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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Preethi, C. Haripriya, K. Hammed, S. Shahul Pavalarajan, S. Priyadharshini, S. Varshambigai Achithra, B.R. Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350394702

This study focuses on the creation of a Neura Smart Book Reader that can help people who have visual impairments, especially those who are blind or have low vision and do not know Braille. The project uses IoT technology, which includes hardware, infrastructure, and services within the Internet of Things framework. The project primarily uses the energy-efficient Raspberry Pi device, which has machine-learning capabilities, making it adaptable. The Raspberry Pi computer is small and portable and operates on minimal power, showcasing advanced computing and Machine Learning algorithms. The system has a camera for capturing book pages, and the software performs Optical Character Recognition (OCR), combined with Machine Learning-driven character recognition. This improves the system's proficiency with each use. Once the image is recognized, the Neura Smart Book Reader converts it into audible text, allowing users to listen without tactile interaction. This proves how Machine Learning can improve accessibility. The device also offers online text-to-voice conversion via WiFi and 4G services and uses Machine Learning-based algorithms for enhanced linguistic articulation. This ensures a seamless reading experience for users across digital and hardcopy books. This amalgamation of IoT, Raspberry Pi, and Machine Learning technologies highlights the latest accessible reading solutions for visually impaired individuals,proving how Machine Learning can improve accessibility technology. © 2023 IEEE.

关键词： Machine learning

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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A Novel Deep Learning Approach for Number Plate Detection 7

A Novel Deep Learning Approach for Number Plate Detection

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Kavitha, M.N. Saranya, S.S. Santhiya, E. Swetha, D. Dharshika, R. Department of Computer Science and Engineering Kongu Engineering College TamilNadu Erode India Bachelor of Science - Computer Systems and Design Kongu Engineering College TamilNadu Erode India

ISBN: (纸本)9781665464086

The number of people in the world now is estimated to be 1.38 billion. As the population of India grows, there is a chance that there will be twice as many automobiles on the road. Numerous methods have been put out for finding and identifying the license plates of automobiles using various technologies and procedures. The license plate detection and identification accomplished in the paper is done by OpenCV and Tensor flow for the detection of the license plate. The second set makes use of CNN (Convolutional Neural Network). First, the input image has been taken and pre-processed. Next, the number plate has been detected and contours are extracted. Finally, the characters have been recognized and the model has been trained using CNN for predicting the characters in the number plate. Comparing to other TensorFlow projects, this system provides the highest accuracy. © 2023 IEEE.

关键词： Tensors

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Machine Learning Techniques for Detecting Fraud in Credit Card Transactions 31

Machine Learning Techniques for Detecting Fraud in Credit Ca...

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31st Symposium of Advanced Database systems, SEBD 2023

作者： Mahmood, Tariq Hashemi, Seyedeh Khadijeh Mirtaheri, Seyedeh Leili Greco, Sergio Department of Computer Science Modeling Electronics and Systems Engineering University of Calabria Italy Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran

Credit cards became one of the most popular payment methods as technology advanced and e-commerce services expanded, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in the number of frauds necessitates high banking transaction costs. As a result, detecting fraudulent activities has become an intriguing topic, attracting a large number of researchers. We consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions in this study. We use Bayesian optimization, in particular, to optimize the hyperparameters while taking into account practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as using CatBoost and XGBoost to improve the performance of the LightGBM method by taking the voting mechanism into account. Finally, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one, in order to improve performance even further. We conducted some experiments to evaluate the proposed methods on real-world data. We use recall-precision metrics in addition to the common ROC-AUC to better cover unbalanced datasets. We use a 5-fold cross-validation method to test CatBoost, LightGBM, and XGBoost separately. Furthermore, the performance of the combined algorithms is evaluated using the majority voting ensemble learning method. According to the results, LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79. Also, by using deep learning and the Bayesian optimization method to tune the hyperparameters, we meet the following criteria: ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a big improvement over the state-of-the-art methods we compared it to. © 2023 CEUR-WS. All rights reserved.

关键词： Learning systems

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An Efficient Secure Routing and Hierarchical Approach to Elliptic Curve Cryptography Combined with Distributed Group Key Management 9

An Efficient Secure Routing and Hierarchical Approach to Ell...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Anitha, Cuddapah Priscila, S. Silvia Mahaveerakannan, R. Balakrishnan, Biju Mohan Babu University Computer Science and Systems Engineering SreeVidyaniketan Engineering College AP 517102 India Department of Computer Science Tamil Nadu India Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

Applications for such groupings are numerous, as is the need for reliable and secure internal communication. To ensure multicast application security, a group key must be updated each time a member joins or departs the group. Approximately ten years ago, many effective management techniques were implemented to reduce computer overheads. They had not, however, been able to improve the efficiency of rekeying. Elliptic Curve Cryptography for Effective Group Key Management (ECC) is offered for efficient and group communication that is secure in dynamic multicast groups. This strategy creates a hierarchical arrangement among group members. The key server uses each group's position within the hierarchy private key to create a public key. In this research, we will describe a method for managing group keys that is dispersed and hierarchicalsecures the creation and distribution of group keys using elliptic curve cryptography and the hash function. These freshly obtained public keys are used the creation of a shared group key. The key server then provides a group member with the group key and the private key's inverse value. The group key is updated when a new member joins, and the subgroup members are informed of the modified keys. The main server also updates the other group members of the inverse value of the departing member. As a result, this process will make rekeying less computationally demanding. The suggested method reduces computing overhead and communication overhead according to simulation results. © 2023 IEEE.

