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1st International Conference on Smart Computing and Informatics, SCI 2016

作者： Mittal, Vikas Gupta, Sunil Choudhury, Tanupriya Department of Computer Science Maharaja Agrasen College University of Delhi Delhi India Department of Computer Science & Engineering BML Munjal University Gurgaon India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh NoidaUttar Pradesh India

ISBN: (纸本)9789811055461

The major goal of this analysis is to compare and find out the appropriate protocol for research. According to our analysis, the access control method and authentication used in the wireless sensor networks in some protocols is costlier in the security and message exchange. In analysis, we find out that various security solutions are based on public key cryptography which is highly expensive, susceptible to replay and denial of service attacks. Most of access control protocols depend on the key pre-distribution mechanisms which are combined together with either a one-way key chain or a pseudorandom function. The solution for the access control inculcates various vulnerabilities of the mechanism on which it is developed and its security features also rely on the same. This paper provides detailed analysis over improvements for all the recent protocols that cover the shortcomings of each other. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Authentication

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Analysis of software repositories using process mining 1st

Analysis of software repositories using process mining

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1st International Conference on Smart Computing and Informatics, SCI 2016

作者： Arora, Roohi Garg, Anchal Accenture Solutions Private Limited PuneMaharashtra India Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Uttar Pradesh NoidaUttar Pradesh India

ISBN: (纸本)9789811055461

Software repositories such as Issue Tracking Systems (ITS) log bug history that contains important information about the activities followed during bug resolution. Mining of ITS can provide useful insights of such processes. This study uses process mining to extract knowledge from the event logs recorded in bug history report of ITS Monorail to determine the as-is process model. This study will help in identifying the kind and source of inefficiencies and inconsistencies and eventually help improving the software bug resolution process. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Monorails

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Enhancing Decision Tree Classification Accuracy through Genetically Programmed Attributes for Wart Treatment Method Identification

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Procedia computer science 2018年 132卷 1685-1694页

作者： Sabita Khatri Deepak Arora Anil Kumar Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Lucknow India

Origin: Warts are produced and developed on the human body due to infection induced by Human Papillomavirus. The most influenced zone of warts are hands and feet particularly, which is bit irritating and difficult to recoup in later stages. The major challenge in treating warts is the diversity of treatment method applicable on different patients, so it becomes difficult to recognize specific treatment method to be adopted in order to treat this infection. Ramifications of machine learning techniques in the medical domain have become crucial nowadays for early disease detection and developing expert systems. Objective: This research work focuses on enhancing predictive accuracy of J48, which is a binary decision tree based classifier by adding attributes based on genetic programming. These genetically tuned attribute construction not only just upgrades the classification capabilities of J48 classifier but also additionally expand the information space, intending J48 for giving more exact predictions for wart treatment method identification. Method: For their experimental setup, authors have chosen immunotherapy and cryotherapy datasets from UCI machine learning repositories, which includes instances of patients responses against treated with immunotherapy and cryotherapy methods for both plantar and common warts. The investigation has been led with the help of WEKA tool, which is an open source for performing data mining operations. Finding: After experimentation, it is found after inclusion of attributes generated through genetic programming, the classification accuracy of J48 can be increased by a substantial amount with less error rate. The result shows significant performance improvements in classification accuracy of J48 by 82.22% to 96.66% and 93.33% to 98.88% for immunotherapy and cryotherapy datasets, implemented with J48 and J48+GA respectively.

关键词： Warts Immunotherapy Genetic Programming Machine Learning Decision Tree

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Analysing the genetic diversity of commonly occurring diseases 50th

Analysing the genetic diversity of commonly occurring diseas...

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50th Annual Convention of computer Society of India : Nature Inspired Computing, CSI 2015

作者： Malik, Shamita Khatri, Sunil Kumar Sharma, Dolly Amity School of Engineering and Technology NoidaUttar Pradesh India Amity Institute of Information Technology Noida NoidaUttar Pradesh India Computer Science and Engineering Shiv Nadar University Greater Noida India

