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共 31245 条记录，以下是821-830 订阅

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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions 1

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Aishwarya, D. Sandeep, Ponduru Akhilesh Varma, Penmatsa V.P.S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371802

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary Patterns, Completed Local Binary Patterns. Local Ternary Patterns, and Dynamic Local Ternary Patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets. © 2024 IEEE.

关键词： Local binary pattern

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Multi-Trajectory Drone Surveillance: Amplifying Monitoring Capabilities in Disaster Management

Multi-Trajectory Drone Surveillance: Amplifying Monitoring C...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Mourya, Gojja Sree Jami, Abhinav Ajith, Abhinav Seikh, Ayan Habib Kumar, J. Sathish Motilal Nehru National Institute of Technology Dept. of Computer Science and Engineering Allahabad India

ISBN: (纸本)9798350348460

In the disaster-affected area, whether it's a search and rescue operation, mock drill, managing food distribution, issuing warnings or anything else, the key to successfully carrying out these activities is preparation. To ensure that plans are accurate, one needs to know what actually happened in the disaster-affected area and for this purpose thoroughly and quickly the entire area need to be studied. To address this issue, we use UAV or drone navigation autonomously in required trajectories. By using UAVs you can cover surveillance across the vast acres of land quickly and know what happened and plan rescue operations accordingly. In this paper, we presented our work on moving the drones in multiple trajectories and swarm, thus amplifying surveillance capabilities. Each trajectory is maneuvered autonomously and a corresponding trajectory-algorithm running at the back end is presented along with the simulation results. © 2024 IEEE.

关键词： Disaster management Drones Multi-Agent Systems Surveillance Swarm Trajectories Unmanned Aerial Vehicles (UAV)

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Local Closeness Gravity Model to Identify the Vital Nodes in Complex Networks 16

Local Closeness Gravity Model to Identify the Vital Nodes in...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Nandi, Suman Dutta, Animesh National Institute of Technology Durgapur Dept. of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350383119

In complex network analysis, identifying the viral nodes is a major concern of the research domain by which any kind of information or infection is controlled throughout the entire network. Several algorithms have been developed over the past few years to identify the viral nodes (influential spreaders) considering many properties of the network. Among them, some authors proposed gravity-based centrality to identify the vital nodes based on the law of gravity with certain limitations. The major limitation of existing gravity-based methods is the mass of the object (i.e. node) is considered as the degree or kshell index only, which does not always signify the spreading ability of the nodes. To address this research challenge, we propose an innovative Local Closeness Gravity method (named LCG) to measure the influential ability of individual nodes, facilitating the identification of the vital nodes in the network. To minimize the computational complexity of Closeness centrality, at first, we measure the local Closeness centrality of individual nodes considering all the nodes residing in the truncation radius. Thereafter we introduce a new parameter 'information sharing ability' based on connectivity strength to measure the distance between the nodes. Finally, the influential ability of each node is measured based on the gravity model considering the local closeness centrality, kshell index, and the distance. The efficiency of LCG is compared with the existing baseline centrality methods by using the Susceptible-Infectious-Recovered (SIR) simulator. The correlation between the LCG method and the baseline centrality methods with the SIR method is compared by Kendalls' tau method considering various infection probabilities and various percentages of seed nodes respectively. The ranking uniqueness of the LCG method and the baseline centrality methods are also measured by the monotonicity metrics. Through the obtained results and various analyses, it becomes evident that t

关键词： Complex Networks information sharing ability Local Closeness centrality SIR epidemic simulator Vital nodes

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An Effective Way to Detect Phishing Website Using Machine Learning 6

An Effective Way to Detect Phishing Website Using Machine Le...

引用

6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Toyeer-E-Ferdoush Shorab, Mst. Sumaiya Ritu, Saima Akter International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

In today's interconnected digital landscape, phishing represents a significant threat to both individuals and organizations. Phishers cleverly utilize deceptive techniques to extract sensitive information from unsuspecting users, posing a risk to critical account details. Despite various proposed methods to detect phishing websites, the adaptability of phishers continually challenges the efficacy of existing approaches. Recognizing the vital role of Machine Learning (ML) in combating these evolving threats, this research investigates the application of ML techniques. Common characteristics in phishing attacks can be identified through accurate ML analysis. This research employs a range of ML models and transformer models, including Catboost (Cat), XGBoost (XGB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), TabNet, K-nearest neighbor (KNN), and Logistic Regression (LR) with grid-search. Among these models, the Catboost classifier achieved a remarkable accuracy of 97%. This study shows dedication to utilizing diverse methodologies to advance the field of phishing website detection. © 2024 IEEE.

