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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Karadbhajne, Manaswi Gomkale, Mayuri Chavhan Nekita, A. Agrawal, Rahul Dhule, Chetan G H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350344387

This study uses a CNN model to explore deeper into the complex categorization of mask face emotion identification. There are number of application techniques which uses face emotion recognition [FER]. Ex.: - application like (Snapchats), Healthcare (identify genetic diseases), Market research, phone (face locks). The problem over this application is still under the accuracy point of research. Mask Facial Emotion based on image processing and vision of computer. In this project we are presenting the system which run on machine learning algorithm [CNN (Convolutional neural network)]. It is capable of recognition of facial emotion under mask with more accuracy. This system helps to differentiate between 5 universal emotions like Happy, Anger, neutral, Surprise and Sad. © 2023 IEEE.

关键词： CNN (Convolution neutral network) Facial emotion recognition (FER) Mask facial emotion recognition (MFER)

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Machine Learning Algorithm Comparison for Traffic Signal: A Design Approach 8

Machine Learning Algorithm Comparison for Traffic Signal: A ...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Deshmukh, Saloni Parwekar, Aishwarya Danej, Bhagyashri Chavhan, N.A. Agrawal, Rahul Dhule, Chetan G H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350396638

The number of vehicles is increasing significantly every day, especially in major cities. To control traffic flow on extensive road networks and facilitate crossing traffic flow on extensive road networks and facilitate crossing crossings, intersection n traffic signal coordination is required. It is therefore a new technology in the present world for intelligent traffic control and administration systems due to the number of vehicles with so many communication restrictions in arrays. The traffic signal strives to boost the effectiveness of transportation networks, increase safety, and reduce congestion. Technological advances enable better monitoring and management of traffic flow, and traffic signal design and operation are continually developing. The employment of some algorithms for traffic light control has improved the intelligence of traffic light controllers. This research study has developed data instruction strategies using data mining and image processing techniques. By comparing the analysis of several machine learning algorithms for predicting traffic flow and patterns, such as KNN, SVM, DNN, and Random Forest, the accuracy can be increased by lowering the overfitting. © 2023 IEEE.

关键词： Support vector machines

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Review of permission-based malware detection in Android

Review of permission-based malware detection in Android

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International Conference on Artificial Intelligence, Blockchain, Computing and security, ICABCS 2023

作者： Rawat, Nishant Amrita Singh, Avjeet Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

A market study showed that an average of 70% of smartphone users use an android-based smartphone. The Android operating system draws numerous malware threats as a result of its popularity. The statistic reveals that 97% of malware prey on android due to its lack of security. The malware enters the system and after installing the malware application they ask permission from the user to run. Permissions like Read_Phone, Access_Internet, Write_Log, etc give full control of the victim’s phone to the hacker. In recent years more efforts were made to analyze android permissions to detect malware. In this paper, we review some of the already published work in detail which is in the area of malware detection using static analysis. © 2024 The Author(s).

关键词： Machine learning

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Multiclass Brain Tumor Detection using Convolutional Neural Network and Conventional Machine Learning 2

Multiclass Brain Tumor Detection using Convolutional Neural ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vidhya, K. Vamsi Krishna, B. Haritha, M. Rajasekaran, Arun Sekar Roopa Chandrika, R. Velusamy, Jayaraj Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security School of Computer Science and Technology Coimbatore India QIS College of Engineering and Technology Department of AIMLDS Andhra Pradesh India QIS College of Engineering and Technology Department of Mechanical Engineering Andhra Pradesh India SR University Department of Electronics and Communication Engineering Telangana India Karpagam Academy of Higher Education Department of Cyber Security Coimbatore India Nehru Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331534950

