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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Alok Kumar Nishad Nitesh Pandey Vinay Kumar Pandey School of Computing Science and Engineering Galgotias University U.P. India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Credit card fraud continues to pose significant financial risk to consumers and any financial institution. To meet this task, this approach looks at machines that are particularly active in credit card fraud detection techniques, mainly focusing on random forest version Using a comprehensive dataset of credit card transactions we see how customized technologies can improve the accuracy of fraud detection. Our research aims to increase our technical understanding of techniques like decision trees, neural networks, and logistic regression. High accuracy deceptive interest detection was demonstrated by the random forest model, which had outstanding power. Beyond only comparing the different models in our study, this example shows how adaptable and trustworthy the model is when handling credit card theft. To investigate the models employed and emphasize the benefits and drawbacks of each technique for identifying fraudulent messages, we offer a thorough study of the system. Our results demonstrate the potential of machine recognition to improve fraud detection and establish random forest versions as predictors in this regard. This information has more concrete implications because it provides a reliable and powerful means of reducing economic losses and increasing the security of the financial system and of every individual. This study also suggests future directions, and it says that a combination of similar developments and models could also lead to more robust fraud detection systems. Credit card fraud continues, and is a constant *** conclusion, our research offers valuable information about credit card fraud detection, and the Random Forest version of the tool is effective, accurate, and gives better protection against fraud even in the face of ongoing attacks.

关键词： Adaptation models Accuracy Neural networks Credit cards Fraud Security Stakeholders Reliability Protection Random forests

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SentinelAI: Next-Gen Accident Detection and Emergency Notification System Integrating Autoencoders and Proximity Analytics 5

SentinelAI: Next-Gen Accident Detection and Emergency Notifi...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Karthikeyan, U. Raj, J. Relin Francis Gopikumar, S. Sangeetha, A. Priyanka, B. Rani, P. Alice School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai Avadi India Psna College of Engineering and Technology Department of Information Technology Dindigul India J.P College of Engineering Department of Electronics and Communication Engineering Thenkasi India Sarah Tucker College Department of Tamil Tirunelveli India

ISBN: (纸本)9798350379945

SentinelAI introduces an advanced accident detection and emergency notification system, utilizing cutting-edge technologies for superior performance. By employing autoencoder-based anomaly detection algorithms, SentinelAI precisely identifies accidents through G-force data from the ADXL377 accelerometer sensor, ensuring rapid and accurate event recognition. The system incorporates proximity analytics using the Haversine formula to calculate real-time distances to nearby hospitals and police stations, facilitating swift emergency response. This proximity assessment optimizes dispatch and minimizes response times, crucial in critical situations. SentinelAI features a robust communication framework, integrating Wi-Fi and GSM modules for efficient information transmission. Upon detecting an accident, the proposed system immediately notifies the nearest emergency services and a predefined insurance company, providing essential location and event details for prompt action. Combining autoencoder-based anomaly detection, proximity analytics, and rapid notification capabilities, SentinelAI represents a significant advancement in accident detection and emergency response systems. By leveraging next-generation technologies, SentinelAI aims to enhance road safety and save lives globally. © 2024 IEEE.

关键词： Accelerometers

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A probabilistic public key encryption switching scheme for secure cloud storage

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International Journal of Information Technology (Singapore) 2023年第2期15卷 675-690页

作者： Bhat, Radhakrishna Sunitha, N.R. Iyengar, S.S. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur 572103 India School of Computing and Information Science Florida International University Miami FL United States

The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates O(m+2 log N) bits of ciphertext where m∈ N and m< n, n∈ N is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper. © 2022, The Author(s).

