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检索条件"任意字段=4th International Multi-Conference on Computing in the Global Information Technology"

共 11813 条记录，以下是131-140 订阅

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Enhancing Object Detection Robustness in Adverse Weather Conditions 4

Enhancing Object Detection Robustness in Adverse Weather Con...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Yacoob, Shaik Kumar, D. Phani Harshitha, G.G.S. Teja, V.Y. Sai Veni, C.H. Satya Reddy, K. Mahidhar Department of Computer Science and Engineering Godavari Global University Rajahmahendravaram533296 India Department of Computer Science and Engineering Godavari Institute of Engineering & Technology Rajahmahendravaram533896 India

Fundamental applications in autonomous cars, robots, and surveillance systems include object detection for safety reasons such as recognizing vehicles, pedestrians, trees, street poles, and buildings. However, one of the major challenges it faces from adverse weather conditions like rain, haze, and fog through deterioration of accuracy and an increase in safety risks such as traffic accidents. this paper proposes an enhanced object detection system to improve its robustness and accuracy in challenging environments. the proposed approach effectively addresses the problem of poor image quality due to adverse weather conditions by using unsupervised domain adaptation and including a Quasi-Translation Network and Feature Calibration Network in the YOLO(You Only Look Once) framework. It attempts to leverage the success made in CNNs(Convolutional Neural Networks) and mass amounts of datasets in order to adapt YOLO-series detectors in the best possible way, achieving object detection under challenging environmental conditions with precision and speed. Experimental results demonstrate that the solution proposed outperforms existing models in terms of adaptability and accuracy, providing a scalable and reliable solution for autonomous systems to enhance overall safety and operational efficiency in real-world environments. © 2025 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Classification-Based Machine Learning For Detection of DDoS Attack In Cloud computing

Classification-Based Machine Learning For Detection of DDoS ...

引用

computing and information technology (ICCIT-1441), 2020 international conference on

作者： Amenah Naji D Alazmi Yasser Obaid Alharbi College of Computer Science and Engineering University of Hail Hail Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

the proliferation of cloud computing has marked a pivotal shift in the delivery of information technology (IT) services, offering enhanced scalability and operational efficiency through on-demand access to remote data centers over the Internet. As cloud infrastructures continue to evolve, the imperative for robust security mechanisms becomes increasingly critical. these attacks not only disrupt traditional network environments but also present substantial challenges to emerging paradigms such as edge computing, cloud platforms, and the Internet of things (IoT) [1], [2]. the proposed solution presents a promising avenue for fortifying the security of cloud computing by enabling the precise identification and timely mitigation of DDoS attacks. the utilization of classification algorithms not only facilitates the Perception of such attacks but also enhances the overall resilience of cloud-based services, ensuring uninterrupted access for legitimate users. these algorithms have achieved high accuracy rates ranging from 89.7% to 95% in different studies. the proposed method combining KNN, SVM, DT, NB and RF achieved the highest accuracy of 95%.

关键词： Support vector machines Cloud computing Accuracy Scalability Denial-of-service attack Classification algorithms Security Internet of things information technology Software defined networking

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A Fuzzy-Based System for Water pH and Conductivity Level Management

A Fuzzy-Based System for Water pH and Conductivity Level Man...

引用

computing and information technology (ICCIT-1441), 2020 international conference on

作者： Jafar Alyaseen Eid Albalawi Department of Computer Science College of Computer Science and Information Technology King Faisal University AlHofuf Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Maintaining pH, conductivity, and water tank levels is essential for water quality in industrial heat transfer applications. Traditional Proportional-Integral-Derivative (PID) controllers may struggle to manage nonlinear relationships and process dynamics. To address this challenge, this paper proposes using a Mamdani-type Fuzzy Logic Controller (FLC) designed specifically for industrial settings. the FLC uses sensor data to regulate control valves and achieve the desired water quality and quantity. the results demonstrate the effectiveness of the FLC in managing complex industrial water systems.

关键词： Fuzzy logic Storage management Process control Water heating Water quality Conductivity Valves PD control Heat transfer Tuning

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Enhancing Automated Requirement Analysis in Agile Environments through Visualization Tools

Enhancing Automated Requirement Analysis in Agile Environmen...

