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检索条件"机构=Computer Engineering and Applications"

共 9153 条记录，以下是21-30 订阅

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Mobile Cloud Computing: The Future of Cloud

Mobile Cloud Computing: The Future of Cloud

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kushwaha, Sumit Rai, Ankur University Institute of Computing Chandigarh University Department of Computer Applications India Gla University Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350373783

The term 'Mobile Cloud Computing (MCC)', which combines mobile and cloud computing, has gained popularity in the IT sector and become a hot topic of conversation. MCC has completely changed how mobile users use services while on the go around the world. Only a few years ago, mobile handsets were merely tools for making voice calls;today, they are smart gadgets that let users access value-added services whenever and wherever they want. As MCC is still in its infancy, it is essential to gain a complete understanding of the technology in order to identify the course of future research. With the latter objective in mind, this paper provides an overview of the history and guiding principles of MCC, as well as its traits, benefits, uses, and emerging research trends. The history of MCC is briefly described, starting with mobile computing and ending with cloud computing. The rest of the paper examines the difficulties of mobile cloud computing, provides an overview of various related research efforts, and identifies possible future research topics. © 2024 IEEE.

关键词： Cloud platforms

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Lane Detection for Autonomous Vehicles with Canny Edge Detection and General Filter Convolutional Neural Network 18

Lane Detection for Autonomous Vehicles with Canny Edge Detec...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kishor, S. Nair, Rekha R. Babu, Tina Sindhu, S. Vishnu Vilashini, S. Department of Computer Applications Mangalore India Alliance University Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India

ISBN: (纸本)9789380544519

Lane detection has been a complex issue that has garnered the attention of the computer vision community for many years. It is a crucial element for self-driving cars and computer vision in general. Lane detection is used to define the path for autonomous vehicles and prevent the risk of drifting into another lane. So proposed a study to develop a method that can detect lane lines in real-time using the OpenCV library and computer vision concepts. To accomplish this, identify the white markings on both sides of the lane. The proposed work focuses on identifying the road lane lines that autonomous cars must adhere to, ensuring that they do not cross into other lanes or drive in the opposite direction, which could lead to accidents. The performance of the work assessed using actual road images and videos captured by the car's front-mounted camera. So proposing a basic and easy algorithm for tacking and detecting lanes. The lanes have been detected through all competitions based on data from the camera, which is carried by algorithms. The proposed work implementing image processing techniques to capture the exact and accurate lane ways and hough transformation techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Arduino UNO Canny Detection DHT11 Hough Transformation LDR sensor

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Integrated Life Saving Solution for Free Movement of Ambulance in Heavy Traffic Using YOLOV8

Integrated Life Saving Solution for Free Movement of Ambulan...

引用

2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Mothukuri, Gangadhar Krishna, J. Hari Kumar, Jetti Pavan Velagapudi Ramakrishna Siddhartha Engineering College Department Of Computer Applications Vijayawada India Department Of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350366846

Ambulance Service is considered to be the fastest service that is to be reached to every citizen in this existing world. This Vehicles serves in the Medical Emergencies of a person. At the traffic signals when the red signal is given a large number of vehicles will be stopped and waits for green signal. When an Ambulance is coming through that way then this waiting vehicle will be a great issue to the Ambulance motion forward in that way. In order to resolve this issue, we will make software consisting of different audio, cam sensors and Hardware components with micro controllers that will detect the Ambulance a little before reaching the signal junction. After detecting the required vehicles, it will send a signal to the Hardware components. Then the software present in Hardware will give the yellow signal first and then green signal to the way through which the Ambulance is coming. By this way we can clear route to emergency vehicles in traffic jam. As the traffic is clear, it will be very easy for the Ambulance diver to reach the patient in the vehicle to Hospital on time. © 2024 IEEE.