关键词： Encryption Switching Homomorphic encryption Probabilistic encryption Public key cryptosystem Secure Cloud storage and retrieval

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A Deep Learning based Image Processing Technique for Early Lung Cancer Detection 1

A Deep Learning based Image Processing Technique for Early L...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Bhatt, Aman Joshi, Kireet Jain, Paras Gupta, Vishan Kumar Vidisha, Vidisha Graphic Era Deemed to be University School of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Department of Cse Dehradun India Vit Bhopal University School of Computing Engineering and Artificial Intelligence Kothri Kalan MP Bhopal India Amity University Punjab Amity School of Engineering and Technology Punjab Mohali India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350376517

The world's most common cause of cancer-related death is still lung cancer, which is mostly caused by rising tobacco use and industrialization in developing countries. Early detection combined with state-of-the-art medical facilities is essential since early diagnosis can drastically lower the death rate. In this study, we used 1,190 CT scan images from the Kaggle IQ-OTH/NCCD lung cancer dataset to test deep learning (DL) techniques. In order to identify high-risk patients, diagnose lung cancer, and predict malignancy, we developed enhanced images representing normal, malignant, and benign instances after completing thorough image preprocessing methods. This allowed for early intervention to avert long-term repercussions. We performed a thorough performance comparison within the framework of conventional Convolutional Neural Networks (CNN). Rigorous image preprocessing techniques such as affine transformation and Gaussian noise were utilized. Our approach achieved a validation accuracy of 99%, demonstrating the efficacy of our preprocessing methodology in reducing model complexity. The comparison highlighted that our suggested preprocessing method resulted in a higher F1-score of 99%, validating the robustness and accuracy of our approach. © 2024 IEEE.

关键词： Lung cancer

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Advanced Driving Assistant System: An Approach of Edge Detection Algorithm

Advanced Driving Assistant System: An Approach of Edge Detec...

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Advances in computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Mrinal Dev Gautam Kumar Abhishek Kumar Kavita Saini School of Computing Science & Engineering Galgotias University India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Improving road safety and reducing road accidents, hence saving lives, is a major objective of Advanced Driver Assistance Systems. Detection of road lanes and/or road limits appears to be one of the most difficult and complex tasks of future road vehicles. This is built on vehicle detection, which includes identification of the road, assessment of the car’s position in relation to the road, and analysis of the car’s driving direction. Using the vehicle’s visual system for identifying road limits and lanes is among the most popular ways. Yet, lane recognition is a challenging problem because of the variety of traffic conditions drivers can experience. A vision-based lane recognition approach that can function in real time and is resistant to lighting changes and shadows is presented in this research study. The device acquires the front image with a vehicle-mounted camera and then performs a few steps to recognise the lanes. This device captures the front image of the vehicle using a mounted camera and employs a variety of methods to identify the lanes. Using two fit plots to the lane edges, the Hough transform recovers the lanes. Its proposed lane detection method is adaptable to straight and curved roads, paint and unpainted roadways, and different weather situations. No more information is needed for the suggested method, including lane width, the length of time between lane crossings, or the offset between the centres of the lanes. No coordinate transformation or camera calibration is also necessary. The system was evaluated under a range of conditions, including varying illumination, shadow effects, and varied road kinds, with no speed restrictions. Ultimately, a comprehensive assessment of the methodologies and their prospective deployment prospects were addressed.

关键词： Roads Image edge detection Vehicle detection Lighting Transforms Visual systems Cameras

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Application of electrochemical impedance spectroscopy on overcharged lithium iron phosphate batteries: A review

Application of electrochemical impedance spectroscopy on ove...

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Australasian Universities Power engineering Conference, AUPEC

作者： Umair Afzal Weixiang Shen School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

Lithium Ion (Li-ion) batteries are susceptible to abuses and faults. They have the potential to be overcharged due to a faulty battery management system, failure of operation of charging devices, sensor inaccuracies, and inconsistency between battery cells in a pack. Overcharge faults can be very dangerous and may potentially result in thermal runaway, explosion and fire. Slight overcharging may not cause thermal runaway but can irreversibly damage a battery and accelerate ageing. Impedance is affected by different abuses and faults including overcharge faults and hence has the potential to be used as an indicator to detect an overcharging fault. Electrochemical Impedance Spectroscopy (EIS) is a powerful technique that can distinguish impedances related to different elementary chemical processes in a battery and gives rich information about changes happening inside the battery. In this paper, we present a focused review summarising the non-invasive application of EIS on overcharged lithium iron phosphate batteries. The results show the increase in ohmic and polarisation impedance values.

