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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kaur, Mananjeet Kaistha, Shreya Aggarwal, Shubhani Budhiraja, Ishan Chaudhary, Rajat Bindle, Abhay Amity University Department of Computer Science Engineering Punjab India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida India Maharishi Markandeshwar University Department of Electronics and Communication Engineering Haryana Mullana Ambala India

ISBN: (纸本)9798350391725

Robberies of ATMs have been on the rise in the last few years and are becoming a major concern for Banks and their customers. Currently, we operate ATMs using a PIN that can become a victim of theft, guessed, and can be hacked while the transaction is going on. This raises a question on the security of ATM transactions and requires new techniques and schemes that can offer a second level of protection. Considering these security concerns, we proposed a security check on the existing ATM systems. We authors have created two applications that present the new technique. Our banking application, called Fingerpay, works like traditional banking applications with an added feature where the user can enable the MFA (Multi-factor Authentication) through MPIN verification. In this case, a request for fingerprint authentication is prompted as a notification on the user's registered mobile number. Upon successful authentication, the transaction is authorized and completed securely. The key contribution of this research lies in the formation of a secure and user-friendly MFA system for ATM transactions. By integrating fingerprint authentication with existing banking applications, users are prompted to verify their identity through a registered fingerprint scan during ATM transactions. This additional layer of authentication increases security by significantly reducing the risk of unauthorized access and fraudulent transactions. In addition, biometric authentication outperforms traditional methods, including increased accuracy, reliability, and resistance to identity theft or impersonation. In the end, this proposal will go a long way in resolving the problem of ATM transactions. Further, this report contains the introduction, design and implementation part of the proposed model. © 2024 IEEE.

关键词： Crime

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A Concise Review of MRI Feature Extraction and Classification with Kernel Functions 3

A Concise Review of MRI Feature Extraction and Classificatio...

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

作者： Singh, Jagendra Arya, Chandrakala Yamsani, Nagendar Kumar, Mohit Singh, Prabhishek Bhagat, Vivek Kumar School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Computing Graphic Era Hill University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9798350303360

Magnetic resonance imaging is a common medical imaging tool for the identification and monitoring of brain diseases. Accurate classification of MRI brain pictures is extremely beneficial for early diagnosis and therapy planning. In this study, a method for MRI classification and feature extraction using a Support Vector Machine (SVM) and hybrid RBF which is radial Basis function kernel is proposed. Some of the interconnected steps in the suggested methodology include pre-processing, hybrid RBF kernel creation, feature extraction, SVM classification, feature selection, model evaluation, and model optimization. The GLCM technique is utilized to derive texture characteristics from MRI images. A blend of multiple RBF kernels with different parameter settings yields a hybrid RBF kernel. Performance indicators are used to assess the SVM classifier after it has been trained on a training dataset. The hybrid RBF kernel and the SVM's parameters are altered using model optimization. The suggested methodology intends to classify MRI brain pictures appropriately for enhanced treatment planning and disease diagnosis. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Autonomous Vehicle Forensics: Investigating Data Streams for Traffic Prediction and Incident Mitigation

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1211-1218页

作者： Srivastava, Vivek Mishra, Sumita Gupta, Nishu Albalawi, Eid Basheer, Shakila Amity University Uttar Pradesh Lucknow Campus Amity Institute of Information Technology India Amity University Uttar Pradesh Lucknow Campus Amity School of Engineering and Technology Department of Electronics and Communication Engineering India VTT Technical Research Centre of Finland Ltd Oulu90570 Finland King Faisal University College of Computer Science and Information Technology Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Science Department of Information Systems Riyadh11671 Saudi Arabia

The growing implementation of autonomous cars in intelligent transportation systems requires solid traffic forecasting and incident prevention mechanisms. Yet, there are difficulties in attaining system interoperability and user acceptability. In this research, a deep learning-based framework is suggested for traffic forecasting and prevention based on the use of a forensic method on autonomous car data. A restricted boltzmann machine derives deep, weighted features which are subsequently handled by an adaptive dilated long short-term memory model optimized by using the position updated osprey optimization algorithm. Forecasted traffic data are analyzed further to formulate mitigation strategies such as optimized path planning. Experimental results demonstrate better performance compared to the baseline methods based on various metrics, highlighting the effectiveness of the framework in improving future transportation systems and autonomous vehicle forensics. © 1975-2011 IEEE.