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computing and information technology (ICCIT-1441), 2020 international conference on

作者： Nawaf Alrabighi Mohammad M. Alqahtani Dept. of Computer Science and Artificail Intelligence University of Jeddah Jeddah Kingdom of Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Agile methodologies have profoundly transformed project execution paradigms, prioritizing iterative development, adaptability, and extensive stakeholder collaboration. However, accurately defining and verifying project requirements remains a considerable challenge. this paper developed a comprehensive framework that utilizes visualization tools to augment each phase of the requirement analysis process. this framework is designed not only to tackle the technical facets of requirement analysis but also to enhance communication and collaboration among stakeholders. the successful implementation of this novel framework has demonstrated its efficacy in navigating the complexities of Agile projects, enhancing collaboration, and ensuring successful project execution.

关键词： Visualization Navigation Collaboration Complexity theory Stakeholders Requirements engineering Iterative methods information technology

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Research on Quantitative Security Assessment Model of computing Power Services 25

Research on Quantitative Security Assessment Model of Comput...

引用

Proceedings of the 2025 4th international conference on Big Data, information and Computer Network

作者： Guo Yu Jing Ai Ai Gu Mengtao Han The Ministry of Industry and Information Technology Key Laboratory of Performance and Reliability Testing and Evaluation for Basic Software and Hardware The Fifth Electronics Research Institute of the Ministry of Industry and Information Technology of P.R. CHINA Guangzhou Guangdong China

ISBN: (纸本)9798400712425

With the accelerated growth of the global demand for computing power, computing power services (CPSs) have emerged as the times require and are becoming a brand-new form of business delivery. Meanwhile, there are security risk issues in CPSs. However, the research on computing power security mainly focuses on the security risks that CPSs may face and related security technologies, lacking systematic research on the security assessment of CPSs. this paper puts forward a security assessment model of CPSs and uses the Analytic Hierarchy Process (AHP) to assign different weights to all indicators within the model according to their importance, enabling the assessment model to be quantified. Eventually, a quantitative security assessment model of CPSs is formed, which provides technical support for the security assessment of CPSs and offers a powerful reference for users to choose safe, reliable and high-quality CPSs.

关键词： Analytic Hierarchy Process (AHP)

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HMM Based Approach for Arabic Word Segmentation

HMM Based Approach for Arabic Word Segmentation

引用

computing and information technology (ICCIT-1441), 2020 international conference on

作者： Ayoub Kadim Dept. of Computer Science Faculty of Applied Sciences A-M IMIS Research Laboratory Ibn Zohr University Morocco

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Word decomposition or segmentation is one of the first stages of Natural Language Processing, particularly written language. It aims to find the decomposition of each compound word into its sub-elements. However, given the inflectional nature of the Arabic language, this process becomes more difficult for Arabic words, especially for ambiguous cases, in which the same word can have several possible decompositions. In this article, we try to propose a new approach based on the Hidden Markov Model to decide which segmentation to choose in case of ambiguity. the decision will be based on an auxiliary element predicted by the HMM for each word according to its context.

关键词： Vocabulary Hidden Markov models Tagging Vectors Natural language processing Compounds information technology

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Hierarchical Quantum Classification 4th

Hierarchical Quantum Classification

引用

4th international conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Gohel, Prashant Joshi, Manjunath HCL Technologies Karnataka Bengaluru India Dhirubhai Ambani Institute of Information and Communication Technology Gujarat Gandhinagar India

ISBN: (纸本)9789819757022

Quantum computing in machine learning gives us a new tool to address few complex problems in the domain of artificial intelligence (AI). In this work, we use the hybrid, i.e., a combination of quantum and classical Quantum convolution neural network (HQCNN) in a hierarchical way for image classification. A four class classification is obtained hierarchically using three HQCNNs. In phase 1, we perform binary classification using HQCNN1 to get the two class classification with each class a mix of two individual classes. We then further classify each of the images into their individual categories with the use of HQCNN2 and HQCNN3 in phase 2 by performing two more binary classifications. Two experiments are conducted to show the efficacy of our proposed approach. In the first experiment we use X-ray images of organs consisting of Chest, Hand, Ankle, and Knee images which are taken from UNIFESP dataset. In our second experiment we use Deer, Horse, Airplane, and Ship images drawn from CIFAR10. We implement our proposed approach on Pennylane Software Development Kit (SDK), a simulation tool that can be implemented on a classical machine. Comparison of results with the classical method clearly indicates that the training using the proposed approach takes fewer number of epochs with improvement in accuracy and the convergence is also stable when compared to the classical approach. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum computers

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Optimization-based Techniques Prediction Model in Determining Employee Turnover 4

Optimization-based Techniques Prediction Model in Determinin...