ISBN: (纸本)9789811067464

It is generally believed that the existence of all organisms present on this earth has their point of convergence in a common gene pool. The current species passed through an evolutionary process which is still underway. The theoretical assumptions relating to the common descent of all organisms are based on four simple facts: first, they had wide geographical dispersal;second, the different life forms were not remarkably unique and did not possess mutually exclusive characteristics;third, some of their attributes which apparently served no purpose had an uncanny similarity with some of their lost functional traits;and last, based on their common attributes these organisms can be put together into a well-defined, hierarchical and coherent group, like a family tree. Phylogenetic networks are the main tools that can be used to represent biological relationship between different species. Biologists, mathematicians, statisticians, computer scientists and others have designed various models for the reconstruction of evolutionary networks and developed numerous algorithms for efficient predictions and analysis. Even though these problems have been studied for a very long time, but the computational model built to solve the biological problems fail to give accurate results while working on real biological data, which could be due to the premises on which the model is based. The objective of this paper is to test and analyse the transmission of commonly occurring diseases to fit into more realistic models. The problems are not only important because we need to know how they came into existence and how they migrated, but also helpful for the treatment of such diseases and drug discovery. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Biodiversity

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Analysis of external content plagiarism using character swarm optimization 1st

Analysis of external content plagiarism using character swar...

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1st International Conference on Smart Computing and Informatics, SCI 2016

作者： Chakraborty, Sudeshna Kumar, Vivek Choudhury, Tanupriya Jagannath University Jaipur India DCTM-Palwal Palwal India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh NoidaUttar Pradesh India

ISBN: (纸本)9789811055461

A maneuver on plagiarism is the theft on intellectual property. A considerable apprehension on the problem of plagiarism in course assignments by students has caused academicians and students to advocate a number of devices which instructors use in an effort to control the plagiarism problem. In an effort, this paper shows the classification supports using character swarm optimization, which results in very deep understanding of various linguistic patterns, for example changing the texts into semantically equivalent but with dissimilar words and organization, shortening texts with concept simplification and specification, and gaining ideas and vital contributions of others. Some literature overview on prevalent system is also reviewed like Turnitin Detection Software, Plagchecker, etc. The paper encompasses the nomenclature which supports a deep understanding of various dialectal, textual patterns in committing the plagiarism. A plagiarism revealing system with the help of an algorithm is put forward which was recently implemented as conceptualized software in an institution. The primary aim of the conceptualized system is to spot similarities in academic processes for student assignments and to assist in detecting possible instances of plagiarism. In this paper, the concerns with evaluation of tools were discussed that motivated this work to generate a character swarm optimization (CSO). © Springer Nature Singapore Pte Ltd. 2018.

关键词： Intellectual property

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Correction to: A smart intuitionistic fuzzy‑based framework for round‑robin short‑term scheduler

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The Journal of Supercomputing 2021年第4期78卷 4680-4680页

作者： Raheja, Supriya Alshehri, Mohammed Mohamed, Ahmed A. Khaitan, Supriya Kumar, Manoj Stephan, Thompson Department of Computer Science and Engineering Amity University Noida India Department of Information Technology College of Computer and Information Sciences Majmaah University Majmaah Majmaah Saudi Arabia Department of Computer Science College of Computer and Information Sciences Majmaah University Majmaah Saudi Arabia Department of Information Technology Faculty of Computer and Information Assiut University Assiut Egypt Department of Computer Science Galgotia University Greater Noida India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Faculty of Engineering and Technology M. S. Ramaiah University of Applied Sciences Bangalore India

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Application of Machine Learning for Cardiovascular Disease Risk Prediction

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Surjeet Dalal Pallavi Goel Edeh Michael Onyema Adnan Alharbi Amena Mahmoud Majed A. Algarni Halifa Awal Amity University Haryana Gurugram Haryana Indiaamity.edu Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Department of Vocational and Technical Education Faculty of Education Alex Ekwueme Federal University Ndufu-Alike Abakaliki Nigeria Adjunct Faculty Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai *** Clinical Pharmacy Department College of Pharmacy Umm Al-Qura University Mecca Saudi Arabiauqu.edu.sa Department of Computer Science Kafrelsheikh University Kafr El-Sheikh Egyptkfs.edu.eg Department of Clinical Pharmacy College of Pharmacy Taif University P.O. Box 11099 Taif 21944 Saudi Arabiatu.edu.sa Kwame Nkrumah University of Science and Technology Kumasi Ghanaknust.edu.gh Department of Electrical and Electronics Engineering Tamale Technical University Tamale Ghanatatu.edu.gh

Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.