关键词： Data transfer

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Financial Analytics with Artificial Neural Networks: Predicting Loan Repayment 8

Financial Analytics with Artificial Neural Networks: Predict...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Thakar, Siddharth Patel, Deep Gandhi, Vaibhav C. Trivedi, Dhriti Computer Engineering Department Vadodara India Bachelors in Computer Applications Vadodara India Computer Engineering Department Anand India

ISBN: (纸本)9798350376425

With the advancement of the economic sector, a large number of individuals are applying for bank loans. However, financial institutions have limited resources, which must be allocated selectively to minimize risk. Deciding who should be given credit is a crucial decision-making process. This study seeks to reduce the potential challenge of choosing appropriate borrowers, thus saving valuable time and resources for banks. The method includes analyzing past information about people who have received loans before, and utilizing this information to educate a system using an Artificial Neural Network (ANN) model in order to generate precise forecasts. The main goal of this paper is to forecast if providing a loan to a specific person would be a safe and wise choice. © 2024 IEEE.

关键词： Artificial Neural Network Banking sector Finance Loan Repayment Machine Learning classifier

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A Study of Clustering Algorithms on Big Data using Spark 4

A Study of Clustering Algorithms on Big Data using Spark

引用

4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Vijay, A. Shilpa, S. Bms College of Engineering Department of Computer Applications Bangalore India

ISBN: (纸本)9798350393354

Big data clustering on Spark is a practical method that makes use of Apache Spark's distributed computing capabilities to handle clustering tasks on massive datasets such as big data sets. Using the unsupervised learning technique of clustering, related data points are grouped together to find underlying structures and patterns. Spark's strong platform overcomes the performance and scalability restrictions of conventional clustering techniques, allowing effective and scalable clustering operations. The performance and dependability of big data clustering on Spark are further improved by fault tolerance and in-memory processing. By employing popular clustering algorithms like k-means, hierarchical, bisecting, GMM, and BIRCH, researchers and practitioners can extract valuable insights from extensive datasets, making informed decisions and unlocking meaningful patterns on a large scale. © 2024 IEEE.

关键词： Cluster analysis

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Fitness EDA using Advanced PySpark 4

Fitness EDA using Advanced PySpark

引用

4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Kiran, C. Pushpa, T.S. Shilpa, S. Shailaja, K.P. Bms College of Engineering Department of Computer Applications Bengaluru India

ISBN: (纸本)9798350393354

Exploratory Data Analysis (EDA) plays a pivotal role in comprehending intricate datasets and deriving meaningful insights. This research focuses on harnessing advanced PySpark and SQL techniques for conducting EDA on FitRec (Fitness Record) data, an extensive dataset obtained from the well-known fitness tracking application, Endomondo. Leveraging PySpark's distributed computing capabilities and Spark SQL's querying prowess, this study delves into data preprocessing, cleaning, and visualization. Also, this study enables a comprehensive understanding of users' fitness patterns and performance. Through interactive analysis and real-world case studies, the study showcases the effectiveness of employing PySpark and SQL for in-depth EDA on FitRec data, delivering valuable knowledge to researchers and data analysts in the fitness domain. © 2024 IEEE.

关键词： Data visualization

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IntelliSecure AI-Powered Intrusion Detection Framework 7

IntelliSecure AI-Powered Intrusion Detection Framework

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Govindaraj, M. Asha, V. Marutheesha, H. Sai Kumar, Madala Dileep Muniprasad, M. Ramesh, Nithya New Horizon College of Engineering Computer Applications Bengaluru India

ISBN: (纸本)9798350359299

The rapid advancement of technology has led to the emergence of AI-driven security systems that mimic human behavior, particularly in detecting intrusions. Security remains a paramount concern in the interconnected society, with Intrusion Detection Systems (IDS) being crucial for identifying various categories of attacks. This study explores the utilization of machine learning (ML) techniques in intrusion detection for networks, focusing on recent strategies and approaches employed in Network-centric Systems. Through analysis of recent research, we highlight the typical attacks detected by these systems and the challenges they face. The primary objective of this study is to detect attacks based on specific patterns in the flow of information across the internet. AI-based intrusion detection systems play a critical role in combating malware and hacking, ensuring the confidentiality of sensitive data within organizations. These systems are designed to address real-world challenges, aiding in the prevention of network misuse and the identification of web vulnerabilities. Utilizing methods such as machine learning, decision trees, and fuzzy semantics, this intrusion detection system offers a comprehensive approach to enhancing cybersecurity. © 2024 IEEE.