关键词： Decision trees

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Enhancing Decentralized Finance for Scalability, Interoperability, and User Experience 8

Enhancing Decentralized Finance for Scalability, Interoperab...

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8th IEEE International Conference on Computational System and information technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rao, Vishak Rao, Karthik Shobha, P. Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

This research explores Decentralized Finance (DeFi) through the lens of enhanced applications, introducing the concept of Decentralized Finance Enhanced (DeFine). It examines the transformative potential of DeFi, analyzing its evolution and the innovative solutions driving its progress. The objective is to investigate how DeFine addresses scalability, interoperability, security, and user experience challenges in the DeFi ecosystem. Through case studies and analysis, this proposed work provides insights into the implications, opportunities, and risks associated with the adoption and maturation of DeFi enhancements. © 2024 IEEE.

关键词： Decentralized finance

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Fuzzy Based Intelligent Transportation Systems for Smart Cities to Mitigate Road Traffic Congestion

Fuzzy Based Intelligent Transportation Systems for Smart Cit...

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2024 International Conference on Innovations in Science, engineering and technology, ICISET 2024

作者： Kabir, Md. Humayun Islam, Md. Saiful Hoque, Md. Jiabul Chittagong University of Engineering and Technology Dept. of Electronics and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350355499

Traffic congestion in smart cities is a growing concern. This paper explores the application of Fuzzy Logic in Intelligent Transportation Systems (ITS) to address this challenge. Fuzzy logic can handle imprecise and dynamic traffic data and offers advantages over traditional fixed-time signal control. The research proposes a Fuzzy-based ITS framework for dynamically traffic signal control. This framework utilizes real-time sensor traffic data to adjust signal timings, dynamically prioritizing congested areas. The system also incorporates emergency vehicle detection using siren sensors. This ensures priority for emergency vehicles by granting them the right of way. This paper discusses the design of the fuzzy inference system, including the selection of fuzzy variables and membership functions. The effectiveness of the proposed system is evaluated through simulations, demonstrating its potential to reduce traffic congestion and improve traffic flow in smart cities. © 2024 IEEE.

关键词： Traffic congestion

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Nannofossil Image Enhancement for Improved Detection Using Modified Contrast Stretching Method and YOLO-v8 16

Nannofossil Image Enhancement for Improved Detection Using M...

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16th International Conference on information technology and Electrical engineering, ICITEE 2024

作者： Dyah Kurniasari, Andi Shafira Fauziati, Silmi Hartanto, Rudy Akmaluddin, Akmaluddin University of Gajah Mada Faculty of Engineering Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia University of Gajah Mada Faculty of Engineering Dept. of Geological Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350375817

This research focuses on enhancing the quality of nannofossil images, specifically the discoaster and coccolith genus, to improve the performance of detection and prediction models using advanced deep learning techniques. The study employs Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for image resolution enhancement and a modified mean-standard deviation contrast stretching method for contrast enhancement. The You Only Look Once version 8 (YOLOv8) algorithm is then used to detect enhanced images. Evaluation metrics, including precision and recall, were used to assess the impact of image enhancement on detection performance. Results indicate that the modified mean-standard deviation contrast stretching method significantly improves image clarity and object detectability, achieving a precision of 0.592 and a recall of 0.211. However, these results are still lacking to be considered as good result for object detection. Thus, further improvement is required to analyze the data. This research is expected to make significant contributions to the study of paleoclimatology and the development of deep learning-based object detection methods in the future. © 2024 IEEE.

