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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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An Optimal DCP-Based Defogging Method of Improving Objects detection in Adverse Weather

An Optimal DCP-Based Defogging Method of Improving Objects D...

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Networking systems of AI (INSAI), international conference on

作者： Xinyu Qian Peng Sun Duke Kunshan University Duke University Duke Kunshan University

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

As one of the most important components of practical autonomous driving technology, designing efficient and reliable object detection methods has been the focus of attention in academic and industrial circles for recent years. Fortunately, by taking advantage of the development of deep learning technology, various traffic-related object detection methods based on deep networks have been designed using different feature extraction methods, such as convolutional neural networks (CNNs). However, existing approaches often focus on target detection in fine weather conditions and ignore the negative impact of bad weather on target detection performance. Therefore, in this article, we analyze the problem of target recognition in rainy and foggy weather, and do survey of existing defogging methods. Accordingly, we propose our Dark Channel Prior (DCP)-based image-defogging method to improve the detection efficiency of low-visibility targets in rainy and foggy weather. In addition, we discuss the two most commonly used data sets and how they are generated. Then, by considering the experimental results and the practical application scenarios of autonomous driving techniques, we further discuss the open challenges of designing a practical defogging method for supporting autonomous deriving.

关键词： Deep learning Histograms Target recognition Atmospheric modeling Clustering algorithms Object detection Convolutional neural networks Artificial intelligence Autonomous vehicles Meteorology

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A Comparative Study of Prediction of Pneumonia and COVID-19 Using Deep Neural Networks

A Comparative Study of Prediction of Pneumonia and COVID-19 ...

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Electronic and Electrical Engineering and Intelligent system (ICE3IS), international conference on

作者： Md Sabbirul Haque Md Siam Taluckder Siam Bin Shawkat Md Ahnaf Shahriyar Md Abu Sayed Chinmoy Modak IEEE Professional Community IEEE Phillip M. Drayer Department of Electrical Engineering Lamar University Computer Science Department American International University Bangladesh College of Technology and Engineering Westcliff University Irvine CA Department of Professional Security Studies New Jersey City University Jersey City New Jersey Department of Computer Science Florida Polytechnic University Lakeland Florida

Pneumonia, a contagious respiratory infection caused by both bacteria and viruses, carries significant global implications. Rapid identification of infected individuals is paramount to effectively curb its transmission. This study explores the application of medical image analysis to address this challenge by proposing machine learning techniques for pneumonia prediction using chest X-ray images. Chest X-ray imaging, known for its accessibility and cost-effectiveness, plays a pivotal role in pneumonia diagnosis. However, the interpretation of X-rays for pneumonia detection can be intricate due to potential overlaps with radiographic features of other respiratory conditions. In this research, various machine learning models, including DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and Xception, are evaluated and compared using chest X-ray images from pneumonia patients. Performance metrics and confusion matrices are employed to assess and differentiate these models. The findings highlight DenseNet121 as the top-performing model, achieving an impressive accuracy rate of 99.58%. This study underscores the significance of machine learning in accurately detecting pneumonia through the analysis of chest X-ray images. It offers valuable insights into the potential of technology to mitigate pneumonia's spread by facilitating precise diagnostic tools.

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Comparative Evaluation of Exploratory Data Analysis Techniques for Power Theft detection in Residential Distribution Grids

Comparative Evaluation of Exploratory Data Analysis Techniqu...

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Smart Technologies for Power, Energy and Control (STPEC), IEEE international conference on

作者： Swati Agarwal Ayush Srivastava Ranjana Sodhi Tukesh Soni Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar India Dept. of Mechanical Engineering UIET Punjab University Chandigarh India

This paper presents a comparative evaluation of the performance of various Exploratory Data Analysis (EDA) techniques for detecting power theft in residential distribution networks. The techniques include Pearson Correlation Coefficient (PCC), Fuzzy C-Means Clustering (FCM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). They are assessed using well-known classification assessment measures such as Accuracy, Geometric Truth Rate, Youden’s Index and Area under Receiver operating characteristics curve. In the present work, power theft scenarios are divided into four different categories. Suspicious consumers are then identified using the concepts of feature extraction, correlation, and clustering. The information needed to detect the corrupt customers comes from a monitoring system at the distribution transformer for a block and electricity load data from smart meters installed at customer homes in the same block. The analysis is tested on actual dataset from UK power networks and it is found that FCM outperforms the other techniques. Sensitivity analysis is further performed to determine how the technique performs significantly w.r.t. various parameters.

