| Paper ID |
IJIFR/V12/E12/004
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| Author |
J. M. Mahadevan
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| Paper Title |
An Analytical Review on GAN-based Image Dehazing models for Real world Hazy images
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| Subject Category |
Computer Engineering
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| Abstract |
Performance of machine vision systems is highly affected by Haze. Due to reducing scene visibility, which means that it reduces performance in various tasks such as detecting objects, tracking targets and segmenting the scene, so image dehazing is widely considered a key area of machine vision. Removing haze reduces noise, enhance edge clarity, the ratio signal-to-noise improves, and the overall robustness and performance of the machine vision system are enhanced. Due to these benefits, image dehazing is very popular across numerous applications, such as security surveillance, driver assistance systems, weather analysis, and geospatial information analysis. This paper presents a series of dehazing techniques, and explores in particular the GAN-based approach. These GAN-based approaches have been introduced to resolve the issues facing by image dehazing. GANs use a distribution of haze free images for removal of haze from degraded one and improve computer vision performance in hazy situations.GAN-based image dehazing is widely accepted as the best method for real time dehazing. The present paper qualitatively analyses different approaches and discusses their strengths and weaknesses. In addition, this review discusses the mainstream benchmarks, existing challenges, performance metrics and future potential in image dehazing research.Inside the review paper Section 2 provides and evaluates existing dehazing methods, which are classified into four main categories. Section 3 provides an overview of Generative Adversarial Networks (GANs). Sections 4 & 5 provide existing and GAN-based image dehazing methods. Section 6 describes conventional Losses in GAN-based image dehazing. Section 7 organizes and classifies image dehazing datasets into three types. Sections8 and 9provide a brief overview of evaluation metrics and results used in image dehazing. Finally, Sections10 and 11discuss the key challenges and conclusion of paper.
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| Keyword |
Image Dehazing, Atmospheric scattering model (ASM), Generative Adversarial Network (GAN), Losses, Evaluation metrics
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| Paper ID |
IJIFR/V12/E12/003
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| Author |
Rajshree Shanmugam
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| Paper Title |
Reform and Practice of Automatic Control Principle Course under the Certification of Engineering Education
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| Subject Category |
Information Engineering
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| Abstract |
The professional certification of engineering education is an internationally recognized quality assurance system of engineering
education, and also an important basis for achieving international mutual recognition of engineering education and engineer
qualifications. The Washington Agreement advocates three major educational concepts of student-centered, output-oriented and
continuous improvement, highlighting students' ability to solve complex projects. Engineering majors who have passed the
certification of engineering education should not only deeply understand and grasp complex engineering problems, but also
cultivate students' ability to solve complex engineering problems according to the international principle of substantial
equivalence.
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| Keyword |
Automatic control principle, Engineering education professional certification, Teaching content
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| Paper ID |
IJIFR/V12/E12/002
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| Author |
V. N. Ambade,
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| Paper Title |
OpenAI looks to promote ‘healthy use’ of ChatGPT with mental health updates
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| Subject Category |
Computer Engineering
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| Abstract |
OpenAI’s new efforts to promote the healthy use of ChatGPT come against the backdrop of an alarming number of users turning to AI chatbots for therapy and professional advice. This emerging trend has sparked concerns among mental health professionals who have cautioned that AI chatbots may not be equipped to offer appropriate guidance and may end up having an amplifying effect on some users’ delusions as they are designed to generate outputs that please users. this paper study the present issues related to the Open AI & Chat GPT
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| Keyword |
Chat GPT, Open AI, Amplifying effect
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| Paper ID |
IJIFR/V12/E12/001
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| Author |
J S ABHISHEK
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| Paper Title |
Robotic Process Automation (RPA): Overview and Build a Robot
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| Subject Category |
Robotic Engineering
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| Abstract |
Robotics and automation continue to transform industries, from manufacturing to healthcare. Collaborative robots, or cobots, are working alongside humans in factories, enhancing productivity and safety. In warehouses, autonomous mobile robots are optimising logistics operations, while surgical robots are enabling minimally invasive procedures with unprecedented precision. In agriculture, robotic systems are automating planting, harvesting, and crop monitoring. Drones are finding applications in everything from package delivery to search and rescue operations. Meanwhile, software robots, or bots, are automating complex administrative tasks in offices around the world. As artificial intelligence becomes more sophisticated, these robotic systems are becoming increasingly autonomous and adaptable.
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| Keyword |
ndustrial robots, collaborative robots (cobots), autonomous mobile robots Key companies: Boston Dynamics, ABB, FANUC, Universal Robots, iRobot Notable applications: Manufacturing, logistics, healthcare, exploration
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