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Artificial Intelligence for Satellite image processing: Application to Rainfall Estimation 1st

Artificial Intelligence for Satellite Image Processing: Appl...

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1st international conference on Intelligent Systems and Applications

作者： Ouallouche, Fethi Labadi, Karim Mohia, Yacine Lazri, Mourad Ameur, Soltane Univ Mouloud MAMMERI Tizi Ouzou Fac GEI Lab LAMPA Lab Anal & Modelisat Phenomenes Aleatoi Tizi Ouzou Algeria ECAM EPMI Quartz Lab Lab LR2E Cergy Pontoise France

ISBN: (纸本)9789811965814;9789811965807

Meteorological satellites are widely used for collecting information about the atmosphere. Due to the indirect relation between satellite data and measurements of meteorological parameters, mathematical models, especially those based on artificial intelligence, have been developed for meteorological modeling. Indeed, in recent years, machine learning has enabled fundamental advances in the modeling of random systems. In this context, we will show the contribution of techniques based on artificial intelligence in the estimation of precipitation. Based on the expertise of our research laboratories in this field, the objective of this paper is to present our recent results and developments using machine learning, such as ANN, SVM, and RF. For the classification and estimation of rainfall intensities, satellite images were used for the implementation of these techniques. The training and validation was carried out by comparing the satellite images to the corresponding radar images. The results of these artificial intelligence-based techniques indicate very interesting performance.

关键词： Weather satellites Intelligence artificial machine learning Precipitation

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Unified Approach Integrating machine learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st international conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector machine (SVM), Natural Language processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

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2025 IEEE international students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.V. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 IEEE.

关键词： image analysis

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Preserving Accuracy in Federated learning via Equitable Model and Efficient Aggregation 6th

Preserving Accuracy in Federated Learning via Equitable Mo...

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Mehdi, Muntazir Makkar, Aaisha Conway, Myra Sama, Lakshit University of Derby DerbyDE22 1GB United Kingdom Capgemini Engineering Adelaide Australia

ISBN: (纸本)9783031530814

machine learning has revolutionized research by extracting complicated patterns from complex data, particularly in healthcare and medical imaging, where accurate diagnosis is critical. The concept of federated learning has gained popularity in the field of machine learning as a viable technique for addressing privacy issues in distributed settings. This research explores federated learning in healthcare, demonstrating its capability to achieve results comparable to centralized data while enhancing the accuracy of deep learning models for clinical data interpretation. To ensure reliable model performance during federated learning rounds, this study introduces a proactive mechanism for coordinating server updates with equitable client modifications. The equitable model, designed to reduce accuracy fluctuations, consistently improves accuracy across multiple training rounds on a non-IID dataset. We achieved smooth accuracy improvement by implementing the novel Equitable model, resulting in robust model development. As healthcare AI continues to advance, federated learning emerges as a critical tool for developing precise prediction models while preserving patient data privacy and aligning with increasingly strict data standards worldwide, such as GDPR regulations. This strategic approach not only promotes ethical, efficient, and secure progress in medical research and practice, but it also emphasizes the importance of protecting patient data privacy while utilizing machine learning’s potential. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clinical research

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Iris Grid image Classification using Naive Bayes for Human Biometric System 1

Iris Grid Image Classification using Naive Bayes for Human B...

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1st international conference on Information System and Information Technology, ICISIT 2022

作者： Wibawa, Adhi Dharma Pamungkas, Yuri Perdana, Muhammad Ilham Rachmatika, Ratih Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Biometrics is a measurement of a person's physical and behavioral characteristics. Iris image is one of many biometrics data such as fingerprint, voice, face, and gait that can be used as an identifier. Iris is the colored part of the eye that helps the pupil see clearly and regulates light entry. Iris recognition is one of the important topics in biometric systems because of its unique pattern. Several related studies have been carried out to automatically obtain the most efficient method to understand and recognize the iris for human verification. This study proposes an analysis of iris images for biometrics systems with effective image processing techniques for system recognition. CVBL Iris image dataset was used in this study with 4320 iris images. After converting the iris image into a rectangle form, the Grid iris image experiment was implemented to find the highest accuracy. Several iris image grid-size were simulated to find the best accuracy. Multinomial Naive Bayes is used as a classifier. The Naive Bayes method is a machine learning method that uses probability calculations (rules-based). This algorithm uses probability and statistical methods, which predict future probabilities based on the previous data. The study results indicate that the proposed method can recognize the iris by identifying its fibers and encoding the fibers data using a grid image approach, with a classification accuracy of 92.37%, using an iris grid size of 70x50 pixels. This research can be useful for developing human biometric systems based on iris with a simple preprocessing approach. © 2022 IEEE.

关键词： Biometrics

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An Effective Automatic recognition Approach of Chinese Herbal Medicine by Micro image processing 21

An Effective Automatic Recognition Approach of Chinese Herba...

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21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on Data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Pang, Guangyao Yu, Zhenming Pan, Honglin Xie, Chengwen Gong, Ping Zhu, Xiaoying Peng, Zizhen Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Wuzhou Univ Guangxi Coll & Univ Key Lab Ind Software Technol Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： image recognition identification of traditional Chinese medicine transfer learning data enhancement microscopic image

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Fake News Investigation Using Ensemble machine learning Techniques 6th

Fake News Investigation Using Ensemble Machine Learning Tech...

