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检索条件"任意字段=2nd International Conference on Computer Communications Control Technology"

共 23700 条记录，以下是211-220 订阅

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Effective Malware Classification using Fine-tuned CNN Architecture: An Image-Based Approach 2

Effective Malware Classification using Fine-tuned CNN Archit...

引用

2nd IEEE international conference on Networking and communications, ICNWC 2024

作者： Sasikala, L. Shanmuganathan, C. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350365269

Malware detection and classification are crucial in cybersecurity to protect systems and networks against malicious attacks. This study introduces a proficient image-based method employing convolutional neural network (CNN) architecture. It aims to enhance malware classification by addressing the challenge of detecting polymorphic or obfuscated malware variants, which can be difficult with conventional techniques reliant on static or dynamic analysis of executable files. Our proposed model tackles this constraint by representing malware samples as grayscale and RGB images, capturing both structural and visual aspects. To improve classification accuracy, we fine-tuned a pre-trained CNN model on a Malign dataset of 25 families and 9342 malware samples, leveraging transfer learning. Our experimental results show that our method accurately classifies distinct types of malware with excellent precision and recall rates. Additionally, image-based representation is resistant to evasion strategies used by advanced malware. Our proposed approach demonstrates potential for enhancing malware detection and classification in practical cybersecurity settings © 2024 IEEE.

关键词： Cybersecurity

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Prediction of Cement Slurry Formulation Based on RMAPCN 2

Prediction of Cement Slurry Formulation Based on RMAPCN

引用

2nd IEEE international conference on control, Electronics and computer technology, ICCECT 2024

作者： Liu, Ruohuan Wang, Bin Yang, Yazhi Zhang, Xingpeng School of Intelligent Terminal Industry Chengdu Technological University Yibin China School of Computer Science SouthWest Petroleum University Chengdu China

ISBN: (纸本)9798350380958

Cement slurry formulation design is a very important process in cementing operations, and the performance of the formula will directly affect the quality of cementing. In traditional cementing operations, professionals perform experiments based on information from adjacent Wells to obtain fuzzy cement formulas, and then constantly adjust the final cement formula in the actual operation. This operation can cost a lot of labor and time, which is not good for real-time operation. To solve the above problems, a new forecasting model RMAPCN (IndRNN with Multi-Head Attention Mechanism and PSO-CNN) is proposed in this paper for the prediction of cement slurry formulation. The model can quickly obtain the cement slurry formulation with higher accuracy and better timeliness by mining the characteristics of the existing data information and using the well information. The comparison between the RMAPCN model and the baseline model shows that the RMAPCN model has obvious advantages in each performance index, and can be used to guide the actual cement slurry formulation design. © 2024 IEEE.

关键词： Recurrent neural networks

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AI Based Gym Management System with Body Performance Index measurement and Tips 2

AI Based Gym Management System with Body Performance Index m...

引用

2nd IEEE international conference on Networking and communications, ICNWC 2024

作者： Srivastava, Aman Diya Ajith, K. Annamalai, Suresh SRM Institute of Science and Technology School of Computing College of E&T Department of Networking and Communications Tamil Nadu Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

The GMS-BPI Gym System is like a super cool computer program for gyms! It helps gym owners and people who go to the gym. For the owners, it makes things easier like signing up new members, planning classes, fixing gym stuff, and keeping track of money. There's even a fancy screen that shows how the gym is doing. But the awesome part is how it helps people who work out. GMS-BPI uses special gadgets to collect info about how fit they are. So, people can see how healthy they're getting, make goals, and get workout plans just for them. And it figures out a special number called the Body Performance Index to show how fit someone is overall. Recent Techniques - Wearable Fitness Trackers Integration, AI-Powered Personalized workout Plans, VR, AR Techniques for Workout Enhancements. Challenges Faced by the GMS-BPI System - Data Privacy and Security Concerns, Integration Complexity with diverse devices, User Engagement & Retention, Accuracy and Reliability of BPI. © 2024 IEEE.

关键词： Machine learning

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IoT-Enabled Horticultural Lighting for Optimizing Plant Growth and Agriculture Operations 2

IoT-Enabled Horticultural Lighting for Optimizing Plant Grow...

