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共 113 条记录，以下是11-20 订阅

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An Effective Classification of Ddos Cloud Based Attack Through Tree Founded Classifiers 1

An Effective Classification of Ddos Cloud Based Attack Throu...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Goel, Vikas Goel, Pragati Ranjan, Raju Sharma, Amit Kumar KIET Group of Institutions Delhi-NCR Dept. of IT Ghaziabad India NCRD'S Sterling Institute of Management Studies Nerul Dept. of Computer Application Navi Mumbai India School of Computing Science & Engineering Galgotia University Greater Noida India Ch. Charan Singh University Campus Dept. of CS&E Meerut India

ISBN: (纸本)9798350345957

The term 'cloud computing' refers to a relatively new technology that provides users with access to various computing services and resources, including software, data storage, and communication infrastructure. There are currently certain security concerns with cloud computing. An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DoS attacks, which aim to disrupt, disable, or otherwise harm a service, have become increasingly common against cloud applications and internet services. Due to the cloud's complexity and decentralized nature, it is difficult to detect attacks on its infrastructures. Moreover, by allowing a wide variety of smart devices to access cloud-based infrastructures, it raises both the difficulty and the complexity of assaults. A decision tree is a method of categorization used in data mining. It is recommended to utilize this method when developing a classification scheme from a reclassified data set. Classifiers such as the Classification and Regression Tree (CART), C4.5, and the Random Tree (RT) are proposed in this work. The utilized CART method has a tendency to generate additional trees on nodes, until no tree can be formed judging whether or not a node is terminal. Because of its adaptability, the CART technique can handle gaps in the data. The tree is pruned to make it smaller and more manageable in C4.5. A classifier's complexity is decreased while its prediction accuracy is improved. Decision trees were used to generate the chain of basic decisions used in RT's label assignment process. © 2023 IEEE.

关键词： Decision trees

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Analysis of Bones Abnormality Detection and Segmentation 11

Analysis of Bones Abnormality Detection and Segmentation

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11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Ismail, Mennat-Allah Mohammed, Yasmine Gamal, Nadine Hatem, Maram Ibrahim, Sara Abbas, Esraa Hamdi Mohsen, Mayar Nasser, Maram Ebied, Hala M. Ain Shams University Faculty of Computer and Information Sciences Dept. of Bioinformatics Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Dept. Bioinformatics Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information System Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

Numerous musculoskeletal disorders can be caused by bone diseases, which are widespread. Musculoskeletal conditions are estimated to affect 1.71 billion people worldwide. In addition to musculoskeletal fractures, knee osteoarthritis, fractures, and femoral neck injuries are all relatively prevalent bone illnesses. Missed fractures, on the other hand, are a frequent prognostic failure in accidents and emergencies. As a result, patients' treatment and care are complicated and delayed. Deep Learning (DL) and Artificial Intelligence (AI) are currently garnering a lot of interest for their potential to help physicians identify bone fractures. Studies in traumatology and orthopedics have demonstrated the utility and potential of DL in the radiograph-based diagnosis of fractures and illnesses. In this paper, different classification techniques have been compared to classify the bone. The methods used for detection and segmentation of wrist bones are Inception-v3, VGG16, Densenet-121, and Resnet-50while for elbow bones, Resnet-50, Inception-v3, Densenet-121, and Densenet-169 are used. Two datasets are used in the experiments.: Kaggle dataset and MURA dataset. The experimental results show that the Densenet-169 and Inception-v3 performed better than the other models using MURA dataset and Kaggle dataset respectively. © 2023 IEEE.

关键词： Medical imaging

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Examining driver sleepiness based on his visual actions with ML Techniques

Examining driver sleepiness based on his visual actions with...

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Electrical Electronics and computing Technologies (ICEECT), International Conference on

作者： Vandana Rawat Kaushal Patwa Harendra Singh Negi Anupriya Sharma Ghai Dept. Of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Dept. Of Computer Application Graphic Era Deemed to be University Dehradun India Dept. Of School of Computing Graphic Era Hill University Dehradun India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Driver drowsiness is a significant risk factor for road accidents. It is essential for road safety to develop efficient sleepiness detection systems. This work concentrate around analyzing driver sluggishness in view of visual activities, expecting to recognize solid marks of To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are drowsiness utilizing PC vision methods. To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are extracted from real-time video footage captured by an onboard camera using a combination of image and video processing algorithms. To prepare and assess the sluggishness location model, a different dataset of drivers in different conditions of languor is gathered, including both reproduced and genuine situations. The dataset is clarified with ground truth marks showing various degrees of languor, got through physiological estimations, driver self-reports, and master assessments.