关键词： Lithium-ion batteries Fault diagnosis Temperature sensors Reviews Fault detection Lithium Ions Iron Impedance Electrochemical impedance spectroscopy

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Leveraging Invariant and MultiScale Features for Parkinson's Disease Detection

Leveraging Invariant and MultiScale Features for Parkinson's...

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Cognitive computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Pranjal Mohan Pandey Vedant Pandey Sakalya Mitra Arpit Singh Shreya Lakhera Trapti Sharma School of Computing Science and Engineering(SCSE) VIT Bhopal University Bhopal MP India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The moment most common neurological affliction - Parkinson's Disease (PD), effects the brain, giving rise to challenges related to movement, including issues such as rigidity, slowness, imbalance, and involuntary shaking, with the severity progressing with time. There is no established cure for the disease but AI based solutions have been continuously evolving proving their efficiency and accuracy in PD diagnosis. However, a few issues exist which needs to be addressed for PD detection: a. there is dearth of invariant modelling of features, b. there is lack of study on combined use of multiscale and invariant features and c. there is a lack of models which are able to make predictions on different types of hand drawings. The proposed model addresses these issues in three ways: first, a dataset having different types of hand drawn images like circle, meander and spiral is used. Speeded Up Robust Features (SURF) is exploited for extracting scale and rotation invariant features. A transfer learning model is also used for multiscale feature extraction in proposed hybrid model. Finally, a dense layer in employed to give the output as Healthy or Patient. The NewHandPD dataset has been used in carrying out the experiment which gives an accuracy of 99.34% outperforming all the existing works on the same dataset.

关键词： Hands Accuracy Spirals Parkinson's disease Computational modeling Biological system modeling Transfer learning Feature extraction Robustness Rigidity

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Traffic Sign Detection Using YOLOv8

Traffic Sign Detection Using YOLOv8

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Rahul Kumar Aniket Gupta Rajeswari D Department of Data Science and Business Systems School of computing College of Engineering and Technology

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy.

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Evaluation of Dynamic Power Flow Control and P2P Energy Sharing of Community Microgrids Using PSS Sincal

Evaluation of Dynamic Power Flow Control and P2P Energy Shar...

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Smart Systems for Electrical, Electronics, Communication and Computer engineering (ICSSEECC), International Conference on

作者： Gokul Sidarth Thirunavukkarasu Mohamed Ishraf Mohamed Ismath Mehdi Seyedmahmoudian Saad Mekhilef Alex Stojcevski School of Science Computing and Engineering Technologies Swinburne University of Technology Victoria Australia

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This paper presents an in-depth evaluation of peer-to-peer (P2P) energy sharing in community microgrids using PSS Sincal for power flow simulations. The study focuses on an IEEE bus system configured as community microgrids, where each bus functions as an independent microgrid equipped with loads, Solar PV systems, batteries, and converters. These microgrids are connected to the main grid and leverage a dynamic power flow control mechanism through strategically placed switches managed by a custom algorithm by ensuring the power flow converges. Four distinct scenarios were rigorously tested: ”Isolated Operation,” where each community grid meets its power requirements solely through PV generation; ”Total Dependence on the Main Grid,” where PV systems generate no power, and communities rely entirely on the main grid; ”Partial PV Generation,” where PV systems partially meet power needs, supplemented by the main grid; and ”Surplus PV Generation,” where PV systems generate excess power for sharing. The PSS Sincal simulations demonstrated the algorithm's effectiveness in enhancing energy distribution efficiency, stability, and the utilization of renewable resources. This study underscores the practical viability of P2P energy sharing in community mi- crogrids, contributing valuable insights to the development of resilient and intelligent power networks.

关键词： Renewable energy sources Heuristic algorithms Power system dynamics Microgrids Power system stability Stability analysis Peer-to-peer computing

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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