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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Yi Zheng, Wenying Pandi, Vijayakumar Bhuiyan, Md Zakirul Alam Thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

The edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 IEEE.

关键词： Edge computing

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Answer Type Prediction: Unveiling the Power of Deep Learning Models 2

Answer Type Prediction: Unveiling the Power of Deep Learning...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Swathi, B.P. Geetha, M. Shenoy, Manjula Suhas, M.V. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information & Communication Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal India

ISBN: (纸本)9798350308846

Answer type prediction plays a crucial role in natural language question answering systems, enabling the generation of relevant SPARQL queries, a query language for retrieving data from RDF databases. This research paper focuses on answer type prediction for SPARQL query generation. Answer type prediction involves identifying the expected type of the answer to a given question, such as a person's name, a location, or a numerical value. By accurately predicting the answer type, the subsequent SPARQL query generation process can be tailored to retrieve the desired information from a knowledge base. The proposed work evaluates the performance of four deep learning models, which include GRU (Gated Recurrent Unit), Bi-GRU, LSTM (Long Short-Term Memory), and Bi-LSTM. The study's conclusion highlights GRU as the top-performing model for predicting the answer type based on the analysis of input natural language queries. The performance of the answer type prediction model is evaluated on a publicly available dataset, demonstrating its effectiveness in achieving an accuracy of 81% using GRU. The results obtained from the study emphasize the importance of accurate answer type prediction and provide promising outcomes. © 2023 IEEE.

关键词： Answer type prediction Bi-GRU Bi-LSTM GRU LSTM Natural Language Processing Natural Language Query Question Answering Systems SPARQL

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Skin Cancer Diagnosis Using High-Performance Deep Learning Architectures 3rd

Skin Cancer Diagnosis Using High-Performance Deep Learning A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Bindhu, A. Ahilan, A. Vallisree, S. Maria Jesi, P. Muthu Kumar, B. Marriwala, Nikhil Kumar Sabr, Aznul Qalid Md Department of Computer Science Infant Jesus College of Arts and Science for Women Tamil Nadu Kanyakumari District Mulagumoodu India Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Electronics Engineering MIT Campus Anna University Tamil Nadu Chennai India Department of Computer Science and Engineering Loyola Institute of Technology and Science Loyola Nagar KK District Thovalai India School of Computing and Information Technology REVA University Karnataka Kattigenahalli560064 India Department of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra University Kurukshetra Thanesar India Department of Artificial Intelligence FCSIT University in Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9789819986453

Skin cancer is one of the dangerous diseases which causes human death. It is originated by the abnormal growth of melanocytic cells. The malignant Skin cancer is also referred by another name as melanoma. The key source for the computer aided diagnosis is Dermoscopy image which acquires the skin cancer. Fortunately, a high percentage of skin cancers can be cured if they are found early. Automatic early detection approaches are being used as quick and practical skin cancer screening due to the expense and morbidity of this biopsy procedure. The existing methods of melanoma detection meet-up the demerits such as less accuracy, false segmentation and more time consumption. To overcome these issues, the proposed High performance Deep Learning Architecture method for classifying the skin cancer. It involves three types of techniques are used for comparison study are MobileNet, ResNeXt101, and Multi-Model Ensemble. The performance metrics used for the comparison are accuracy, precision, and recall. The comparison of the accuracy value for MobileNet, ResNeXt101 and Multi-model ensemble techniques are 97.2%, 84.2% and 86% respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Real-Time Driver Drowsiness Detection Using YOLOv8 with Whale Optimization Algorithm

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SAE International Journal of Transportation Safety 2025年第1期13卷 15-33页

作者： Nandal, Priyanka Pahal, Sudesh Sharma, Tripti Omesh, Omesh Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering India Maharaja Surajmal Institute of Technology Department of Electronics and Communication Engineering India Maharaja Surajmal Institute of Technology Department of Information Technology India