关键词： Inverse problems

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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Sector-Based Incremental Clustering and Scalable Deletion for Real-Time Big Data Streaming Application 2nd

Sector-Based Incremental Clustering and Scalable Deletion fo...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Ananthi, M. Mangayarkarasi, T. Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031689079

Massive streaming data is the most current technology for storing and manipulating large quantities of data. Processing the substantial amount of streaming data is still a challenging issue. The speed and throughput can be improved while extracting this huge amount of data by using a dynamic query processing technique. The sector-based incremental clustering indexing and scalable deletion approach is used in this paper to improve the system performance. Stream processing can be more effective and flexible by applying dynamic query processing technique. Incremental clustering using sector-based method and scalable deletion which removes unwanted, spurious data is proposed in this work in order to process incoming streaming data effectively. Incremental clustering is one of the clustering methods used for processing dynamic information. It is not necessary to plan the entire dataset ahead of time in this method which is suitable for live streaming data, because it processes one instance at a time. So, less space and time is used. Various queries with different types of datasets executed using sector based incremental clustering and scalable deletion methods. The proposed system achieves the highest results for more than 90% accuracy with less space and more speed when the execution time and data retrieval are empirically increased compared with conventional approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data streams

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IBS: Secured Healthcare Data Management over Cloud Environment using Integrated BlockChain Strategy 1

IBS: Secured Healthcare Data Management over Cloud Environme...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Haripriya, K. Preethi, C. Hammed, S. Shahul Pavalarajan, S. Meenambika, A. Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350359756

Healthcare needs better diagnosis and disease analysis methods to simplify the *** address these concerns, a novel approach called Integrated Blockchain Strategy (IBS) has been developed, combining various cutting-edge technologies, including Blockchain, and Cloud-based Data *** aims to enhance healthcare disease prediction and secure data management. It leverages the logical Blockchain and secured cipher strategy, with a focus on the Advanced Encryption Standard (AES) algorithm for data security. A unique aspect of this approach is the Attribute-Based Secret Key Generation Logic (ASKGL), which modifies the key generation process by incorporating user authorization attributes and concatenating them with a random key to create a new cipher key. By integrating these elements, the proposed approach ensures data security on the server end and facilitates disease prediction analysis with improved accuracy. Artificial Intelligence (AI) plays a vital role in handling complex healthcare-assisted data management tasks, simplifying the overall process. The result provides compelling evidence to support the efficiency of the proposed approach. It demonstrates reduced time consumption constraints, enhanced prediction accuracy, and other relevant benefits. In conclusion, the suggested approach offers a comprehensive and effective solution for healthcare data management, addressing the challenges faced in the healthcare domain with clarity and precision. © 2023 IEEE.

关键词： Artificial intelligence

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Backup subscription scheme for differential protection using IEC61850-9-2 sampled values

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Energy Informatics 2024年第1期7卷 110页

作者： Katoulaei, Mohammad Khalili Rahmani, Aamir Høidalen, Hans Kristian Oleinikova, Irina Mork, Bruce Department of energy systems Norwegian University of Science and Technology Trondheim Norway Department of electrical power system Michigan Technical University Michigan United States Department of Electrical Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Electrical and Computer Engineering Michigan Technological University HoughtonMI49931 United States

In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection. © The Author(s) 2024.

关键词： Hardware-in-the-loop simulation

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Extracting Motion Patterns of Greeting Gestures Using GNG with Spiking Neuron Model 13

Extracting Motion Patterns of Greeting Gestures Using GNG wi...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Obo, Takenori Wibowo, Angga Wahyu Sato-Shimokawara, Eri Shibata, Hiroki Ho, Yihsin Kobayashi, Ichiro Tokyo Metropolitan University Department of Mechanical Systems Engineering Tokyo Japan Tokyo Metropolitan University Department of Computer Science Tokyo Japan Takushoku University Department of Electronics and Computer Systems Tokyo Japan Takushoku University Faculty of Engineering Tokyo Japan

ISBN: (纸本)9798350373332

This study focuses on behavior patterns, which are characteristic actions common to groups or communities, to promote intercultural understanding. Understanding greeting gestures is crucial for comprehending social relationships, but these vary across cultures and regions. Recent advancements in sensing technology and artificial intelligence, such as RGB-D cameras and markerless motion capture systems, have made it easier to capture motion data in diverse formats. Therefore, it is important to extract relevant motion patterns and apply appropriate structuring for effective long-term archiving. We propose a method for extracting motion patterns using a Growing Neural Gas (GNG) algorithm with a Spiking Neuron Model. Additionally, this paper presents an experimental example of extracting a gesture pattern in Indonesian style. © 2024 IEEE.

关键词： Economic and social effects

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