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AI-Driven Continuous Integration and Continuous Deployment in Software engineering 2

AI-Driven Continuous Integration and Continuous Deployment i...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Mohammed, Abdul Sajid Saddi, Venkata Ramana Gopal, Santhosh Kumar Dhanasekaran, S. Naruka, Mahaveer Singh School of Computer and Information SciencesUniversity of the Cumberlands KY United States Technology Lead RaleighNC United States Technology Lead CharlotteNC United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India G.L. Bajaj Institute of Management Greater Noida India

ISBN: (纸本)9798350371055

AI driven Continuous Integration and Continuous Deployment is a new way of managing and continually updating a software project. This process, powered by Artificial Intelligence, automates the entire software delivery and deployment process - from code submission to monitoring and bug fixing. It eliminates manual errors and allows for multiple versions to be tested in parallel, saving time and effort. By increasing agility, it allows organizations to launch new features to production faster than ever. Continuous Integration and Continuous Deployment leverages artificial intelligence in implementation and execution. It automates the process of integration, testing, packaging and deployment. Furthermore, AI is used to detect and fix bugs which can prevent delays and costly production bugs. AI driven Continuous Integration and Continuous Deployment has become an increasingly popular development strategy. It helps reduce the overall cost and accelerate the software's production cycles, making it easier for developers to quickly get their features and services in the hands of the market. © 2024 IEEE.

关键词： Adaptive Systems Artificial Intelligence automation Cloud Computing Machine Learning

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Analytical Approach Towards Cybersecurity Through AI-Enabled Threat Intelligence 11

Analytical Approach Towards Cybersecurity Through AI-Enabled...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Singh, Anurag Kanishka Dubey, Sanjay Kumar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

A growing variety of cyber hazards are appearing as the world becomes more and more digital. The sophistication and rate of innovation of emerging threats puts organizations of all sizes at danger, and traditional cybersecurity solutions frequently fall short of keeping up. Threat intelligence powered by artificial intelligence, or AI-TI, offers a fresh and cutting-edge method for tackling cybersecurity issues. To find patterns and anomalies that could point to a cyber-attack, AI-TI solutions can examine massive amounts of data from a variety of sources, including as internal networks, security logs, and open threat intelligence feeds. Then, with the use of this information, security teams can be made aware of prospective dangers and assist in the creation of defense measures. Compared to conventional cybersecurity solutions, AI-TI systems have a number of benefits: automation: AI-TI systems have the potential to automate a number of threat detection and response-related duties, freeing up security staff to concentrate on more strategic projects. Continuous improvement: AI-TI systems have the ability to continuously learn and get better with time, which enables them to stay on top of the most recent threats. Advanced threat detection: AI-TI systems can be used to identify and react to threats that are too sophisticated or subtle for traditional approaches to identify. © 2024 IEEE.

关键词： Artificial intelligence

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AI-Based Early Detection of Coronary Artery Disease Using Atherosclerosis Inflammatory and MicroRNA Biomarkers in Angina Patients 6

AI-Based Early Detection of Coronary Artery Disease Using At...

引用

6th international conference on Robotics and automation in Industry, ICRAI 2024

作者： Sajid, Muhammad Hassan, Ali Khan, Dilshad Ahmed Abbas Gilani, Sayed Tanveer Khan, Shoab Ahmed Bakhshi, Asim Dilawar CEME National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Rawalpindi Pakistan MCS National University of Sciences and Technology Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest and is responsible for a high percentage of global mortality. Early diagnosis of CAD in patients with chest pain is challenging. Coronary angiography, an invasive procedure that is not widely available in developing countries, confirms the diagnosis of CAD. It is imperative to establish noninvasive biomarkers for the early diagnosis of CAD in patients with angina. Motivated by two recent medical diagnostic studies that used novel molecular (microRNA) and chemical (atherosclerosis-inflammatory) biomarkers for early detection of CAD, the proposed study explores the hidden potential of novel biomarkers using machine learning (ML). The study combines the biomarkers of two medical studies and presents a NUMS-NIHD dataset. From the complete dataset, the best feature set is shortlisted after an extensive evaluative search in the biomarker feature space. Ten ML classifiers are applied to the selected biomarker inputs, including clinical, chemical, and molecular cardiac biomarkers. To select optimal biomarker-classifier combinations, experiments use K-fold cross-validation. The results of K-fold cross-validation are validated against a hold-out set of data. The results are then compared to current state-of-the-art benchmark studies. The detection strategy achieved an accuracy of 90.18%, a sensitivity of 92.33 % and a specificity of 100.00%. The proposed study validated previous medical diagnostic studies and significantly improved the results. These ML biomarkers-classifier combinations can be used to exclude healthy individuals non-invasively before angiography. © 2024 IEEE.

关键词： Diseases

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Sensorless Control of Permanent Magnet Synchronous Motor of Scroll Air Compressor of High Power PEM Fuel Cell Based on LADRC and HSMO 6

Sensorless Control of Permanent Magnet Synchronous Motor of ...

