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检索条件"机构=Department of Computer Applications in Science and Engineering"

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

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Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes 10

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at H...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Ke Tang, Jiankai Jiang, Zhang Wang, Yuntao Liu, Xiaojing Li, Dong Shi, Yuanchun Qinghai University Department of Computer Technology and Applications Qinghai810016 China National Key Laboratory of Human Factors Engineering Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

ISBN: (纸本)9798331520861

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO 2 predictions by 7. 6 % and 1 0. 6 % compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2. 5 % for SpO 2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation by 1 7. 8 %. © 2024 IEEE.

关键词： Photoplethysmography

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Enhancing Gas Leak Detection with IoT Technology: An Innovative Approach 2

Enhancing Gas Leak Detection with IoT Technology: An Innovat...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Babu, Tina Nair, Rekha R. Kishore, S. Vineeth, M. Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Mangalore India Buisness Intelligent Consultant Zellis HR Infopark Ernakulam India

The presence of a natural gas leak within a household carries the potential for fires and poses a risk of natural gas poisoning. Similar to how we approach other hazardous energy sources such as electricity and gasoline, it is crucial to exercise caution when dealing with natural gas exposure. To prevent potential hazards and dangerous situation, identified a smart gas detection system for rapid and accurate detection of gas leaks. The proposed gas leakage detection system combines advanced sensor technology, real-time monitoring, and automated alert mechanisms to ensure timely identification and response to gas leaks. The MQ2 sensor helps in detection of gas leakage. The MQ2 sensor possesses the capability to detect a wide range of gases, including methane, propane, carbon monoxide, and hydrogen. This versatility makes it an invaluable tool in ensuring safety and protecting against potential hazards and early identification of gas leaks. The collected data is analyzed using sophisticated algorithms to distinguish between normal background gas levels and potential leaks. NodeMCU, equipped with its Wi-Fi capabilities, functions as the central control unit of the system. The NodeMCU gathers real-time data from the gas sensors, constantly monitoring gas levels. It processes this data and sends it to the cloud or a central server using its internet connection. The system's ability to monitor in real-time ensures that any gas leaks detected are quickly reported to the relevant personnel or authorities through automated alerts. The fast notification system allows for quick actions, reducing the risks of accidents, saving lives, preventing property damage, and mitigating harm to the environment. © 2024 Elsevier B.V.. All rights reserved.

关键词： Gas detectors

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A Novel Hybrid Genetic Algorithm for Deadline Based Cost Optimization in Cloud Environment 4

A Novel Hybrid Genetic Algorithm for Deadline Based Cost Opt...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bothra, Sandeep Kumar Bhatt, Devershi Pallavi Sharma, Amit Kumar S. S. Jain Subodh P.G. College Department of Computer Science Jaipur India Manipal University Jaipur Department of Computer Applications Jaipur India Manipal University Jaipur Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798331529635

Cloud computing is a well-established paradigm that is gaining popularity due to its high performance, elastic, and scalable computing resources over the internet. These features make it an it an appropriate platform to schedule complex scientific workflows over it. Workflow scheduling is considered NP-hard in nature. Scientists face a challenge when scheduling scientific workflow in a cloud due to its security, heterogeneity, and dynamic nature. Although there have been many studies on workflow scheduling in relation to grid and cluster computing throughout the years, not many of them have been specifically designed for the cloud. Furthermore, the earlier research did not take into account the fundamentals of cloud infrastructure, like varying performance of Virtual Machine (VM), acquire delay, virtual machine (VM) startup and shutdown times, bandwidth of channel, communication delay, and other QoS parameters. To fulfill the deadline and minimize the total computation cost, this research suggests a hybrid model based on Genetic Algorithms (GA) with appropriate heurist techniques. Heuristic techniques aid in producing high-quality seed to improve the performance of GA in its convergence rate. For this, we have applied Predict Earliest Finish Time (PEFT) and Heterogeneous Earliest Finish Time using Task Duplication (HEFT-TD) during the initialization of the population. We have used cutting-edge techniques in the genetic algorithm's crossover and mutation operators. Our hybrid paradigm considered varying performance of virtual machine, acquisition delay, booting time, and shutdown time of VMs. We performed our experiment on the simulator WorkflowSim on four types of scientific workflows. The outcomes show that the recommended approach performs workflow scheduling with the lowest costs and makespan when compared to other algorithms, demonstrating its exceptional superiority. © 2024 IEEE.

