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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： V. Preetha P. Sathishkumar m. Janani B. manohari S. Jhansi Ida S. Suganyadevi Department of CSE Sri Ramakrishna Institute of Technology Coimbatore India Department of CSE Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Department of CSE KPR Institute of Engineering and Technology Coimbatore India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems R.M.K Engineering Colleg Kavaraipettai India Department of ECE KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9781665453424

ISBN: (纸本)9781665453431

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses c

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A machine Learning method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

关键词： Support vector machines Deep learning Sentiment analysis Analytical models machine learning algorithms mood Oral communication

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Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning

Intelligent Deep Learning Framework for Breast Cancer Predic...

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Global Conference for Advancement in technology (GCAT)

作者： Chintala Nageswara Rao K Chatrapathy A Jainul Fathima Goski Sathish Saptarshi mukherjee Pundru Chandra Shaker Reddy CSE-AI & ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing & Information Technology REVA University Bengaluru Karnataka India Information Technology Francis Xavier Engineering College Tirunelveli TamilNadu India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

The mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. The diagnostic process can benefit from the application of machine-learning (mL) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. The input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer’s neurologist count and firing rate for each successive hidden layer. The most crucial data points have been elected utilizing three different feature-selection methods. The optimized DRNN was contrasted to five standard mL strategies: decision-tree (DT), support-vector-machine (SVm), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). The elected features have been applied to both standard mL models and optimized DRNN. The outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate.

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A Survey on Video-based Evidence Analysis and Digital Forensic

A Survey on Video-based Evidence Analysis and Digital Forens...

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International Conference on Computing methodologies and Communication (ICCmC)

作者： R. V. mante Reshma Khan Assistant Professor Department of Computer Science & Engineering Government College of Engineering Amravati Maharashtra India M.Tech Scholar Department of Computer Science & Engineering Government College of Engineering Amravati Maharashtra India

ISBN: (数字)9781728148892

ISBN: (纸本)9781728148908

In the past couple of decades, the most emerging domain across the globe is Information technology [IT]. It has made a significant contribution in cracking the criminal case and preparing the strong cases in the court of justice. IT technologies have become a vital tool for examination and gathering of the digital evidences. As crime rate is increasing day-by-day, in order to reduce the occurrence of crime and bring the culprit of the crime to justice, the computer forensics plays an indispensable role. With the recent technological developments, it has become more powerful and portable. Large amount of data has created stored and accessed everyday. many technologically advanced devices are acting as repositories for the collected personal information. Such devices remain portable and accessible with a single click or even to the voice command. To have such large-scale information is important to get the conviction for the culprits but at the same time, investigating agencies has to maintain the privacy of the recovered data to make it admissible in the court. This gives rise to the security mechanism for such data. Collection, analysis, investigation and reporting of the digital data come under forensic science, which is a part of digital forensics domain. In this paper, we have presented a review on the existing researches available in the field of video forensic analysis.

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Experimental Evaluation of Internet of Things Assisted Industrial Automation System with Smart Emergency Alert mechanism

Experimental Evaluation of Internet of Things Assisted Indus...

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Information technologies and Communications (GCITC), Global Conference on

作者： B Yamini S Sreeraj K. Supriya T Vinithra Banu P Y Ragini Phaneendra Kurada Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of M. Tech CSE Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Business Systems Panimalar Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad Telangana India

Environmental sustainability, energy and raw material availability, and globalization are just a few of the major societal concerns that are generating new demands on a wide range of societal players. Efficiency, adaptability, sustainability, and competitiveness are some of the new criteria. These have been around for a while, and a variety of systems have been tuned locally in terms of a few of these requirements, but recent developments with regard to communication and computational characteristics have made them much more useful to guarantee new possibilities for combining various parts of society, allowing for optimum performance on a scale that is far greater. The impact of IoT on several sectors of the economy has been substantial. The Internet of Things (IoT) is a technology that allows us to manage physical things over the internet in order to save time and energy. There are several factories in India because of the country’s rapid economic growth. However, industrial accidents were a severe problem in many sectors. However, industrial accidents were a big problem because they resulted in casualties and financial losses for businesses. This initiative aims to reduce the devastating effects of fires in high-risk industries including the oil, chemical and petrochemical sectors, where many lives and fortunes have been lost. It is crucial to have a system in place that can ensure the safety of the factory grounds and promptly alert the proper authorities should an emergency arise. In the past, a single bottle at a time would be loaded onto a conveyor and filled. This strategy is time-consuming and costly. Internet of Things (IoT) in Industries has been implemented to monitor and notify the individual in charge to take suitable measures in order to reduce manual overhead and therefore partially meet our needs. The manufacturing sector might benefit from AI-based management and oversight. “Ecosystem having manufacturing Relevant Implication” better describes the I

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Security Provision in VANETs Using Group Based Key Share Algorithm

Security Provision in VANETs Using Group Based Key Share Alg...