关键词： Generative adversarial networks

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A Novel Dual Channel Isolated Current Source Gate Driver for Fast Switching MOSFETs 50

A Novel Dual Channel Isolated Current Source Gate Driver for...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Elahi, Asjad Youssef, Mohamed Dept. of Electrical Computer and Software Engineering University of Ontario Institute of Technology OshawaON Canada

ISBN: (纸本)9781665464543

A new dual-channel discontinuous mode isolated current source gate driver (ICSGD) is proposed in this paper. The proposed gate driver can provide two isolated gate drive signals for two power MOSFETs in circuits where these switches are in sync. The proposed gate driver can achieve very fast turn on and turn off transitions, due to its current mode nature. In addition, this technique can achieve partial gate energy recovery by recovering a fraction of the RMS (CV2) energy, which is typically dissipated over the gate drive resistors in voltage source gate drive (VSGD) techniques. Thus, provides better efficiency. The proposed circuit consists of six transistors and a small pulse transformer to allow swift operation over a wide range of duty cycles. Current through the transformer is discontinuous to avoid conduction losses due to circulating currents such as in some resonant gate drivers (RGD). Consequently, the proposed ICSGD provides superior performance over its VSGD counterparts, especially in high-frequency (HF) operation. The design is implemented in both PSIM and Typhoon HIL (hardware in the loop) platforms. The model is verified with a match of results from PSIM and Typhoon. The PSIM & HIL results show that the ICSGD achieves a minimum of 20% efficiency improvement. © 2024 IEEE.

关键词： High frequency transformers

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Enhancing Athletic Performance:2D Human Pose Estimation Using Deep Neural Networks for Movement Analysis 7

Enhancing Athletic Performance:2D Human Pose Estimation Usin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Devi, Pothreddypally Jhansi Sumani, Aleshwaram Chandra, Chenna Mukesh Thrisha, Battu Bhanu Vamshi, Arkatala Sai B V Raju Institute of Technology Dept. of Computer Science Engineering Telangana India

ISBN: (纸本)9798350372816

computer vision is a field that is used to build many neural network-based prototypes efficiently. It analyses images and videos effectively and processes them digitally. Human Pose estimation (HPE) is one of the computer vision applications which additionally uses neural networks for prediction. Human pose estimation is used widely in many fields which includes action recognition, fitness, gaming, and motion capture. HPE identifies and locates the key points on the body joints of a person's body. These body points are connected to form a Skeletal Structure. This skeletal representation helps to easily visualize the image in 2-D. Focusing on Sports domain, HPE plays major role in estimating athlete movements. It helps in analyzing the feedback on the posture which allows to make adjustments during training. It also helps viewers to analyse the insights of athlete performance. To achieve the model that contains all these features, requires involvement of few frameworks. OpenPose is utilised for fast processing of videos and can detect the multiple people in one frame. OpenPose has the capability of handling the occulsion. On the Other hand, the model also import the capabilities of HRNet, which is used for analyzing athlete movements and tracks the gestures. After detecting the keypoints on the body joints, these points must be integrated to form 2-D images. To achieve the spatial relationship between the bodypoints Joint Graph Convolutional Neural Networks (JGCN) framework is used. The proposed model is estimated to generate approximately (4- 5)percent more efficiency and accuracy in its output 2-D image compared with the previous models. © 2024 IEEE.

关键词： Convolutional neural networks

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AI Sentries: Evaluating Machine Learning Models for Superior Phishing Email Detection 8

AI Sentries: Evaluating Machine Learning Models for Superior...

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8th IEEE International Conference on Computational System and information technology for Sustainable Solutions, CSITSS 2024

作者： Sahit, S. Vaishnavi, S. Vaibhav Reddy, A. Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

Email Phishing Detection focuses on studying how to process emails and spot threats using machine learning and advanced natural language processing. It aims to shield people and businesses from real email misuse. This study tries to fix some problems that earlier research had faced. It does this by making better machine learning detectors to spot fake emails. These detectors use different data sets and look at how to pull out key info. They also try to adapt to new tricks scammers use. The solutions put forward use new methods. These include tweaking features, combining different learning models, and using deep learning to boost accuracy while checking emails in real-time. The study also looks at how to use natural language processing (NLP) to understand what emails mean. This helps catch more fake emails. By looking at both how emails are written and what they mean, the system can spot scams more. This cuts down on false alarms and makes the system more reliable. This well-rounded approach not pushes the boundaries of spotting fake emails but also creates a strong system that can change as cyber threats do. This ensures protection that lasts and can bounce back from attacks. © 2024 IEEE.

关键词： Phishing

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