引用

2nd IEEE international conference on Networking and communications, ICNWC 2024

作者： Srinivasan, S. Lk, Shoba Alavanthar, T. Srinivasan, C. Murugan, S. Sujatha, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Department of Biomedical Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350365269

The Internet of Things (IoT) and horticulture lighting systems may improve plant growth and agricultural operations. The variation in the light spectrum, intensities, and durations affect plant physiological systems. The proposed system can dynamically adjust and control lighting settings using real-time sensor data by seamlessly integrating IoT capabilities. These sensors carefully track plant health, ambient conditions, and energy use. This dynamic feedback system allows operators to make informed choices and adjust lighting techniques for optimum growth, yields, and resource use. Plant growth and resource efficiency benefit from light parameter improvement. The connection between IoT and horticulture lighting leads to sustainable agriculture that maximizes agricultural yields and energy efficiency. Moving from static to adaptive lighting systems represents a paradigm change in agriculture, as data-driven decisions enable precision farming. Combining IoT's real-time abilities with horticulture lighting systems promises unique yields, energy savings, and sustainable agriculture practices. © 2024 IEEE.

关键词： Horticulture

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Cloud-Enabled Isolation Forest for Anomaly Detection in UAV-Based Power Line Inspection 2

Cloud-Enabled Isolation Forest for Anomaly Detection in UAV-...

引用

2nd IEEE international conference on Networking and communications, ICNWC 2024

作者： Ramasamy, Jayabharathi Srividhya, E. Vaidehi, V. Vimaladevi, S. Mohankumar, N. Murugan, S. RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Mission's Kirupananda Variyar Arts and Science College Department of Computer Science Tamil Nadu Salem India Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Management Department of Commerce and Business Administration Tamil Nadu Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350365269

Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a Cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm's efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system's cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales. © 2024 IEEE.

关键词： Remote sensing

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Recognition of Hand Signs for Hindi Language 2

Recognition of Hand Signs for Hindi Language

引用

2nd IEEE international conference on Advances in Information technology, ICAIT 2024

作者： Hande, Atharva Nitin Karn, Sreejan Mukesh Pulgam, Namita Department of Computer Engineering Ramrao Adik Institute of Technology D Y Patil deemed to be University Maharashtra Navi Mumbai India

ISBN: (纸本)9798350383867

With a sign recognition system in place, the hearing challenged will be able to be express all their emotions and ideas easily. Offering a unique way to sign language detection through machine learning algorithms, this paper uses the dataset of Hindi sign language a sits subject of study. These ten compasses a variety of sign languages to which people signal typical words as well as the Hindi alphabet. For the proper characterization of these movements, a deep learning model is offered constructed on Convolutional Neural Networks (CNNs). The CNN model achieves high accuracy rates more than 90% on the test set, provided that it is trained using a small subset of the dataset. Also, this project investigates the use of data augmentation for introducing variations in hand positions and lighting conditions in order top reduce the model able to operate in this sort of conditions. The results thus testify about the feasibility and exclusivity of the method designed to detect and identify Hindi sign language gestures with success, thereupon might be helpful in practical applications that aid communications for those with hearing impairment. ©2024 IEEE.

关键词： Convolutional neural networks

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Research on River Floating Objects Detection Method Based on Deep Learning 2

Research on River Floating Objects Detection Method Based on...

引用

2nd international conference on computer Vision and Intelligent technology

作者： Xu, Jiaqi Zhao, Yining Cao, Yuqi Hou, Dibo Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou Peoples R China

ISBN: (纸本)9798331540050;9798331540043

The environmental issues have become increasingly prominent, and water environments have remained a major societal concern. Surface pollutants in rivers are one of the key factors damaging water environments. Given the large number and wide distribution of rivers, manual river inspections and the collection of floating objects are time-consuming and labor-intensive. In this study, a river surface floating objects detection method based on deep learning is proposed. To address the noise issues caused by interferences in natural scenes, an image preprocessing method based on median filtering and Laplace operator is studied. In order to select the most suitable deep learning algorithm, this work compares the YOLOv5, Faster-RCNN, and SSD algorithms. After comparative analysis, an optimized YOLOv5 model is proposed for detecting floating objects on river surfaces. Finally, a visualization platform is developed based on PyQt5, enabling real-time monitoring and display of floating objects in river surveillance videos. Experimental results demonstrate that this method can effectively detect floating objects on river surfaces, with recall rate of 0.741 for artificial trash, mAP@0.5 of 0.798, and mAP@0.5:0.95 of 0.704. This system achieves real-time surface floating objects monitoring in rivers and open channels, providing crucial information for the protection and management of water environments.