引用

6th international conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Yuan, Weiwei Guo, Xuan Cai, Zenan Ou, Kai School of Computer and Big Data Minjiang University Fuzhou China School of Mechanical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (纸本)9798350353563

Aiming at the problem of speed fluctuation of permanent magnet synchronous motor (PMSM) caused by periodic load change during the operation of scroll air compressor, a sensorless control method based on linear active disturbance rejection control (LADRC) and high-order sliding mode observer (HSMO) is proposed. The HSMO is utilized to observe the rotor position information. To mitigate the impact of load disturbances on the observation effectiveness of the HSMO, the LADRC for speed loop is designed. The simulation results demonstrated that the HSMO can accurately observe the rotor position information under the load fluctuations. Additionally, compared with the PI controller, the range of speed fluctuations was reduced by 1.06% through LADRC, thereby significantly enhancing the stability of scroll air compressor operation. © 2024 IEEE.

关键词： Disturbance rejection

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Machine Learning based Early Detection of Ongoing Cyber-Attacks 3

Machine Learning based Early Detection of Ongoing Cyber-Atta...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bhagyalakshmi, A. Sruthi Laya, C.D. Yoga Preethikaa, A.M. Varsha, V. Easwari Engineering College Computer Science and Engineering Department India

ISBN: (纸本)9798350375190

The need for sophisticated detection and response systems to counteract cyber threats has grown in importance as the cybersecurity landscape changes. In order to improve cyberattack detection and response, this study proposes a novel combination of a Security Orchestration, automation, and Response (SOAR) framework with a machine learning (ML) model. A Random Forest classifier was created and trained to distinguish between benign and potentially dangerous files using a dataset that included a variety of executable file properties. The classifier demonstrated a notable level of accuracy and precision. Then, a prediction API was established to enable real-time threat analysis by seamlessly integrating the learned machine learning model into a SOAR system. Through this integration, possible threats can be continuously identified by evaluating real-time data that has been retrieved from network traffic and endpoints. The SOAR system is activated when a suspicious file is found, which sets off a sequence of automatic events that include notifying security staff and starting response procedures. For a comprehensive and strong defence strategy, the system also integrates with already-in-use security solutions like firewalls and intrusion detection systems (IDS). The integration strengthens the overall cybersecurity posture by improving response time and increasing the effectiveness of cyberattack detection. This study presents the integrated approach's methodology, system architecture, and effectiveness, showcasing its potential as a comprehensive cybersecurity solution. © 2024 IEEE.

关键词： Cybersecurity

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Measurements of Insulation Parameter C3 on Installed, Faulted Line Bushings

Measurements of Insulation Parameter C3 on Installed, Faulte...

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2024 international conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2024

作者： Iskra, Pavel Zatsepina, Violetta Zatsepin, Evgeny Lipetsk State Technical University Automation And Computer Science Faculty Lipetsk Russia

ISBN: (纸本)9798350395013

The measuring test lead is an important part of high voltage bushings with capacitor-type insulation. The test lead is connected to the outer capacitor lining of the frame, dividing the input capacitance into two: C1 (pipe - last outer lining of the frame) and C3 (last outer lining of the frame - flange). For diagnostics of the in service condition of the input, it is sufficient to measure the insulation resistance R3, the capacitance C1 and the dissipation factor tgδl. The measured and corrected value tgδ1 is compared with the data of the acceptance test report. In the delivery condition, the obtained value tgδl should be close to the nameplate value. A significant difference of more than 3% between the capacitance value C1 and the value specified in the acceptance test report may indicate damage during transportation or installation, so that the input must not be put into operation. The value of the capacitance C3 depends on how the input is integrated. The article deals with the method of measuring the insulation parameter C3 at the installed, faulted line bushings. The confirmation of the normal contact of the outer lining of the hulls of the already installed line bushings is obtained. The conclusion contains conclusions on the results of measurements and recommendations to the equipment manufacturers to make changes in the operating manuals of the bushings. © 2024 IEEE.

关键词： Capacitance

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Automatic Feed Dispenser for Aquaculture using Arduino Technology 3

Automatic Feed Dispenser for Aquaculture using Arduino Techn...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Adimulam, Raghuvira Pratap Kokkiligadda, Vasanth Polagani, Aaryasri Velagapudi Ramakrishna Siddhartha Engineering College Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350354171