关键词： Visualization Head Sleep Eyelids Streaming media Feature extraction Magnetic heads Time measurement Artificial intelligence Vehicles

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A Design of Software Architecture for Validate and Verification of Linear-Optical Quantum computing Platform

A Design of Software Architecture for Validate and Verificat...

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Quantum computing and Engineering (QCE), IEEE International Conference on

作者： Duwon Lee Yong Kwon Kanghyeon Kim Jaehyeok Lee Byung-Soo Choi Dept. of Physics Pukyong National University Dept. of Computer Engineering Pukyong National University Dept. of Scientific Computing Pukyong National University

Compared to other semiconductor quantum computing platform, it is relatively untouched about the verification of linear-optical quantum computing (LOQC) platform. To overcome this problem, we propose a universal and versatile software platform which can support any variation of LOQC layout structure. It also supports the user-friendly interface for smooth experiment.

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A Comprehensive Framework for Unmanned Aerial Vehicle (UAV)-Enabled Real-Time Human Detection System for Disaster Management

A Comprehensive Framework for Unmanned Aerial Vehicle (UAV)-...

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2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Upadhyay, Govind Murari Joshi, Mukesh Rathi, Preeti Vats, Prashant Narula, Meenakshi Gupta, Shashi Kant Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Graphic Era Hill University School of Computing Uttarakhand India K.R. Mangalam University Dept. of CSE School of Engg.and Techn. Haryana Gurugram India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India Jagannath International Management School Department of Information Technology New Delhi India ITM University Gwalior Department of CSE Madhya Pradesh India

ISBN: (纸本)9798350378092

Disasters pose significant challenges to emergency response efforts, often hindering timely and effective interventions. In recent years, integrating Unmanned Aerial Vehicles (UAVs) has emerged as a promising approach to enhance disaster management capabilities. This paper presents a novel real-time framework leveraging UAV technology for disaster response. The framework encompasses UAV deployment strategies, real-time data acquisition through aerial imaging and remote sensing, robust data transmission using advanced communication infrastructure, automated AI and machine learning analysis, and dynamic resource coordination based on actionable insights. By integrating these elements, the proposed framework aims to improve situational awareness, optimize response efforts, and facilitate informed decision-making during disaster scenarios. This paper discusses the key components of the framework, highlighting its potential impact on enhancing disaster response capabilities in real-time scenarios. © 2024 IEEE.

关键词： Decision making

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Looking into the Black Box: Monitoring computer Architecture Simulations in Real-Time with AkitaRTM

Looking into the Black Box: Monitoring Computer Architecture...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Ali Mosallaei Katherine E. Isaacs Yifan Sun Electrical and Computer Engineering Dept. University of Michigan Ann Arbor USA Scientific Computing and Imaging Institute University of Utah Salt Lake City USA Dept. of Computer Science William & Mary Williamsburg USA

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

computer architecture simulators are essential for validating novel chip designs. However, they often provide little transparency during execution. This opaqueness limits the ability of users to identify issues during the simulation, leading to both wasted computational and human time. We address these issues by providing an intuitive user experience during simulation execution. Particularly, we reveal the status of executions and allow users to control the execution of a computer architecture simulator through AkitaRTM, an interactive web-based tool for real-time monitoring of computer architecture simulations. We based its design on the design workflow inefficiencies experienced by computer architects when using simulations. We demonstrate AkitaRTM's utility through two case studies, the second leading to a patch in the simulator. Additionally, we conducted a user study with computer architects, aiming to validate AkitaRTM. We found that, in addition to solving the observed problems, AkitaRTM also provided an educational benefit by making simulators more transparent to users. Based on these findings, we reflect upon the design of AkitaRTM and provide guidance for future human-centered tools in this space.

关键词： Human computer interaction Microarchitecture computer architecture Microcomputers Real-time systems User experience Hardware Usability Monitoring Interoperability

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A Comprehensive Analysis of Advanced Change Detection Technique for Planning and Monitoring Sustainable Mango Crop Areas using Landsat-8 Images

A Comprehensive Analysis of Advanced Change Detection Techni...