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Verification Method of Intelligent Prosthetic Algorithm Based on Anthropomorphic Walking Robot

Verification Method of Intelligent Prosthetic Algorithm Base...

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Robotics, Automation and Artificial intelligence (RAAI), IEEE international conference on

作者： Honglei An Lei Sun Hongxu Ma Qing Wei College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

A key issue in the research of intelligent prosthetics is the verification and analysis of the proposed prosthetic motion planning and control methods. In this paper, it is proposed that using a humanoid walking robot as the subject can effectively test the interaction dynamics between the prosthesis and the human body. This method also avoids the risk of injury to amputee patients when participating in the test before the control method is mature. Specifically, the anthropomorphic walking of bipedal walking robots is first realized, and a testbed that fully simulates the straight leg walking is obtained. The right leg (below the thigh) of the bipedal robot is then replaced with an intelligent prosthesis. When testing the planning and control methods of intelligent prosthetics, prosthetic controllers run independently. The bipedal walking robot, except for the part below the right thigh, normally performs the “humanoid motion controller”. Effective intelligent prosthetic control methods will make the prosthesis-“human” coupling system produce smooth walking movements, while ineffective intelligent prosthetic control will cause the coupling system to fall due to strong interference.

关键词： Legged locomotion Couplings Performance evaluation Motion planning Trajectory tracking Thigh Humanoid robots

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Secure Data Reduplication system with Efficient and Reliable Multi-Key Management in Cloud Storage

Secure Data Reduplication System with Efficient and Reliable...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： B Jayaram T. Suguna Shaik Fairooz Arul Selvam P S. Vimal Department of Computer Science and Business Systems RMK Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of CSE -AIML Malla Reddy Engineering College Secunderabad Telangana India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

Malicious hackers may compromise hundreds or millions of machines with little effort, allowing them to shut down networks, steal sensitive data, and compromise critical infrastructure. Data is stolen and used for malicious purposes by hackers who exploit security flaws in the network. And the sophisticated technology they use, like the bucks they do, are continually feeding them the information they needed, not just once. Moreover, even if Internet firewalls have improved, cybercrime has been on the rise. Then, we have a long way to go before we approach the limit, what with all the different kinds of cloud server devices that can connect to the internet nowadays. It's certain that in the future, little devices, no matter how helpful they are really in our everyday lives, will become the preferred tool of villains. In this research, we offer a novel security technique for protecting sensitive data on cloud servers. This intelligent cloud server was protected from any hacking with the aid of this algorithm. The system's output was measured against those of pre-existing algorithms. More than 96% of inputs are authenticated by the proposed technique, while password cracking operations are reduced by about 3%. The source code security management is improved to 99% and the updating issues for various devices entries is reduced 0.02%, and further the privacy breach is reduced up to 0.32%. Hence the proposed method was more secured from cyber security issues.

关键词： Cloud computing Computer hacking Source coding Software algorithms Passwords Software Virtual private networks

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Understanding the Limitations of Secure Shell (SSH) in Wireless Network Security

Understanding the Limitations of Secure Shell (SSH) in Wirel...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Amit Upadhyay Sandip Kulkarni Sharmistha Roy K. Yuvaraj V. Ashok Sunil D. Kale Department of Computer Science Mangalayatan University Aligarh India Department of Computer Science Himalayan University Itanagar India Department of Coputing & Information Technology Usha Martin University Ranchi India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

comfy Shell (SSH) is an effective community protocol used to securely connect devices over a computer community. It gives information encryption for relaxed conversation among clients and servers, and can be used to guard information at the same time as being transmitted over a wi-fi community. But, as with every protocol, there are positive obstacles that should be considered when the usage of SSH to relaxed wi-fi networks. Because of the character of wireless networks, in which records is transmitted through the air, SSH is at risk of numerous vulnerabilities. First, SSH is susceptible to man-in-the- center attacks, which can permit an attacker to intercept the network traffic and examine, alter, or block positive packets. Specially, using SSH makes it viable for an attacker to perform password sniffing, for the reason that protocol does not encrypt the user's login credentials. Additionally, the usage of SSH protocols does now not shield against malware or different malicious software, making it feasible for an attacker to monitor or manipulate an affected system.

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A Complex Road Target detection Algorithm Based on YOLOv5n-SBF 23

A Complex Road Target Detection Algorithm Based on YOLOv5n-S...

