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Behavioral Biometrics Authentication Systems: Leveraging Machine Learning for Enhanced Cybersecurity

Behavioral Biometrics Authentication Systems: Leveraging Mac...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Madavarapu, Jhansi Bharathi Mittal, Mudit Salagrama, Shailaja Adnan, Myasar Mundher Rana, Aryan Yadav, Kanchan University Of Cumberlands Department Of Information Technology 6178 College Station Drive WilliamsburgKY40769 United States Department Of Computer Science & Engineering Graphic Era Deemed To Be University Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Amity School Of Engineering & Technology Amity University India Gla University Department Of Electrical Engineering Mathura India

ISBN: (纸本)9798350366846

This study focuses on behavioral biometrics and machine learning procedure that is employed vigorous in the verification frameworks to improve cybersecurity. Elaborating interesting designs in client behaviour, such as keystroke flow and mouse developments along with advanced machine learning algorithms we propose to generate verification components capable of accurately identifying a person's identity while adapting themselves all the fast live changes associated either. The experiments carried out on real-life datasets demonstrate LSTM models pervasive implementation in contrast to traditional machine learning algorithms (SVM, KNN and Random Forest). Thus, LSTM achieved an accuracy of 95.6%, outperforming SVM (88.7%), KNN (84.5%) and Random Forest (923). Additionally, LSTM proved great flexibility and competitive performance despite longer training times. The abovementioned results have demonstrated that LSTM is effective in learning the complicated real world conditions in sequential data and could be used for improvement of behavioral biometric recognition systems. © 2024 IEEE.

关键词： Long short-term memory

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FesNas: A Breakthrough Algorithm for Multi-Classification of Wheat Black Rust Intensity Levels 14

FesNas: A Breakthrough Algorithm for Multi-Classification of...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Amandeep Sehgal, Sweety Mahajan, Manish Kukreja, Vinay Sharma, Rishabh Vats, Satvik Gulzar Group of Institutions Punjab Ludhiana India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Amity University Amity School of Engineering and Technology Punjab Mohali India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335095

Wheat black rust poses a severe danger to wheat production all over the world. The neural architecture search feature selection (FesNas) classification model is used in this study to offer a multi-classification model for the wheat black rot. It was decided to gather and annotate a dataset consisting of 10,000 high-resolution photographs, with each image indicating a different severity degree of the condition. The model accomplished a remarkable level of overall accuracy by reaching 95.98%. The findings reveal that the FesNas algorithm is successful in properly diagnosing wheat black rust over a wide range of severity levels. This research makes an important contribution to precision agriculture and gives insightful information that can be used to develop effective methods for disease prevention and management. The methodology that was suggested has the potential to help farmers improve crop productivity while also reducing the negative effects that wheat black rot has on their crops. © 2023 IEEE.

关键词： Image classification

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Utilizing Sentiment Analysis for Assessing Suicidal Risk in Personal Journal Entries 3

Utilizing Sentiment Analysis for Assessing Suicidal Risk in ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Indoria, Devadutta Singh, Jagendra Rubi Singh, Yajush Pratap Kumar, Bura Vijay Singh, Prabhishek Vikram Dev University Department of Commerce Jeypore India School of Computer Science Engineering & Technology Bennett University Greater Noida India Swami Vivekanand Subharti University Department of Education Meerut India Amity School of Engineering and Technology Amity University Noida India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350303360

Mental health is a rising alarming issue and its condition among teenagers and adults is influenced by various factors including financial situation sociocultural context resource availability and personal experiences according to the national institute of mental health factual evidence suggests that mental health concerns are largely common among teenagers One out of five teenagers in the united states experiences mental health disorder which includes anxiety and depression ranking up being the most common conditions similarly various studies have found that many white-collar such as healthcare workers first responders and educators have a higher risk of having mental health problems because of their high-stress work environment and exposure to traumatic events. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Data storage security issues in cloud computing

Data storage security issues in cloud computing

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2021 International Conference on Innovative Practices in technology and Management, ICIPTM 2021

作者： Singh, Devang Pratap Kaushik, Prakarsh Jain, Manjari Tiwari, Vidushi Rajpoot, Subhadra Amity School of Engineering and Technology Student of Computer Science and Engineering Greater Noida India Amity School of Engineering and Technology Department of Applied Science Greater Noida India

ISBN: (纸本)9780738132891

Cloud computing gives on request administration on their information or data to its customers. Information storage(storing) is among one of the essential administration given by the cloud computing. Cloud specialist organization has the information of information proprietor on their server and client can get to their information from these servers. As information, proprietors and servers are various personalities, the worldview of information storage raises numerous security challenges. A free component is required to ensure that information is effectively facilitated to the cloud storage server. Right now, we will examine the various methods that are utilized for secure information storage on cloud. © 2021 IEEE.