Brain tumor (BT) affect the brain and reduce the lifespan of humans. Therefore, diagnosis and treatment of illnesses in its early stage is required. The objective of this paper is to evaluate the effectiveness of the Convolutional Neural Network (CNN) and Conventional Machine Learning (CML) strategies for identifying multiple classes of brain tumor. In the research, a comparison is made between these two systems for the picture classification of brain tumor. The first step is the collection and preprocessing of images of several classes of brain tumor. The CNN and CML models are trained using the preprocessed dataset. CML makes use of traditional machine learning (ML) algorithms for the purpose of feature extraction and classification, whereas CNN relies on convolutional layers to automatically extract significant features from the images it is fed as input. Precision, accuracy, F1 score, and recall are the criteria that are utilized to evaluate methods. In terms of accuracy and overall performance, CNN is superior to CML. CNN provides a more precise classification of a variety of tumor classifications. Based on these findings, convolutional neural networks (CNN) are likely superior than machine learning for the identification of multiclass brain tumor. CNNs are superior because of their ability to directly understand intricate patterns and characteristics from photographs. This work demonstrates the usefulness of CNNs for multiclass classification in brain tumor detection systems because of their high accuracy and efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting cross-site scripting attacks using machine learning: A systematic review

Detecting cross-site scripting attacks using machine learnin...

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International Conference on Artificial Intelligence, Blockchain, Computing and security, ICABCS 2023

作者： Baniya, D. Amrita Chaudhary, A. Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

In today s age, many daily tasks are performed through the Internet using various web applications. While using the web application, the information and data are stored in the database of the network which can easily be attacked by the attacker. The attacker used different types of attack patterns to steal the victim s information or any details like their bank information, and business details. Among the different types of attacks over the web application vulnerabilities, the most prominent category of attack is a cross-site scripting (XSS) attack. In the XSS attack, an attacker transmits the script to the victim s web Browser. As the victims are unaware that they were being attacked leading to stealing the valuable information like personal details, bank details, and cookies. Using the cookies of the victim, the attacker can easily access their account without using a username and password. Early, different types of approaches have been done to shoot out the vulnerabilities related to XSS. This paper purports to be a survey on XSS. The primary contribution of this study to the current state-of-the-art XSS is a systematic review of various types of research done to detect XSS vulnerabilities using Machine Learning. This paper also provides the background related to XSS, techniques, difficulties and future guidelines in the field of XSS to provide researchers with significant knowledge for further work. © 2024 The Author(s).

关键词： Machine learning

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Design of Smart Irrigation System Based on MLA

Design of Smart Irrigation System Based on MLA

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Bobade, Gunjan Dhule, Chetan Khadilkar, Ritika Agrawal, Rahul Morris, Nekita Chavhan G H Rasioni College of Engineering IOT Cyber Security Department of Data Science Nagpur India

ISBN: (纸本)9798350310061

Agriculture has always been a fundamental aspectof Indian culture, making it the country's most efficient andimportant occupation. Over the years, smart and advanced techniques have been developed to optimize farming practices, and one such development is the smart irrigation system. This system has become increasingly popular among farmers as it helps them reduce water usage while maximizing profits. This has not only helped farmers increase their profits but has also made farming a more sustainable and eco-friendly occupation. The proposed Model is a AI Based Smart irrigation System, which utilizes machine learning algorithms (ML) to predict soil moisture levels based on parameters such as temperature, humidity, and soil moisture. This system is developed through cloud computing, allowing for real-time data to be extracted from (IoT) sensors. The decision tree, a supervised learning algorithm, is applied to the data to classify or predict values. The result obtained from the decision tree helps predict the appropriate amount of water needed for the crops, gives the accuracy of 90% It allowing for efficient water usage and promoting yield growth. © 2023 IEEE.

关键词： Decision trees

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Credit Card Fraud Detection Using Machine Learning Techniques 1

Credit Card Fraud Detection Using Machine Learning Technique...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Murkute, Purvi Dhule, Chetan Lipte, Praneti Agrawal, Rahul Chavhan, Nekita G H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350342796

Due to the advancement of online transactions also widespread usage of credit cards for electronic payments, credit card fraud had major concerns in the financial sectors. Efficient and effective fraud detection techniques are urgently needed to protect financial institutions and consumers against evolving and sophisticated fraudulent activity. This study analyse the methods and developments used to find credit card frauds. With the objective to detect credit card fraud using an extremely unbalanced dataset, this research gives a thorough evaluation of 4 widely known machine learning algorithms: Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost. To assess how well the algorithms detect fraudulent transactions, the area under the precision-recall curve (AUPRC) and Area Under the Receiver Operating Characteristic (AUC-ROC) was estimated. © 2023 IEEE.