引用

International Conference on computing for Sustainable Global Development (INDIACom)

作者： Harish Chandra Verma Tasneem Ahmed Crop Improvement and Biotech Division ICAR - Central Institute for Subtropical Horticulture Lucknow India Dept. of Computer Application Integral University Lucknow India Dept. of Computer Application Advanced Computing Research Lab. Integral University Lucknow India

An efficient and accurate mango (Mangifera Indica L.) crop planning is a very important factor for sustainable mango crop production. Mango is a perennial fruit crop and alternate bearer, which is a major challenge for production sustainability. The main objective of this study is to detect the land cover changes using satellite images for sustainable land cover planning in three main mango-producing districts, namely Lucknow, Bulandshahr, and Meerut of Uttar Pradesh State, India. In this study, Landsat-8 OLI images from 2013–14 to 2020–21 have been used to detect the changes by implementing the log ratio (LR) and principal component analysis (PCA) fusion-based change detection method. In order to extract meaningful information from Landsat-8 images, Red and NIR band images were used to create soil-adjusted vegetation index (SAVI) images. Thereafter, the fusion-based change detection method using LR-PCA has been applied to retrieve the enhanced image. To obtain the final change images with the detected temporal changes in the area under the mango crop, image thresholding has been used. After conducting a detailed analysis, it is observed that the change detection accuracy of the LR-PCA fusion-based change detection technique is retrieved as 92%, which is quite high. In the future, proper mango monitoring techniques may play a very important role in developing sustainable mango orchards and maintaining environmental health. Future applications of this study will include change detection in mango crop areas in all major mango-producing districts in Uttar Pradesh.

关键词： Earth Artificial satellites Crops Vegetation mapping Production Planning Sustainable development

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Next Generation HR: Transforming Talent Management with AI and ML Innovations

Next Generation HR: Transforming Talent Management with AI a...

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Disruptive Technologies (ICDT), International Conference on

作者： Niharika Singh Pooja Singh Sudeshna Chakraborty Dept. of Master of Computer Application GL BAJAJ Institute of Technology & Management Greater Noida U.P. Department of CSE School of Computing Science & Engineering Galgotias University Greater Noida (U.P.) Department of Computer Application School of Computing Science and Engineering Galgotias University Greater Noida

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-dept. analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success.

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Quantifying uncertainty in Bayesian Networks structural learning

Quantifying uncertainty in Bayesian Networks structural lear...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Vitor O. Barth Henrique O. Caetano Carlos D. Maciel Marco Aiello Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo Department of Service Computing Institute for Architecture of Application Systems University of Stuttgart

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

The key goals in learning Bayesian networks (BNs) from data are to identify significant statistical relationships between variables and to build a Directed Acyclic Graph (DAG) that represents these relationships through Joint Probability Distributions. Most research relies on score-based or conditional test methods for model selection. However, when using real-world data, it can be challenging to identify whether the learned DAG represents the underlying relations inherent in the limited datasets, particularly when evaluating data obtained from multiple independent sources. This study presents a methodology to assess the credible interval for both the existence and direction of each edge within Bayesian networks derived from data. Furthermore, it explores the fusion of models acquired from distinct and independent datasets. This approach enables the Bayesian learning of Bayesian Networks (BNs) from data by treating the uncertainty associated with the existence and orientation of each edge as a random variable. By evaluating the probability of the orientation of each edge, it is possible to suggest the existence of a potential latent variable within the dataset. If an edge exhibits equiprobable directions and is verified to exist, it becomes a plausible hypothesis for a latent variable. The Fast Causal Algorithm, originally introduced by [1], is the foundation of this approach. Finally, by employing a maximum a posteriori estimation, the most prominent edges and their respective orientations are identified and employed to create the leading DAG. We present our findings in simulated datasets with different length sizes. By comparing the structure of the learned DAGs with existing structures and evaluating the inference capabilities of the final BN, we establish that our approach achieves results comparable to the most recent studies in the field, while offering insights into the model’s reliability and improving the use of the data.

关键词： Maximum a posteriori estimation Uncertainty Scalability Data models Probability distribution Bayes methods Random variables

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Intelligent Resource Allocation in Power Grids: Leveraging Variable Neighborhood Search for Optimal Switch Allocation

Intelligent Resource Allocation in Power Grids: Leveraging V...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Henrique O. Caetano Matheus de S. S. Fogliatto N. Luiz Desuó Benvindo R. Pereira J Carlos D. Maciel Marco Aiello Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo Department of Service Computing Institute for Architecture of Application Systems University of Stuttgart

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Within the paradigm of Evolving and Adaptive Intelligent Systems, the Electrical Power System (EPS) represents a Critical Infrastructure demanding robust reliability metrics. This study pioneers the application of Variable Neighborhood Search (VNS) heuristic to the Optimal Switch Allocation (OSA) problem in the EPS. Adapting neighborhood structures and local search methods, we consider the joint allocation of Manual Switches and Remote-Controlled switches, addressing interdependencies. Results highlight VNS efficacy in navigating OSA challenges, showcasing its adaptability in an evolving system. Comparative analyses endorse VNS as a valuable tool for addressing EPS reliability within the dynamic landscape of evolving and adaptive intelligent systems.

关键词： Adaptive systems Uncertainty Switches Search problems Robustness Power grids Critical infrastructure

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