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Deep Brain Neural Network Model Application Using Rabi Antenna Probes as Quantum Consciousness Sensors

SSRN

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SSRN 2022年

作者： Bunruangses, Montree Youplao, Phichai Garhwal, Anita Ray, Kanad Roy, Arup Pham, Nhat Truong Prabpal, Prakasit Yupapin, Preecha Jalil, Muhammad Arif Ali, Jalil Kaiser, Shamim Mahmud, Mufti Mallik, Saurav Zhao, Zhongming Department of Computer Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon Bangkok10300 Thailand Faculty of Industrial Technology Rajamangala University of Technology Isan Sakon Nakhorn Campus Sakon Nakhon547000 Thailand Asia Metropolitan University 6 Jalan Lembah Bandar Baru Seri Alam 81750 Johor Masai Malaysia Amity University Jaipur Rajasthan India Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla San Manuel Ciudad Universitaria Av. San Claudio y Av. 18 sur Col. Puebla Pue 72570 Mexico Faubert Lab Université de Montréal MontréalH3T 1P1 Canada Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Department of Electrical Technology School of Industrial Technology Institute of Vocational Education Northeastern 2 Sakhon Nakhon47000 Thailand Department of Physics Faculty of Science Unversiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Human Genetics Center School of Public Health The University of Texas Health Science Center at Houston HoustonTX77030 United States

The plasmonic antenna probe is designed using a silver bar embedded in a modified add-drop filter within a Mach Zehnder interferometer (MZI). The Rabi antenna is formed when the space-time control has reached the two-level system oscillation, which can be applied as a quantum brain-sensingprobe. A photonic neural network is designed using brain-Rabi antenna communication, wherein the transmission is connected by the neurons. The communication signals are transferred by the electron spins (up-down) and tunable Rabi frequency. The hidden variables and deep brain signals can be obtained by the external detection. We have developed a Rabi antenna via simulation using computer Simulation technology (CST) software. We have developed a communication device using the Optiwave program (OptiFDTD). The output signal is plotted by the Matlab program using parameters from the results of the OptiFDTD simulation. The proposed antenna resonates in the frequency range of 192THz-202THz with a maximum gain of 22.4dBi. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Moreover, an intelligent machine learning algorithm is proposed for the identification of high-quality transmission and predicting the behavior of the transmission in near future. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Finally, human quantum consciousness, dreams and human thoughts can be retrieved and discovered. © 2022, The Authors. All rights reserved.

关键词： Antennas

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A comprehensive comparison of ant colony and hybrid particle swarm optimization algorithms through test case selection 3rd

A comprehensive comparison of ant colony and hybrid particle...

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3rd International Conference on computer and Communication Technologies, IC3T 2016

作者： Agrawal, Arun Prakash Kaur, Arvinder Department of Computer Science and Engineering Amity University NoidaUttar Pradesh India University School of Information and Communication Technology GGSIP University New Delhi India

ISBN: (纸本)9789811032226

The focus of this paper is towards comparing the performance of two metaheuristic algorithms, namely Ant Colony and Hybrid Particle Swarm Optimization. The domain of enquiry in this paper is Test Case Selection, which has a great relevance in software engineering and requires a good treatment for the effective utilization of the software. Extensive experiments are performed using the standard flex object from SIR repository. Experiments are conducted using Matlab, where Execution time and Fault Coverage are considered as quality measure, is reported in this paper which is utilized for the analysis. The underlying motivation of this paper is to create awareness in two aspects: Comparing the performance of metaheuristic algorithms and demonstrating the significance of test case selection in software engineering. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Ant colony optimization

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Forecasting Stock Price Using Conditional Inference Tree

Forecasting Stock Price Using Conditional Inference Tree

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International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

作者： Shivam Thakur Shekar singh Seema Sharma Computer Science & Engineering Amity School of Engineering & Technology Noida India

ISBN: (纸本)9781538641200

Now a days trading in stock market for the purpose of gaining huge profits is becoming more lucrative for people across the globe. Any attempts that are able to predict the financial markets productively and with precision have always enthralled the curiosity and attention of economist, bankers and scientist. Stock market prediction can be defined as the process of determination of future value of the stocks of particular company or any other financial exchange. Precise predictions in this field are of great importance for a variety of reasons. Among all the reasons the one that stand out the most is the need for and investor to protect against all the potential risks and the opportunities for mediators and speculators to earn money and huge profits by trading indexes. Stock market can be defined as a place where the trading and issuing of shares occurs. The trade of the shares occurs mainly through stock exchanges in digital or in physical form. Data mining has its own significance in financial modeling because of its ability to reveal useful patterns and correlations. Data mining, can be defined as process that deals with the discovery of knowledge, data analysis along with full and semi-autonomous decision making in the field of computational intelligence. This very phenomenon of stock market prediction is gaining a lot of attention in recent times. This paper presents a system based on decision tree which is classified as c tree. This paper will be using the same model to predict the future trends of certain stocks based on the data from the past and their respective behavior.

关键词： Stock markets Hidden Markov models Predictive models Input variables Computational modeling Forecasting Neural networks

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