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Proceedings of the 2023 3rd international conference on Big Data, Artificial intelligence and Risk Management

作者： Guoyang Lin Huarong Xu School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9798400716478

Missed detection is usually caused by variations in target scales, dense occlusion, and uneven illumination. To tackle the challenge of missed detection in road target detection during complex environments, an enhanced approach called YOLOv5n-SBF based on YOLOv5n has been proposed. Specifically, YOLOv5n-SBF replaces the backbone network of YOLOv5n with Swin Transformer structure and fuses features extracted from Swin Transformer Block and neck network, effectively solving the problem of missing detection and improving the ability to extract features from small targets. Furthermore, we add the BiFormer attention mechanism to the neck of YOLOv5n to capture global and local features, improving the model's performance in visual tasks. Extensive experiments have been conducted on two real-world datasets. The experimental results demonstrate that YOLOv5n-SBF exhibits superior detection efficiency compared to baseline models, and achieves 5.7 percentage points and 18.2 percentage points in [email protected] scores on the public datasets of KITTI and UA-DETRAC, respectively.

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Identification of Fingerprint Orientation Using Improved Generative Adversarial Network with Support Vector Machine

Identification of Fingerprint Orientation Using Improved Gen...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Abbas Hameed Abdul Hussein Gobinath Ravindran Zamen Latef naser Muntather Almusawi Santhiya K College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Civil Engineering SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq The Islamic university Najaf Iraq Department of Artificial intelligence and data science PSG Institute of Technology and Applied Research Coimbatore India

Fingerprint authentication is one of the methods used to prevent the loss of personal data, but the minutiae and angle variance of finger patterns make the process difficult. To overcome the issue, an authentication system for fingerprint orientation using an improved Generative Adversarial Network (GAN) with Support Vector Machine (SVM) is being developed. The process begins with image enhancement using Contrastive Limited Adaptive Histogram Equalization (CLAHE) techniques to increase image contrast. Next, GAN with SVM is employed to generate synthetic data through data augmentation, and decision-making is carried out using SVM. Both real and synthetic samples are binarized using the global thresholding technique, and edge-based thinning methods are applied to enhance the fingerprint patterns. Finally, features such as minutiae points of fingerprints are extracted using the Scale Invariant Feature Transform (SIFT) algorithm, serving as input for the SVM classifier. The implemented GAN-SVM model demonstrates superior performance, achieving a False Acceptance Rate (FAR) of 0.12%, a False Rejection Rate (FRR) of 1.3%, an Equal Error Rate (EER) of 0.29%, and an accuracy of 95.87%. When compared to previous models like Multi-layer Perceptron Neural Network (MLP), Fuzzy Commitment (FC), and Genetic Encryption Algorithm (GEA).

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An IoT and Information Mining Algorithm-Based Self-Monitoring and Analysis system for Solar Power Plants

An IoT and Information Mining Algorithm-Based Self-Monitorin...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ali Hadi Abdulwahid Kruti Jani Navdeep Dhaliwal Rajesh Singh A. Bhasha Jayapal Lande Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq Shri Chimanbhai Patel Post Graduate Institute of Computer Application Ahmedabad Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University India Dept of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

Because of their affordability and sustainability, renewable sources of energy are receiving a lot of study interest. Particularly, solar power plants are regarded as a type of renewable sources of energy that may be employed in many areas since, while requiring less space than traditional systems, they demand less money for installation and maintenance. The majority of small generating units control space occupancy by putting the machinery on an exposed terrace. However, acres of land is needed for the building of huge power producing stations. It is difficult for human workers to maintain such a huge region of a power plant. The suggested algorithm would assist human workers in identifying the consistency of energy production and failure or faulty areas in solar energy systems using IoT and database mining (DM) techniques. This enables immediate fault repair action to be taken, which increases producing station effectiveness.

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A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time

A Scalable Platform to Collect, Store, Visualize and Analyze...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： Pravin Ramdas Kshirsagar Dhoma Harshavardhan Reddy Mallika Dhingra Dharmesh Dhabliya Ankur Gupta Department of Artificial Intelligence S B Jain College of Engineering and Technology Nagpur Maharashtra India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Mumbai Maharashtra India Department of Mathematics and Statistics Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India

Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. By using the Programming interface key, Twitter data for this article was collected from Twitter. Similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection.

关键词： Social networking (online) Blogs Multimedia Web sites Organizations Footwear Programming Reflection

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