关键词： Cloud platforms

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Advanced Cyberbullying Detection: A Hybrid Model Integrated with Naïve Bayes 3

Advanced Cyberbullying Detection: A Hybrid Model Integrated ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Priyadharshini, M. Hemagowri, J. Banu, A. Faritha Nithya, B. Srinidhi, K. Murugesh, V. Department of Computer Science and Engineering Hyderabad501 203 India Nehru Arts and Science College Department of Computer Science Tamilnadu Coimbatore641 105 India Karpagam Academy of Higher Education Department of Computer Science Tamilnadu Coimbatore641 021 India St. Josephs's College for Women Department of Computer Applications Tamilnadu Tirupur641 607 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India

ISBN: (纸本)9798350369083

The problem of cyberbullying is common on social media and online platforms, but it requires more advanced detection techniques. The below-captioned article suggests a holistic method to recognize cyberbullying utilizing Naive Bayes algorithm and TF-IDF (Term Frequency-Inverse Document frequency) algorithm, for text analysis of various types of social media content. Including the Naive Bayes algorithm, along with Text Preprocessor (TFIDF), for preprocessing text data drastically improves how well and quickly it is able to classify content as non-bullying or bullying. This work not only emphasize on identifying cyberbullying in social media, using various categories of data and approaches but also stresses that integrating different analysis methodologies is necessary due to the challenge nature of detecting CYB in existing multi-type social medias. Our novel model performs best in detection of cyberbullying, which can result to private and reliable online communications as well as more efficient monitoring tool for social media platforms. © 2024 IEEE.

关键词： Phishing

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Modular irregular labelling in Network Analysis with Complete Bipartite Graph as Auto Proctoring 4

Modular irregular labelling in Network Analysis with Complet...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Sivakumar, S. Vidyanandini, S. Nayak, Soumya Ranjan Aluvala, Srinivas Srm Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai603203 India Srm Institute of Science and Technology Kattankulathur College of Engineering and Technology Department of Mathematics Chennai603203 India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350349856

Modular irregular labeling is an edge irregular labeling of a graph in which the edges and vertices are labelled with 1, 2, 3,.. k such that the weight of all vertices are different. The complete bipartite graph is whose vertices are divided into two subsets such that no edge is in the same subset. In this paper the modular irregular labeling technique is applied in the complete bipartite graph. It is also planned to develop certain theorems in work of applying the modular irregular labeling in complete bipartite graph. An application in complete bipartite graph in the field of networking is also discussed. The aim of the work is to build the theorem and to promote an application in complete bipartite graph. The application is about the autoproctor for the online examination to make work easier for the examiner to examine students. © 2024 IEEE.

关键词： Graph theory

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Power Conscious Clustering Algorithm Using Fuzzy Logic in Wireless Sensor Networks 1st

Power Conscious Clustering Algorithm Using Fuzzy Logic in Wi...

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1st International Conference on Innovations in Intelligent Computing and Communication, ICIICC 2021

作者： Sarangi, Sanjaya Kumar Nanda, Arabinda Chowdhury, Manas Ranjan Mishra, Subhadra Department of Computer Science and Applications Utkal University Odisha Bhubaneswar India Department of Computer Science and Engineering Krupajal Engineering College Odisha Bhubaneswar India Department of Computer Science and Engineering Trident Academy of Technology Odisha Bhubaneswar India Department of Computer Science and Applications Odisha University of Agriculture and Technology Odisha Bhubaneswar India