关键词： Wireless sensor networks

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A Deep Learning Based Strategies for Scene-Text VQA System used in Industrial applications: A Critical Analysis 1

A Deep Learning Based Strategies for Scene-Text VQA System u...

引用

1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Agrawal, Mayank Singh Jalal, Anand Sharma, Himanshu Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350384246

Scene-Text Visual Question Answering (Scene-Text VQA) is an emerging research area that combines computer vision, natural language processing, and scene understanding. The goal of Scene-Text VQA is to develop models and algorithms that can comprehend scene text and accurately answer questions based on textual and visual information present in images. Scene-Text VQA models used in different areas such as quality control and inspection, equipment maintenance, safety compliance, supply chain visibility, industrial automation, industry 4.0, assistance for the visually impaired, document analysis, autonomous vehicles, e-commerce, smart cities, cultural heritage preservation, surveillance, and security. Scene-Text VQA models face unique challenges, including accurate text detection and recognition, understanding contextual cues, handling language complexities, fusing text and image features effectively, limited training data, biases in datasets, generalization to unseen text and scenes, computational complexity, and appropriate evaluation metrics. In this paper we will explain different methods and algorithms for Scene-Text VQA models that can comprehend scene text and accurately answer questions based on textual and visual information present in images. The authors will distinguish all the techniques of Scene-Text VQA for effectively process and comprehend the textual information present in images and show how can one technique is conflicting then the other as well. © 2024 IEEE.

关键词： Inspection

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The Rise of Intelligence: A Comprehensive Review on AI and AI Automation 3

The Rise of Intelligence: A Comprehensive Review on AI and A...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhardwaj, Cheshtaa Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331528201

This paper presents a comprehensive review of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies, focusing on their impact in driving AI automation across diverse sectors with a special emphasis on robotics. As AI continues to evolve, its integration into automation systems has revolutionized industries, enhancing efficiency, precision, and decision-making. The role of AI automation in robotics is critically examined, covering key architectural frameworks that enable autonomous operation and intelligent decision-making. Furthermore, the security aspects associated with AI in robotics, identifying attack surfaces and potential vulnerabilities has been analysed. This review highlights the transformative potential of AI automation while addressing the challenges and security concerns that accompany its deployment in modern systems, particularly in robotics. © 2024 IEEE.

关键词： Adversarial machine learning

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CNN Image Forgery Detection: Machine Learning Improves Photojournalism Accuracy and Robustness 3

CNN Image Forgery Detection: Machine Learning Improves Photo...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Thakur, Varsha Agarwal, Rohit Gla University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331528201

The purpose of this study is to address the rising problem of picture modification and fraud, especially in the field of photojournalism, where the ability to recognize changed photos is essential to preserving faith in digital technology. By employing CNN-based approaches, the created forgery detection system exhibits its capacity to reliably discern between actual and AI-generated photos, reaching a balanced classification of 49% and 50%, respectively, with low misclassification. The purpose of this research is to investigate the development of digital picture forensics by using both conventional methods and contemporary innovations such as blockchain and deep learning. In order to achieve a higher level of detection accuracy, the suggested system makes use of machine learning models, such as transfer learning using pretrained models like ImageNet. The findings demonstrate that the system is capable of generalization and has a limited amount of overfitting, as seen by the high training accuracy of 98.44% and the validation accuracy of 98.92%. It is accepted that there is a need for future improvements in scalability, robustness to adversarial assaults, and interpretability, despite the fact that the findings have been somewhat encouraging. In order to successfully battle increasingly complex picture forgeries, future breakthroughs in digital image forensics will need cooperation between the government, industry, and academic institutions. © 2024 IEEE.

关键词： Adversarial machine learning

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