关键词： object detection river floating object deep learning real-time detection

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Research on voice recognition and control technology for the elderly at home 2

Research on voice recognition and control technology for the...

引用

2nd international conference on Signal Processing, computer Networks and communications, SPCNC 2023

作者： Xu, Hailong Hebei University of Engineering China

ISBN: (纸本)9798400716430

This article addresses the issue of elderly people living alone in China and proposes the use of voice recognition technology to facilitate their daily lives. This system first collects sound, then extracts the MFCC parameters of the collected sound, compares the MFCC parameters of the collected sound with the MFCC parameters of the reference sound, The use of pre emphasis technology in the preprocessing stage of speech signals makes the recognized sound more recognizable. In the recognition stage, a large number of reference samples are used instead of a single reference sample, which improves the success rate of recognition, and finally uses the DTW (Dynamic Time Warping) algorithm for speech recognition. © 2023 ACM.

关键词： Speech recognition

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Increasing Emergency Response Time: A Smart GPS and IoT-Based Approach for Ambulance Optimization 2

Increasing Emergency Response Time: A Smart GPS and IoT-Base...

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2nd IEEE international conference on Advances in Information technology, ICAIT 2024

作者： Prajwal, V. Rahul, I.V. Sai Adithya, A. Chayadevi, M.L. Computer Science and Engineering BNMIT Bangalore India HOD Computer Science and Engineering BNMIT Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

This paper proposes a novel approach to enhance ambulance response times and streamline patient transportation during emergencies by leveraging a smart GPS-based system integrated with Internet of Things (IoT) technology at traffic signals. Traditional methods, relying on camera detection or manual traffic clearance, often lead to delays in granting priority to ambulances. In contrast, our system pre-empts traffic signals in real-time, ensuring swift passage for ambulances by communicating with IoT devices installed at traffic control boxes. The proposed system optimizes ambulance routes using real time traffic data and machine learning algorithms, dynamically adjusting paths to avoid congestion. Components include a smart GPS navigation system, IoT devices at traffic signals, robust communication protocols, and centralized control systems. In summary what makes this design effective is its ability to prioritize clearing intersections ahead of emergency vehicles thus reducing time spent on responding to medical calls while at same time giving hope for better outcomes among patients. © 2024 IEEE.

关键词： Traffic signals

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Research on Gastritis Endoscopic Images Classification Based on SE-RepLKNet 2

Research on Gastritis Endoscopic Images Classification Based...

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2nd international conference on computer Vision and Intelligent technology

作者： Jin, Xinao Zhao, Yining Cao, Yuqi Zhang, Gangzhi Zhang, Guangxin Han, Yuehua Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med Dept Gastroenterol Hangzhou Peoples R China

ISBN: (纸本)9798331540050;9798331540043

Autoimmune gastritis (AIG) is an atrophic gastritis caused by abnormal autoimmune function. Recent studies indicate an increasing prevalence of AIG in China. In advanced stages, AIG can progress to neuroendocrine tumors and gastric adenocarcinoma, posing a life-threatening risk to patients. However, due to the insufficient clinical awareness and nonspecific clinical manifestations of the disease, AIG has a low diagnostic rate and is frequently misdiagnosed as type B gastritis caused by Helicobacter pylori infection, which leads to delayed diagnosis. Gastroscopy serves as a critical tool for diagnosing AIG based on distinct gastroscopic features of AIG and type B gastritis. This study proposes a gastritis classification and diagnostic method based on gastroscopic images using a deep learning model, RepLKNet, with large-kernel convolution. Additionally, a channel attention mechanism, the SE module, is integrated to enhance the model's feature extraction capability. Considering the scarcity of publicly available medical image datasets for pretraining, a two-stage pretraining strategy is adopted to incrementally train the model for extracting hierarchical features. Experimental results demonstrate a classification accuracy of 89.6% on gastroscopic images. This system provides clinicians with more accurate classification suggestions for two frequently misdiagnosed types of gastritis, offering crucial information for subsequent treatment and examination.

关键词： deep learning autoimmune gastritis image classification channel attention mechanism large kernel convolution

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