关键词： Area Under Precision Recall Curve (AUPRC) Area Under the Receiver Operating Characteristic (AUC-ROC) Credit Card Fraud Detection Gradient Boosting Machines Logistic Regression Machine Learning algorithms Random Forest XGBoost

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CNN-BO-LSTM: an ensemble framework for prognosis of liver cancer

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1103-1109页

作者： K N, Sunil Kumar Kashyap, Pavan P. Bhyratae, Darshan A. Bhyratae, Suhas A. Kalaivani, A. Department of ISE Sri Venkateshwara College of Engineering Bangalore India Department of Electronics & amp Communication Engineering Sri Venkateshwara College of Engineering Bangalore India Computer Science and Engineering Sahyadri College of Engineering and Management Mangaluru India Department of MCA New Horizon College of Engineering Marathahalli Bangalore India Department of CSE-Cyber Security Sri Venkateshwara College of Engineering Bangalore India

computer-assisted diagnosis (CAD) is preferred for cancer identification across the globe, which relies on computerized image processing. The creation of previous CAD instruments involved a semi-automated approach that employed traditional deep learning techniques. Such techniques are not well versed with CAD instruments in terms of accuracy. Therefore, the given manuscript presents a convolutional neural network normalized architecture embedded with Bayesian optimization and long short-term memory (CNN-BO-LSTM) for the identification of liver cancer. Early pre-processing is done on the input magnetic resonance imaging (MRI) scan images to improve clarity. Next, we used dynamic binary classification to apply the accurate and region of interest (ROI) extracting approach. It is followed by automatic retrieval of CNN-based appearances from the ROI approach. For classification purposes, LSTM is used, which categorizes the images as benign or malignant. The proposed design’s testing outcomes, which combine characteristics with CNN-based ROI extraction and LSTM classification, surpassed the current state-of-the-art techniques. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： computer aided diagnosis (CAD) Convolutional neural network (CNN) Features extraction and bayesian optimization Long short term memory (LSTM)

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Customer Segmentation Based on E-Commerce using K-Mean Clustering

Customer Segmentation Based on E-Commerce using K-Mean Clust...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Bhimarapu, Harshita Bhelkar, Sakshi Chavhan, Nekita Dhule, Chetan Agrawal, Rahul G H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350306927

Due to the increasing growth of customers in market, there is fierce competition in business. Relationships between businesses and customer will be strengthened by understanding customer needs. The importance of giving users a comprehensive experience increases as a result of the digital market's rapid growth. The dataset from an ecommerce website is used in this study to identify all the factors for analysis, including date, customer id, and product;with the help of k-Mean clustering and decision tree. This research work will determine the most effective technique for Customer Segmentation. This facilitates the identification and resolution of distinct requirements and preferences among diverse customer segments. Customer Segmentation is a multifaceted strategy that can positively impact various aspects businesses, from revenue and profitability to customer satisfaction and long-term growth. © 2023 IEEE.

关键词： K-means clustering

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Perception System of Autonomous Vehicles: Sophisticated Attacks, Defense Strategies, and Future Directions 15

Perception System of Autonomous Vehicles: Sophisticated Atta...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neethu, M.R. Remyakrishnan, P. Mukesh, Sa Barath Kumar, J. Royal, Ala Manas Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore641112 India Amrita Vishwa Vidyapeetham TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore641112 India

ISBN: (纸本)9798350370249

The advent of autonomous vehicles marks a significant milestone in the evolution of automotive technologies. Central to the operation of autonomous vehicles is the perception system that interprets the surroundings and makes decisions. The security of perception systems against malicious attacks is a critical concern. This work thoroughly analyzes the vulnerabilities inherent in the perception systems of autonomous vehicles, examining the multifaceted threats posed by adversaries. The crucial need for ongoing research in AV security is underscored and a framework for future investigations to develop more secure and trustworthy autonomous vehicles is provided. The analysis emphasizes the importance of a proactive approach to security, advocating for integrating advanced defense mechanisms as a fundamental component of autonomous vehicle design and development and future research challenges. © 2024 IEEE.

关键词： Autonomous Vehicles Camera Lidar Perception Systems security attacks

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