Driver fatigue and drowsiness portray an integral role in the frequency of road accidents. Putting in place policies intended to alert drivers is imperative for averting accidents and saving lives. This work aims to improve road safety by devising a real-Time driver drowsiness detection system. To accomplish this, drowsiness is detected using YOLOv8 algorithm optimized with the whale optimization algorithm (WOA). Key facial cues such as eye closure and yawning frequency are monitored to analyze driving behavior by the suggested approach. YOLOv8 model optimized with WOA processes video streams in real time and sets off an alarm on the graphical user interface (GUI) dashboard based on the output. The proposed approach was investigated using two datasets namely UTA-RLDD and D3S. A 640 × 640 pixel image with a frame rate of 50 fps was used in the investigation. The mAP at 0.5 (mean average precision at 0.5 IoU (intersection over union) threshold) of drowsiness detection system using UTA-RLDD dataset is 85.4% and using D3S dataset is 84.3%. It was indicated by the obtained results that the WOA-optimized YOLOv8 model attains superior detection accuracy and quicker inference times in comparison to the contemporary methods. This analysis sets the stage for advancements in real-Time drowsiness detection techniques for implementation in vehicle safety systems. © 2025 SAE International. All rights reserved.

关键词： Graphical user interfaces

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Analyzing the Potential Benefits of Data Mining for Customer Relationship Management 1

Analyzing the Potential Benefits of Data Mining for Customer...

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

作者： Aravind, A.R. Katikar, Shilpa M. Acharjya, Kalyan Ezhilarasan, G. Yadav, Surendra Mishra, Abhinav Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350359756

Data mining for client dating control (CRM) is figuring out patterns and traits in customer records to inform selections about product imparting, pricing, sales, advertising and marketing, and different consumer techniques. Companies can use automated records mining strategies to outline client profiles, section clients, and better recognize purchasing conduct. Through knowledge of client conduct, corporations can target their customers greater correctly, grow revenue, and enhance customer satisfaction and loyalty. The advantages of fact mining for CRM are several, which include spotting purchaser needs and options, improving marketing effectiveness, increasing client acquisition, and boosting sales. Additionally, records mining can enable an employer to perceive lost sales possibilities, lessen operational costs, and optimize customer retention efforts. Essential data mining allows CRM-targeted organizations to recognize clients higher, optimize operations, and broaden strategically centered initiatives to improve consumer loyalty. © 2023 IEEE.

关键词： Data mining

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Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach 2

Leveraging Deep Autoencoders for Security in Big Data Framew...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Roy, Sourav Nijaguna, G.S. Shankar, Smitha Sharath Adnan, Myasar Mundher Umaeswari, P. Department of Electronics and Communication Engineering Bangalore India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India The Islamic University Najaf Iraq R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350382952

Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. © 2024 IEEE.

关键词： Cryptography

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Electric Vehicle Charging System Using RFID and IoT for Enhanced User Experience 5

Electric Vehicle Charging System Using RFID and IoT for Enha...

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5th IEEE Middle East and North Africa communications Conference, MENACOMM 2025

作者： Hassain, Md Mehedi Haque Jamil, Injamul Hoque, Md Mahmudul Hasan, Md. Jahid International Islamic University Chittagong Dept. of Electrical and Electronics Engineering Chittagong Bangladesh University of Greenwich Dept. of Computing and Mathematical Sciences London United Kingdom CCN University of Science and Technology Dept. of Computer Science and Engineering Cumilla Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh

ISBN: (纸本)9798331519957

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-Time insights into energy use and account balances. All information is updated and displayed in real-Time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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Enhanced Security and Privacy for IoT Based Locker System Operated at Low Frequency Spectrum Using Blockchain 1st

Enhanced Security and Privacy for IoT Based Locker System Op...