关键词： Autonomous vehicles

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Assessment and Summary of Inverter and Induction Motor Efficiency in Dual Electric Vehicle Drive Systems Across Urban and Highway Driving Conditions 7

Assessment and Summary of Inverter and Induction Motor Effic...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Manjunatha Naresh, B. Dutt, Amit Chaturvedi, Prateek Balaram, A. Bargavi, Y. Krishna New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Institute of Aeronautical Engineering Telangana Hyderabad India Lovely Professional University Punjab Phagwara India Amity University Amity School of Engineering & Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India Griet Department of Computer Science & Engineering Bachupally Hyderabad India

ISBN: (纸本)9798350350067

Recent studies have shown that battery-powered motors and power electronic inverters are crucial parts of contemporary dual electric vehicle driving systems. Consequently, the traction engine and its accompanying drive must run at peak efficiency all the way through the driving schedule. The traction motor and drive system in a hybrid electric vehicle (HEV) typically utilize the full range of torque and speed operating conditions. With this in mind, the objective of this work is to estimate the efficiencies of the inverter and motor over common urban and highway driving patterns. The significant losses within a standard three-phase DC/AC traction inverter, including switching and conduction losses in the insulated-gate bipolar transistors and antiparallel diodes, are modelled and approximated for both urban and highway driving. Using an Automobile Simulator program, a medium-sized SUV equipped with an induction motor was simulated, accounting for copper losses in the stator, rotor, and core. The average efficiencies of the inverter drive and induction traction motor are then evaluated and summarized for both urban and highway driving conditions. Finally, the overall efficiency of the traction motor drive system is assessed based on the individual component performance analyses. © 2024 IEEE.

关键词： Traction motors

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Cervical Cancer Classification using Multi-Scale, Multiple-Attention-based Deep Learning Approach

Cervical Cancer Classification using Multi-Scale, Multiple-A...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Maurya, Ritesh Deshwal, Suvidha Rajput, Lucky Gopalakrishnan, T. Krishna, Raguru Jaya Amity Centre for Artificial Intelligence Amity University Noida India Amity School of Engineering & Technology Amity University Noida India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350374933

Cervical cancer is a widely acknowledged health concern, especially in the areas lacking appropriate medical treatment. The early diagnosis and the explicit categorization of cervical lesions may bring about effective therapy and restrict the expansion of the disease. The novel approach proposed in this work employs a multi-scale, multi-attention based MobileNetV2 network for the categorisation of cervical cancer. The proposed model improves the efficiency of MobileNetV2, a lightweight convolutional neural network (CNN), by incorporating multi-scale and multi-attention techniques to improve feature extraction capabilities and categorisation accuracy. By incorporating multi-scale component, the suggested model can effectively detect features at different resolutions. Moreover, the integration of multi-attention mechanism allows the model to dynamically highlight salient features over multiple scales. The proposed method has been evaluated using single cell conventional Pap Smear images, exhibiting improved performance compared to existing approaches in terms of accuracy. The results suggest that the Multi-Scale, Multiple-Attention MobileNetV2-based network proves to be a useful tool for cervical cancer categorisation, offering potential interests for clinical diagnosis with an accuracy of 92.4. © 2024 IEEE.

关键词： cervical cancer categorisation deep learning MobileNetV2 multi-attention multi-scale transfer learning

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A Novel Cryptographic Encryption Technique for the Security Enhancement of Electronic Transactions 14

A Novel Cryptographic Encryption Technique for the Security ...

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

作者： Boussi, Grace Odette Gupta, Himanshu Hossain, Syed Akhter Fila Rudy, J.J. Amity University Amity Institute of Information Technology Noida India University of Liberal Arts Computer Science and Engineering Department Bangladesh Sharda University School of Engineering and Technology Greater Noida India

ISBN: (纸本)9798350335095

Cybersecurity has been a trending topic for years now, different propositions, studies, and work have been suggested in various forms by other authors all over the world, but the impact of crime is still around us. In the present area, we cannot say that data is safe when it is hidden from an unauthorized person, but it is safe when the intruder cannot understand it, hence encryption is the suitable solution at the moment. Encryption is one of the techniques that we use to protect our data from unauthorized persons. In this technique, we do not stop the unauthorized person from accessing our information, but we made our data appear in an incomprehensive form to the intruder, only the person with the correct key can decrypt the encrypted message. Many encryption techniques are available, and the commonly used and modern ones are RSA, AES, and HAS, each of them is suitable for different scenarios and are all helpful for cyber security. In this paper, we will propose a framework that provides an additional layer of security for any sensitive information in general and those in the banking sector. © 2023 IEEE.

关键词： Cryptography

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Federated Learning for Improved Air Pollution Prediction: A Combined LSTM-SVR Approach 4

Federated Learning for Improved Air Pollution Prediction: A ...