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Jain, Jai Dubey, Vansh Sama, Lakshit Kumar, Vimal Singh, Simarpreet Budhiraja, Ishan Arora, Ruchika Bennett University Uttar Pradesh Greater Noida India Capgemini Engineering Melbourne Australia SR University Ananthasagar Telangana Hasanparthy Hanumakonda India

ISBN: (纸本)9783031530814

The classification of any information as true or false has piqued the curiosity of researchers all around the world. Different types of studies are done to document the impact of misleading and fake news on the general public, as well as people’s reactions to such news. Falsified news or fabricated posts are any textual or visual content that is fake/false that is created in order for readers to believe in anything that isn’t true. For instance, a news item headlined "Beasts in White Aprons" was recently circulated on the microblogging platform-Facebook, by an acknowledged reporter from Srinagar, J &K, and many began to believe it, despite the fact that it was completely false. Therefore, the main goal of this research is to apply various machine learning models to distinguish between real and fraudulent news. By using several machine learning models to discriminate between authentic and false news, we add to the expanding body of research on identifying fake news in this work. Our model performs better in scenarios in which there is limited data. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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NeuroAI: Emerging Artificial Intelligence and image processing Techniques in Neuroscience for Enhanced Medical Diagnosis of Brain Tumor 1

NeuroAI: Emerging Artificial Intelligence and Image Processi...

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Bhatt, stuti Sharma, Sachin Bhadula, Shuchi Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350365016

The combination of Artificial Intelligence (AI) and Neuroscience speaks to an advantageous relationship with significant suggestions. AI, a multidisciplinary field in CS which points to imitate insights in machines, whereas Neuroscience explores the brain's structure and cognitive capacities. The union of these disciplines has driven to shared headways. Neuroscience has altogether affected AI, motivating profound neural systems based on natural neural systems. AI exceeds expectations in assignments such as NLP, object detection, and speech recognition. AI, drawing from neuroscience, utilizes support learning standards watched in people and creatures to create autonomous algorithms. AI's capacity to analyze complex information and reveal unapparent design is important for interpreting neuroscience information. AI interfaces can extricate neural signals from the human brain and interpret them into commands for gadgets like automated arms, supporting those with versatility impediments. AI moreover contributes to restorative areas, analyzing neuroimaging information and supporting early discovery of neurological disarranges. This paper conducts a comprehensive audit and usage of the AI and neuroscience cooperative energy, particularly brain tumor, emphasizing their merging for untimely revelation and forecast of neurological clutters. This paper primarily centers on early identifying and localizing the brain tumor locale utilizing division of patient's MRI pictures. © 2024 IEEE.

关键词： image enhancement

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A Minimalistic Model for Converting Basic Cars into Semi-Autonomous Vehicles Using AI and image processing 1

A Minimalistic Model for Converting Basic Cars into Semi-Aut...

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1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Kashyap, Amith Ray, Aniket Kalyan, B. Bhushan, Pushpesh NMIT Dept of ISE Bengaluru India

ISBN: (纸本)9798350323795

Autonomous vehicles are vehicles that can operate without human intervention, and can go anywhere a traditional vehicle can go and perform any task that a skilled human driver can. These vehicles can address environmental and safety concerns by reducing crashes and decreasing reliance on fossil fuels. Human error accounts for 94% of road accidents, which can be significantly reduced by self-driving cars. In our project, we aim to convert any car with basic cruise control features into a semi-autonomous vehicle using image detection and processing, machine learning, and artificial intelligence. Our system will be a minimalistic model of the real-time product, where we aim to achieve functions such as lane switching, sign detection, object detection, and braking. By using this technology, we can create a safer and more sustainable driving experience for everyone. © 2023 IEEE.

关键词： Autonomous vehicles

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An Efficient Real-Time Word-Level recognition of Indian Sign Language 4th

An Efficient Real-Time Word-Level Recognition of Indian Sign...

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

作者： Chaitanya Madhav, R. Shet, Prasad Mohan Murali, K.S. Likhith, R. Mamatha, H.R. Department of Computer Science PES University Karnataka Bengaluru India

ISBN: (纸本)9789819769919

Advancements in technology have catalyzed innovations in machine learning and artificial intelligence, particularly in image recognition. While existing research has predominantly focused on static sign recognition for Sign Language, this study proposes a real-time dynamic sign recognition of Indian Sign Language. Given the fundamental importance of communication, especially for the hearing and speech-impaired, this research addresses the critical need for instant translation of sign language into text. To resolve this communication gap, this research introduces a comprehensive dataset using references from the "Indian Sign Language Research and Training Centre New Delhi" an official government-backed YouTube Channel dedicated to Indian Sign Language, which standardizes Indian Sign Language across the country. The dataset comprises 60 signs encompassing verbs, animals, vegetables, legal terms, fruits, emotions and more. The authors utilized Mediapipe as a feature extractor to train a total of nine models, including seven deep learning models namely LstM, CNN, GRU, Bidirectional LstM, stacked LstM, Hybrid CNN-GRU, and Convolutional LstM, along with two machine learning models, namely Random Forest and XGBoost. Notably, the Convolutional LstM model achieved the highest accuracy of 99.88%. Reliable evaluation metrics like Mean Average Precision, Confusion Matrix, Accuracy, F1-Score were employed, which shows the correctness of the signs predicted and ensures the precision of dynamic sign recognition. The culmination of this research results in a real-time system capable of instantaneously identifying dynamic signs. This contribution spans diverse domains such as pattern recognition, Computer Vision, Video recognition, etc., addressing key challenges like the need for gloves or additional sensors, the requirement for many videos to train a single sign, utilization of simple signs, high processing power demand, signer dependency, occlusions, pushing the boundaries of sig

关键词： Deep learning

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