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1st International Symposium on Artificial Intelligence, ISAI 2022

作者： Santra, Soumen Sharma, Sweta Deyasi, Arpan Department of Computer Application Techno International Newtown Kolkata700156 India Department of Electronics and Communication Engineering RCC Institute of Information Technology Kolkata700015 India

ISBN: (纸本)9783031224843

Conventional locker system faces continuous threat of breaching of data due to phishing scams, which raises high demand of encryption at server’s end. Blockchain based security system provides efficient solution so far either by applying personal signature, or by adopting peer-to-peer network. In the present proposal, security is entrusted by generating new encrypted message from the already received binary information from user’s end. The code is incremented at each subsequent step which helps to track the number of times the security door is opened with individual time-stamp along with image capture facility. Corresponding circuit is designed at 433 Hz spectrum where relay is connected with electromagnetic door lock through microcontroller. A novel algorithm is proposed to control cloud-based web server for accessing every HTTP request with NodeJS and a MongoDB database. Enhanced security and privacy can therefore be obtained through low-cost hardware system associated with blockchain feature. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Inclusion of IoT, ML, and Blockchain Technologies in Next Generation Industry 4.0 Environment

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Materials Today: Proceedings 2023年 80卷 3471-3475页

作者： Shrivastava, Anurag Murali Krishna, K. Lal Rinawa, Moti Soni, Mukesh Ramkumar, Gowtham Jaiswal, Sushma ECE Lakshmi Narain College of Technology and Science Indore India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Andhra Pradesh Duvvada Visakhapatnam India Department of Mechanical Engineering Government Engineering College Jhalawar Rajasthan India Computer Science & Engineering Department Jagran Lackcity University Bhopal India SRM Institute of Science and Technology Chennai India Koni Bilaspur495009 India

Industry 4.0 is a revolutionizing concept which has a more significant implication to numerous industries of various sectors worldwide. Industry 4.0 acts as a technical catalyst in accelerated growth through systematic adaptation and new-generation cutting-edge engineering tools. IoT, Blockchain, Artificial Intelligence, Augmented Reality, 3D printing, Big-data analytics, cloud-computing are some of the primary technologies which work in join hand to make the vision of Industry 4.0 successful. In addition, it promotes autonomous industry, which in turn considerably reduces human intervention, thus resulting in efficient working and safeguard companies' interests. Due to the faster adaption of Industry 4.0, authors have attempted to define each and every concept related to it and to explore a futuristic roadmap, special focusing on its implication in the contemporary market. In the 21st century Industry, 4.0 is the need of the hour, so it has become essential to understand the state-of-the-art technologies involved. © 2021

关键词： Industry 4.0

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Customer Churn Prediction Analysis for Retention Using Ensemble Learning 15

Customer Churn Prediction Analysis for Retention Using Ensem...

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

作者： Charanya, J. Sureshkumar, T. Kavitha, V. Nivetha, I. Pradeep, S. Dhamu Ajay, C. Kongu Engineering College Department of Artificial Intelligence Erode Tamil Nadu Perundurai638060 India Department of Information Technology Tamil Nadu Coimbatore641105 India Velalar College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Erode638012 India Kongu Engineering College Department of Electronics and Communication Engineering Erode Tamil Nadu Perundurai638060 India

ISBN: (纸本)9798350370249

In the 21st century, there is a handful of information on the Internet. It is easy for customers to switch to a competing company in any sector. It is important for a company to retain its existing customers rather than secure new ones. So, customer retention becomes an essential part of an organisation's business. Customer churn prediction is a type of data analysis based on the historical data of any organization. It helps to predict whether the customers are going to leave the organisation or not in advance. Also, it helps the organisation predict what customers are expecting from them and where they are lacking. There are a wide range of algorithms used for churn prediction. However, there is no agreement on choosing the best one. Therefore, this project presents customer churn prediction using the ensemble method in the telecommunications sector. The ensemble approach amalgamates predictions from base classifiers such as decision trees, random forests, gradient-boosting machines, and support vector machines. By studying the strengths of these models, ensemble techniques like bagging, boosting, and stacking mitigate individual model biases and variance, leading to improved predictive performance. We are using the XGBoost and Random Forest methods to predict the churn. It is a type of ensemble method. The ensemble method is used to increase accuracy by combining multiple models rather than using a single model. From the result, we have observed that the proposed model, XGBoost and Random Forest, attains 99% accuracy. Overall, the project adds significant value by enabling proactive churn management, customer-centric strategies, and sustainable business growth. © 2024 IEEE.

关键词： customer churn Ensemble Learning Random Forest XGBoost

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