In addressing the dynamic needs of the aqua-culture sector, essential for meeting the escalating global demand for seafood, the development of efficient and precise feeding methodologies becomes paramount. The limitations of conventional feeding practices, marked by manual intervention leading to inefficiencies, imprecise schedules, and potential resource wastage, underscore the necessity for automation in aquaculture. Our research, titled 'Automatic Feed Dispenser for Aquaculture,' focuses on revolutionizing the manual feeding process by introducing an IoT-based dispenser that leverages advanced technologies, prominently featuring Ultrasonic sensors, Arduino technology, and a Bluetooth module. This Automatic Feed Dispenser is designed to offer both automatic and manual operation, addressing the challenges associated with conventional methods. Notably, the dispenser incorporates Ultrasonic sensors to optimize the feeding process by enabling obstacle avoidance. This key feature empowers the farmer to control feed dispensing even while occupied with other tasks, highlighting the system's efficiency and adaptability. The utilization of Arduino-based hardware serves as a pivotal component, minimizing human intervention and elevating feeding efficiency. The integration of a Bluetooth module further enhances the system's capabilities, providing a means for remote control and management. Through this innovative approach, our system significantly reduces manual labor, contributing to healthier aquatic environments. By facilitating precise feeding schedules and promoting optimal shrimp growth and well-being, the Automatic Feed Dispenser stands as a technological advancement poised to transform aquaculture practices. © 2024 IEEE.

关键词： Aquaculture

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Benchmarking a ROS 2-Based Indirect SLAM on Edge Devices 4

Benchmarking a ROS 2-Based Indirect SLAM on Edge Devices

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4th international conference on Robotics, automation, and Artificial Intelligence, RAAI 2024

作者： Behret, Valentino Kushtanova, Regina Palme, Frank Munich University of Applied Sciences Laboratory for 3D Measuring Systems and Computer Vision Department of Mechanical Automotive and Aeronautical Engineering Munich Germany

ISBN: (纸本)9798331520038

This study provides insights into SLAM performance on embedded systems and introduces an unsupervised parameter tuning approach for enhanced localisation accuracy. Our paper benchmarks a ROS 2-based indirect Simultaneous Localisation and Mapping (SLAM) system across six NVIDIA Jetson devices: Orin Nano 4 GB, Orin Nano 8 GB, Orin NX 8 GB, Orin NX 16 GB, AGX Orin 32 GB Auvidea, and AGX Orin 32 GB Devkit, using sequences from the KITTI dataset. The edge devices differ significantly in GPU computing power, number of CPU cores, CPU clock frequency as well as memory. SLAM evaluations are conducted within Docker containers to ensure consistency, with feature detection parameters optimised using an unsupervised tuning approach for each device. In addition to localisation accuracy, hardware utilisation is analysed, revealing that less than a half of CPU capabilities and approximately 5- 10 % of GPU power are utilised. The results indicate that in the default configuration devices with lower computing power demonstrate higher fluctuation in SLAM localisation accuracy. However, optimising feature parameters improves accuracy stability, especially for less powerful edge devices, indicating that even lower-powered but more energy-saving devices can achieve similar performance when an optimised balance between feature quantity and runtime constraints is achieved. © 2024 IEEE.

关键词： Graphics processing unit

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Bread Quality Assessment Using Deep Learning with shape and Volume Metrics 10

Bread Quality Assessment Using Deep Learning with shape and ...

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10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Rohan Joel, J. Benjamin, Ruth Moly Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350384369

With the goal to guarantee customer satisfaction and optimize processes for manufacturing in the baking sector, bread quality evaluation is essential. Conventional techniques for evaluating bread quality are frequently time-consuming, subjective, and labor-intensive. In this study, we suggest a novel method for evaluating bread quality that combines shape and volume metrics with deep learning techniques, namely the YOLOv5 algorithm. We enable the YOLOv5 model to properly recognize and categorize bread quality attributes by training it on a dataset that consists of photos of various types of bread with corresponding quality labels. To provide a more thorough assessment of bread quality, we now include shape and volume metrics in addition to the conventional image-based quality assessment. Bakeries may increase productivity, save expenses, and guarantee a constant level of product quality by automating the quality assessment process. Furthermore, the suggested method may be modified for use in tasks other than evaluating the quality of bread, which would promote automation and quality control in the food sector. © 2024 IEEE.

关键词： Food industry

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Navigating the Landscape: A Comprehensive Review of State-of-the-Art Traffic Detection Approaches and Unveiling Open Research Challenges

Navigating the Landscape: A Comprehensive Review of State-of...

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2024 international conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Aeri, Manisha Purohit, Kamlesh C. Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350378092

Traffic management is crucial for the smooth flow of vehicles on the roads. automation in traffic management requires computerized mechanisms for detection of vehicles. With the continuous increase in vehicle population, traffic congestion has become a global issue these days. This has a negative influence on people's lives because of things like the increased commute time, health related issues due to environmental pollution, decreased social life, etc. A significant amount of research has been suggested on this subject to lessen the negative consequences of congestion in our life. Researchers all around the world are paying close attention to the Intelligent Transportation System (ITS), one of the prospective solutions that could enable a variety of applications to enhance travel comfort and safety. Accurate traffic detection is one of the most important issues that must be resolved in ITS to effectively manage the transportation system and alleviate traffic congestion. This survey attempts to provide a thorough overview of the state-of-the-art proposed traffic detection approaches to create the groundwork for understanding the open research challenges in traffic detection. © 2024 IEEE.

关键词： congestion Deep leaning detection Survey traffic management

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