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

作者： Rai, Km manisha Yadav, Anuradha Sagar, Anil Kumar Sagar, Shrddha School of Computer Science and Engineering M.tech Computer Science and Engineering Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781538641194

In today's world VANET get a lot of attention because of its easy and comfortable less infra-structure and life-saving factor. But whatever the facilities is available now it's not properly able to provide a strong security to the vehicle to vehicle (V2V) or vehicle to infrastructure communication (V2I) so attacks obey the authentication, confidentiality, and security which can influence the privacy of the user. The mechanism of VANET is unloosed with a lot of security risk nowadays. This paper gives an idea of VANETS architecture, existing work in VANET with security issues and challenges. Further, provide the solution to that issue with comparative analysis and use group-based key share algorithm which is enhanced the privacy of users and provides some research guidance for upcoming work. © 2018 IEEE.

关键词： Authentication

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Detecting Sybil Attack In Wireless Sensor Networks Using machine Learning Algorithms

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IOP Conference Series: materials science and Engineering 2021年第1期1042卷

作者： mandala mounica R. Vijayasaraswathi R. Vasavi M.Tech Student of Software Engineering Dept. of Computer Science & Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Assistant Professor Dept. of Computer Science & Engineering VNR Vignana Jyothi Institute Of Engineering and Technology Hyderabad Telangana India

In sensitive areas such as battlefields, a Wireless Sensor Network (WSN) is especially in military and civilian applications and it is of utmost importance to develop security in these networks. In various respects, this can improve the quality of life. But to be used for protection reasons in multiple situations such as implementation. There is a high risk of being exposed to multiple viruses and hacking attacks. Unauthorized APs for information protection needs to be detected. Any malicious attacks against these networks, Like Sybil Attack leads to breach of security by enacting as a node that illegitimately declares several false identities at the same time. This misleads valid nodes, and they presume each of those identifiers as actual independent nodes by accident. Thus we proposed a machine learning model to detect Sybil attack in network where rawtraffic data has been collected and is used identify authorized and unauthorized APs in an integrated wired/wireless environment.

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Security Provision In VANETs Using Group Based Key Share Algorithm

Security Provision In VANETs Using Group Based Key Share Alg...

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

作者： Km manisha Rai Anuradha Yadav Anil Kumar Sagar Shrddha Sagar School of Computer Science and Engineering M.tech computer Science and Engineering Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781538641200

关键词： Security Vehicular ad hoc networks computer architecture Routing protocols Floods Privacy

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Biometric Security and Performance metrics: FAR, FER, CER, FRR

Biometric Security and Performance Metrics: FAR, FER, CER, F...

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International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

作者： m. Sivaram mohamed Uvaze Ahamed A D. Yuvaraj G. megala V. Porkodi manivel Kandasamy Department of Computer Networking Lebanese French University – Erbil Kurdistan Region Iraq Department of Computer Science Cihan University Erbil Iraq Department of computer science Cihan University - Duhok Kurdistan Region Iraq Department of Computer Science Engineering M.I.E.T Engineering College Trichy India Department of Information Technology Lebanese French University Erbil Kurdistan Region Iraq Optum Tech|United Health Group USA

ISBN: (数字)9781728137780

ISBN: (纸本)9781728137797

Biometrics manages the computerized acknowledgment of people dependent on natural and social attributes. The example acknowledgment framework perceives an individual by deciding the credibility of a particular conduct normal for person. The primary rule of biometric framework is recognizable proof and check. A biometric confirmation framework use fingerprints, face, hand geometry, iris, and voice, mark, and keystroke elements of a person to recognize an individual or to check a guaranteed character. Biometrics authentication is a form of identification and access control process which identify individuals in packs that are under reconnaissance. Biometric security system increase in the overall security and individuals no longer have to deal with lost ID Cards or forgotten passwords. It helps much organization to see everyone is at a certain time when something might have happened that needs reviewed. The current issues in biometric system with individuals and many organization facing are personal privacy, expensive, data's may be stolen.

关键词： Security Fingerprint recognition Iris recognition Face Fingers Computational intelligence computer science

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Crowdsourcing – A Step Towards Advanced machine Learning

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Procedia computer science 2018年 132卷 632-642页

作者： Abhigna B.S. Nitasha Soni Shilpa Dixit M.Tech Scholar School of computer Science and Engineering Lingaya’s University Haryana India FET Manav Rachna International Institute of Research and Study Haryana India Assistant Professor School of computer Science and Engineering Lingaya’s University Haryana India

This paper avails an in-depth analysis of the field of crowdsourcing and its impacts when used in the world of machine learning. It comprises of various contributions that crowdsourcing can make to improvise the techniques that employ machine learning like – producing data, debugging and checking of models, hybrid smart machines to reduce the human intervention required to facilitate high quality performance by artificial intelligence and developmental experimentation to improve human-computer interaction. A discussion regarding the nature of crowd-workers follows next which focuses on various factors like their reaction to different forms of motivation, their behaviour towards each other and deceit among them. The takeaways of this paper include a few tips and routines to be followed to achieve success through crowdsourcing.

关键词： Hybrid smart machines developmental experimentation debugging model-checking crowdsourcing

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