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4th IEEE Annual Flagship India Council International Subsections Conference, INDISCON 2023

作者： Abimannan, Satheesh John, A. Shukla, Saurabh Satheesh, Dhivyadharsini Amity University Maharashtra Department of Computer Science and Engineering Mumbai India Galgotias University Department of Computer Science and Engineering UP Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350333558

In this research paper, we introduce a unique approach to predict air pollutant concentrations. The method involves combining two pre-existing models, LSTM and SVR, by utilizing federated learning. Our proposed model is evaluated based on air quality data gathered by the Taiwan Environmental Protection Agency (EPA). By using federated learning, we can create a global model that taps into the collective knowledge of all 70 data centers while safeguarding data privacy and security. This novel method has significant implications for addressing joint air pollution mitigation and environmental protection. Additionally, the research paper presents performance comparisons of various models across three locations in Taiwan. We demonstrate the efficiency of our FL-ALSTM and FL-SVR models in predicting air pollutant concentrations. Our findings show that the FL-ALSTM model outperforms other models, improving predictions of air pollution levels by more than 95%. Overall, our study underscores the potential of federated learning in advancing air pollution prediction and environmental sustain ability. © 2023 IEEE.

关键词： Environmental Protection Agency

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Predicting Purchases and Personalizing the Customer Journey with Artificial Intelligence 11

Predicting Purchases and Personalizing the Customer Journey ...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Malik, Varun Mittal, Ruchi Chaudhry, Rishi Yadav, Suman Avdhesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Institute of Management Studies and Research Maharshi Dayanand University Haryana Rohtak India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350357

A mastery of consumer behaviour analysis and the ability to personalise the customer experience are prerequisites for success in today's cutthroat business climate. In order to improve the customer experience and make purchase predictions, this research explores the world of artificial intelligence. Using a systematic three-step technique, this study aims to improve consumer purchase prediction and provide individualised customer experience. The dataset is preprocessed using Enhanced Z-score normalisation first. This will ensure uniformity and mitigate the impact of extreme cases. After a better collection of features is found using Recursive Feature Elimination (RFE), the dataset is optimised for classification tasks. Lastly, state-of-the-art technology is used in the research via the usage of Enhanced Long Short-Term Memory (LSTM) networks for classification and prediction. Better LSTM networks may be able to detect nuanced changes in customer behaviour, resulting in more accurate predictions. Our study aims to contribute to the advancement of AI applications in CRM by following this rigorous process. We want to help companies better their strategy, offer more personalised experiences, and boost the possibility of consumers completing a purchase by delivering relevant insights. © 2024 IEEE.

关键词： Long short-term memory

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Organ Risk Prediction Using Deep Learning and Neural Networks 1

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3rd International Conference on Sustainable Advanced Computing, ICSAC 2021

作者： Bafna, Simran Shankar, Achyut Nehra, Vanshika Thakur, Sanjeev Mala, Shuchi Department of Computer Science Amity School of Engineering and Technology Amity University Noida India

ISBN: (数字)9789811690129

ISBN: (纸本)9789811690112

Parkinson’s disease is a chronic degenerative condition caused predominantly by a deficiency of dopamine in the brain. Due to a long latency before the first dopamine damage, PD is mostly not diagnosed at its early stage. The precise and successful diagnosis have been, therefore, a major challenge. To resolve the precise diagnosis of the problem of the disease, we proposed a non-invasive prediction method based on convolutional neural network prognostic modelling for specific and effective Parkinson’s disease diagnosis by diagnosing the organs at risk at different stages and also its severity. Deep learning-based approaches have recently shown promise for the tasks of medical image processing and disease detection. The proposed deep learning-based approach first extracted features from the clinical data and then merged them to predict organs at risk at various stages. Our findings show that the addition of imaging data to non-imaging clinical data in patients with PD is helpful in predicting outcomes. The suggested approach that combines clinical evidence from both imaging and non-imaging shows great promise for outcome prediction in patients with PD. This paper provides a systematic analysis of the prediction of Parkinson’s disease with an accuracy rate of overall 90.93%. This paper also presents the review of findings obtained in the literature by numerous studies to predict Parkinson’s diseases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Fake News Detection using Machine Learning

Fake News Detection using Machine Learning

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Gupta, Varun Mathur, Rohan Sahai Bansal, Tushar Goyal, Anjali Amity University Amity School of Engineering and Technology Department of Computer and Science and Engineering Uttar Pradesh Noida India Sharda University School of Engineering and Technology Department of Computer and Science and Engineering Uttar Pradesh India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

With the advent of the World Wide Web and the swift advocacy of online platforms paved the way for news propagation that has never been seen in the past. With the present situation of social media platforms, users are developing and sharing more information when compared to the last five years, some of them are not even related to real life. Classifying the text automatically is a tedious and tough job to do. To give a verdict on the truthfulness of an article, a professional too needs to explore multiple aspects of the domain first. Machine learning algorithms are popularly being used to detect the truthfulness of a piece of text. In present scenario, different performance metrics are used to compare and evaluate the effectiveness of various machine learning algorithms. The study examines various textual properties that can be utilized to differentiate the fake and real news. Natural Language Processing techniques are used for data pre-processing which increases the accuracy of the machine learning models. Further, the extracted and preprocessed properties are used to train various ML classifiers with all possible combinations and the built models are then evaluated using various performance metrics. © 2022 IEEE.

关键词： Machine learning

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