ISBN: (纸本)9783031232329

The most challenging difficulties in Wireless Sensor Networks (WSNs) is energy conservation. Data fusion is the process of combining data from several sources to produce a single scenario. It has the ability to save a large amount of sensor energy while simultaneously enhancing sensing data accuracy. Reduced energy usage is mainly essential and difficult examination issue in WSNs. Computational intelligence (CI) principles like localization, grouping, power-sensitive routing, job arrangement, and protection are now widely employed WSNs & other applications. WSNs are organized into clusters to optimize data-collecting efficiency while minimizing energy dissipation. Clustering allows you to organize a deployed network into a connected hierarchy while simultaneously balancing network load and increasing the system's lifespan. Each sensor node in a cluster-based WSN sends the data it has acquired to the cluster coordinator for the cluster in which it is located. The expanse among cluster heads & events, with the energy of clusters, is fuzzified in order to choose clusters for data uploading and fusing using fuzzy logic. The cluster heads apply the fugitive logic technique in confined resolution building as well, and the outcomes of the local decision-making are then relayed to the base station. As a result of this research, a fuzzy logic-based power-conscious active clustering method is developed, that amplifies system duration assessed by means of Last Node Dies (LND). Results of this simulations carried out illustrate the usefulness of this approach. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy logic

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Recognition of Data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Scalable Image Processing with Apache Spark and Convolutional Neural Networks

Scalable Image Processing with Apache Spark and Convolutiona...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sharma, Deepika Rawat, Ruchira Alsalami, Zaid Chinthamu, Narender Varshney, Neeraj Saraswat, Manish Noida International University Department Of Computer Science And It U. P India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Mit Cto DallasTX United States Gla University Department Of Computer Engineering And Applications Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This research presents a new framework called Spark-CNN that incorporates Apache Sparks distributed computing capabilities with Convolutional Neural Networks (CNNs) for images work processing. The results of experimental evaluation show that the proposed framework is indeed effective in handling large-scale image datasets, wherein substantial improvements have been noted regarding reduced processing time, scalability and accuracy. In Trial 1, Spark-CNN outperformed standalone -Spark (180 minutes), and all CNNs (240minutes) with an estimated processing season of 120 mins. Scale2 demonstrated the scalability of analyzing 2, sustaining constant processing times despite an increase in image dataset size from 1 million to 10 million. Further, in the third examination, Spark-CNN got an accuracy of 89%, surpassing standalone CNNs with a percentage score of 85%. Comparing the Trial 4 with Horovod-CNN, Spark-CNN showed a cutthroat processing time (120 minutes) and enhanced accuracy (89% vs., Trial 5 presented the influence of cluster setup on Spark-CNN revealing reduced execution times and better precision with growing numbers of executors beneficiaries per agent. These results establish Spark-CNN as a robust and effective solution to address scalable image processing challenges across different areas. © 2024 IEEE.

关键词： Convolution

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A Robust MAES Encryption And Decryption Algorithm using Ankaru Model Through Fog Computing

A Robust MAES Encryption And Decryption Algorithm using Anka...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Karunya, S. Sara, Belina V.J. Angayarkkani, K. Sowmiya, J. Sonia, A. Jeganraj, P. Dwaraka Doss Goverdhan Doss Vaishnav College Department of Information Technology & Bca Chennai India Srm Institute of Science & Technology Department of Computer Applications Chennai India Aalim Muhammed Salegh College of Engineering Department of Cse Chennai India Srm Institute of Science & Technology Master of Computer Applications Chennai India Srm Institute of Science & Technology Department of BCA-DS Chennai India

ISBN: (纸本)9798350360660

The world as a whole now depends on the data that people have stored online and how these developments are tracked in the future. To satisfy the requirements of safeguarding the information of an individual, a company, or a rapidly growing sector, there is only one standard. The first phase, encryption, and the second, decryption, are absolutely necessary to complete. Data is encrypted for protection, then it is decrypted for retrieval. The Advanced Encryption Standard provides the highest level of security for data protection, ensuring that its processing remains hidden. Data on edge devices is encrypted using the AES method by Fog. Since AES has never experienced a data breach, safety and security are the most fundamental truths that may be discovered through its use. Data from Edge devices is encrypted and then modified using fog computing to secure it using the AES technique. The data is then recovered by decryption. The secret key is primarily concerned with data entering and returning to the source, which transforms the proper propagation outcome. © 2024 IEEE.

关键词： advanced encryption standard advanced encryption standard and transformation cryptography decryption modified

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