关键词： Cloud storage

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Towards Smart Agriculture: Automated Detection and Diagnosis of Bean Leaf Diseases Using ResNext50 Model 14

Towards Smart Agriculture: Automated Detection and Diagnosis...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mahajan, Manish Sharma, Rishabh Singh, Amandeep Kukreja, Vinay Vats, Satvik Amity University Amity School of Engineering and Technology Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Gulzar Group of Institutions Punjab Ludhiana India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335095

Bean leaf halo blight is a widespread plant disease that causes large output losses by affecting the leaves of bean plants. This disease is known as bean leaf halo blight. To develop successful methods for illness management, it is essential to determine the seriousness levels of this disease in a precise and timely manner. The proposed research study present a multi-classification framework that makes use of the ResNeXt50 model to categorize bean leaf halo blight according to one of five distinct seriousness levels. The authors want to achieve the goal of developing a dependable and effective system by utilizing the capabilities of deep learning (DL) and transfer learning (TL). This will allow us to provide farmers with assistance in making well-informed decisions on disease management methods. To make the model more robust, we amped up the data by collecting and processing a collection of bean leaf pictures and by using data augmentation strategies. After being trained and tested, the ResNeXt50 model was found to have an accuracy level of 96.37% overall. Following the investigation of performance parameters for each layer, a confusion matrix was developed to evaluate the classification results produced by the model. In addition, the ResNeXt50 model was judged to be superior to other cutting-edge models under its ability to effectively categorize bean leaf halo blight. This was demonstrated by a comparison with other models. The framework that has been developed has the potential to greatly improve disease control practices, contribute to the enhancement of crop yields, and ensure that agriculture remains sustainable. Future research may concentrate on improving the model and evaluating its performance in real-world settings;this will contribute to the practical implementation of the suggested system. © 2023 IEEE.

关键词： Crops

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Hardware Acceleration Using SIMD Based Vector Processor Array to Enhance Performance of Deblurring Methods for CT and MRI Images Having Motion Blur Artifacts

Hardware Acceleration Using SIMD Based Vector Processor Arra...

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2023 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2023

作者： Sharma, Poonam Dubey, Ashwani Kumar Goyal, Ayush Amity School of Engineering and Technology Amity University Uttar Pradesh Dept. of Electronics and Communication Engineering U.P. Noida India Texas A&m University Dept. of Electrical Engineering and Computer Science Kingsville United States

ISBN: (纸本)9798350347296

A typical medical imaging that gives deep cross-sectional details of human body like MRI and CT, usually scans the subject area in form of many scans called slices. These slices are generally in the order of 100s or 1000s per unit of time. However, these slices have many artifacts including blurring of fine details due to motion between the scan device and the subject that may happen because of several reasons like nonstationary subject or jerk in the motion of capturing device or inappropriate aperture control. There is never-ending demand to always accelerate the medical image processing for faster and real time diagnosis especially for critical life saving situations. The core processor architecture to exploit such accelerations are the core to meet these requirements. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Software Reliability Evaluation using a Hybrid Soft Computing Approach for Battery Management System

Software Reliability Evaluation using a Hybrid Soft Computin...

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Sustainable Energy and Future Electric Transportation (SEFET), International Conference on

作者： Shrddha Sagar Avadhesh Kumar Garima Shukla Sofia Singh Dipak Raskar School of Computer Science and Engineering Galgotias University Uttar Pradesh India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai Maharashtra Department of Artificial Intelligence Amity School of Engineering and Technology Amity University Uttar Pradesh Noida

ISBN: (数字)9798350383997

ISBN: (纸本)9798350384000

Predicting faults in a software system can be performed using software reliability models. Reliability is a real- world aspect that is related to several real-life issues which depends on several factors. Different algorithms and methodologies are present to predict software reliability, still the exploration of hybrid soft computing approaches is required to predict software reliability inefficient way. The reason is soft computing algorithms are able to deal with uncertainties and real time issues. The objective of this study is to propose a reliability assessment model using hybrid approach of GeneticAlgorithm and Self-Organizing Map (SOM) for Component Based Software System (CBSS). The reason for selecting CBSS is the reusability aspects of components, which makes software development process cheaper and faster. This paper is an attempt to identify most reliable components based on three reliability factors (cohesion, coupling, and component dependency) from a software project that can be reused for developing other applications efficiently. Simulation of Battery Management System software package components is completed by the use of MATLAB which were having 37 components out of which 6 most reliable components are selected using proposed method.

