model, which is optimized using Stochastic Gradient Descent. This paper gives an overview of multi-class classification of these images using these models, and their performance evaluation in terms of various metrics. It is observed that convolutional neural networks achieve a greater accuracy as compared to feedforward neural networks for classification of handwritten digits. © 2018 IEEE.

关键词： Classification (of information)

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Self-supervision based task-specific image collection summarization

arXiv

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arXiv 2020年

作者： Singh, Anurag Sharma, Deepak Kumar Sharma, Sudhir Kumar Rodrigues, Joel J.P.C. Department of Computer Science Technical University of Munich Munich Germany Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science Institute of Information Technology and Management New Delhi India Teresina - PI Brazil Instituto de Telecomunicações Portugal

Successful applications of deep learning (DL) requires large amount of annotated data. This often restricts the benefits of employing DL to businesses and individuals with large budgets for data-collection and computation. Summarization offers a possible solution by creating much smaller representative datasets that can allow real-time deep learning and analysis of big data and thus democratize use of DL. In the proposed work, our aim is to explore a novel approach to task-specific image corpus summarization using semantic information and self-supervision. Our method uses a classification-based Wasserstein generative adversarial network (CLSWGAN) as a feature generating network. The model also leverages rotational invariance as self-supervision and classification on another task. All these objectives are added on a features from resnet34 to make it discriminative and robust. The model then generates a summary at inference time by using K-means clustering in the semantic embedding space. Thus, another main advantage of this model is that it does not need to be retrained each time to obtain summaries of different lengths which is an issue with current end-to- © 2020, CC BY.

关键词： Semantics

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