引用

4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Bustillo, James Cloyd M. Department of Information System Agusan Del sur State College of Agriculture and Technology Bunawan Philippines

Employee turnover is a vital challenge for organizations, often resulting in high financial and operational costs. Traditional methods of turnover prediction have been limited in their ability to capture the complexity of factors influencing employee turnover, leading to ineffective retention strategies. this study proposes a novel approach by integrating Genetic Algorithms (GAs) with machine learning models to enhance the accuracy of turnover predictions. the research introduces an optimization-based prediction model utilizing GAs combined with Naive Bayes and KNN algorithms. Four crossover operators, including traditional AX and three advanced operators, CAX, IBAX, and FMSAX, are employed to optimize the model's feature selection and parameters. A dataset of employee turnover records from 2015 to 2023, encompassing various demographic and job-related factors, was used to train and test the model. the results demonstrate that the FMSAX operator, paired with KNN, achieves the highest predictive accuracy, precision, F-measure, and recall compared to other combinations. the findings suggest that the GA-optimized model significantly improves the ability to predict employee turnover and offers actionable insights for developing effective retention strategies. this study contributes to the field by providing a robust tool for human resource management, capable of adapting to dynamic workforce changes and minimizing turnover rates, ultimately enhancing organizational performance. the proposed model improves predictive accuracy and ensures its applicability across different organizational contexts by allowing the integration of new HR data. © 2025 the Authors.

关键词： Prediction models

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Design of Network Security Situation Assessment Algorithm Based on Artificial Intelligence 14th

Design of Network Security Situation Assessment Algorithm Ba...

引用

14th international conference on Frontier computing, FC 2024

作者： Yang, Hao Jin, Yang Li, Haosheng Yun, Kai Bao, Jian State Grid Xinjiang Electric Power Co. LTD. Information and Telecommunication Company Urumqi China

ISBN: (纸本)9789819623907

With the rapid development of information technology, the issue of network security has become increasingly prominent, and has become an important factor affecting national security, enterprise development and even personal privacy. the traditional means of network security protection are inadequate to cope with the increasingly complex and unpredictable network threats. therefore, the use of artificial intelligence technology to evaluate the network security situation has become an innovative and effective method. the significance of this study is to propose a new network security situation assessment algorithm based on artificial intelligence. this algorithm can effectively integrate and analyze large amounts of network data, detect potential threats in time, and provide accurate security assessments to help network security managers make faster and more accurate decisions. Advanced artificial intelligence technologies such as deep learning and pattern recognition will be used in the study to comprehensively analyze multi-source data such as network traffic, user behavior and system logs. By training the algorithm model to identify and predict network security threats, the dynamic assessment of network security situation is realized. It can be seen from the experimental data that the test parameters of the network security situation assessment algorithm of artificial intelligence are the assessment accuracy rate, the processing scheme accuracy rate, the improvement scheme accuracy rate and the data reporting accuracy rate, and the four data are above 95%. therefore, it is not difficult to find the hidden potential of artificial intelligence network security situation assessment algorithm in practical application. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial Intelligence Firewall information Protection Network Security Situation Assessment

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CSRP Protocol for High Accuracy Performance in Wireless Sensor Networks

CSRP Protocol for High Accuracy Performance in Wireless Sens...

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computing and information technology (ICCIT-1441), 2020 international conference on

作者： Ahmed Subhi Abdalkafor Salah A. Aliesawi College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In recent years, Wireless Sensor Network (WSN) has played a broad role in many vital applications that are used to monitor environmental and physical events. One of the major issues in WSN is the overlapping region of sensing bands, which occurs due to densely and randomly deployed nodes in the target environment. the data aggregation technique is used as a solution that processes overlapping regions. this technique manages and redirects these data to the sink node through a multi-hop network. therefore, the data transmission number and the duration toward the sink node are reduced, which fixes the overlapping region issue. However, using data aggregation techniques limited network performance due to energy consumption. the objective of this paper is to address this limitation by employing intelligent algorithms to enhance the operation strategies of data aggregation techniques. A protocol based on the Hierarchical Agglomerative Clustering (HAC), Self-Organized Map (SOM), and Enhanced Radial Basis Function (ERBF) neural network is proposed in order to accomplish this goal. the clustered SOM and RBF-based Intelligent Protocol (CSRP) is the name of this proposed protocol. When compared to pertinent recent studies, the suggested protocol produced the best results in terms of the accuracy of the data arriving at the base station.

关键词： Training Wireless sensor networks Base stations Protocols Accuracy Artificial neural networks Spread spectrum communication Radial basis function networks Data aggregation Sensors

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