关键词： Self-organizing feature maps Couplings Computational modeling Software algorithms Battery management systems Software systems Prediction algorithms Software reliability MATLAB Genetic algorithms

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Deceptive Plastic: Unveiling the World of Financial Fraud 6

Deceptive Plastic: Unveiling the World of Financial Fraud

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Singh, Shikha Hazela, Bramah Asthana, Pallavi Barnwal, Shagun Srivastava, Garima Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Electronic and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India

As human society is developing, so is electronic commerce and with that the use of credit card are increasing. And along with this growth, as people prefer to shop using credit cards in both mode of payments, the possibility of fraud and fraud of credit card is also on rise. The criminals may steal the card physically, but more frequently, they use digital ways to obtain the credit card details along with the personal information linked to it in order to engage in illicit actions. There is a lot of overlap between credit card fraud and identity theft. Indeed, credit card theft is one of the most common forms of identity theft. In these cases, the personal information of victims is stolen to somehow get access to cards by registering a new account without the owner's knowledge and certainly it is a form of cyberattack as well as data breach and is offensive crime. This behavior is both identity fraud and credit card fraud. In this study, we employ a CAT Boost, ADA Boost, LIGHT GRADIENT BOOSTING and CONVOLUTIONAL NEURAL NETWORK model to simulate the process of credit card transaction processing and display how it can be applied to the spot of such frauds. The dataset inspection method, factor determination, and techniques used for detection have a significant influence on the demonstration of fraud detection in credit card transactions. The accuracy for models can be determined by finding precision, recall, f1score, accuracy values but to compare the models, we will take ROC AUC Score into account. So, the ROC AUC Score of CAT BOOST, ADA BOOST, LIGHT GRADIENT BOOSTING and CONVOLUTIONAL NEURAL NETWORK model are 92.44, 79.74, 93.34, 95.25 respectively. And CONVOLUTIONAL NEURAL NETWORK is best among four with 95.25 ROC Score. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Convolutional neural networks

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Advanced Predictive Modelling for Bitcoin and Ethereum Price Dynamics

Advanced Predictive Modelling for Bitcoin and Ethereum Price...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mohd Waseem Shailendra Narayan Singh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Over the last decade, Bitcoin and Ethereum have become cryptocurrencies that have attracted the attention of the financial world with their potential for business transactions and the use of new blockchain technology. This study is dedicated to predicting Bitcoin and Ethereum price trends from 2014 to 2024, covering the main period of growth and change in the cryptocurrency market. This research aims to develop a good forecasting model through a comprehensive analysis of historical price data, market trends, economic progress and macroeconomy. It uses techniques such as machine learning, time series analysis, and sentiment analysis to try to predict future price movements of Bitcoin more accurately and Ethereum. By revealing the fundamental principles influencing cryptocurrency prices, this research leads to a deeper understanding of the dynamics driving digital assets and their impact on the financial sector.

关键词： Economics Sentiment analysis Time series analysis Bitcoin Machine learning Predictive models Market research Blockchains Informatics Forecasting

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A Smart Blockchain Model for Supply Chain Management System 4th

A Smart Blockchain Model for Supply Chain Management System

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago Iwoye Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819724505

Industry 4.0 is the creation of smart industrial processes. Therefore, supply chain management describes a chain of activities from the purchase of raw materials to the distribution of finished goods. The problems with the existing manual supply chain system include consistency, timeliness, traceability, security, and trust among all supply chain actors. This study presented the integration between a rule-based expert system and blockchain technology called SupplyOnBlock for supply chain management. The rule-based expert system was proposed to create smart industrial processes and blockchain technology to create fast, traceable, and secure supply chain processes. The rule-based expert system is to create a smart individual process in the supply chain while the blockchain is to coordinate the individual smart processes in a network of blockchains. The proposed SupplyOnBlock model for supply chain management is designed to use the public key cryptographic hash function for encryption of key processes to avoid non-repudiation at any point in the SupplyOnBlock. The information to be transferred on the block is first hashed into a value to compress its volume then the public key cryptography is used to encode and decode the hashed information. The proposed encryption scheme should be able to validate the legitimacy of the sender using the private key of the public key cryptography algorithm. The receiver should be able to validate the legitimacy of the sender using the public key of the public key cryptography algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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