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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gowda, Dankan v. Kumar, Pullela S.v.v.S.R. Prasad, K.D.v. Ashreetha, B. Kumar, Mekala Bharath Karthikeya, Karanam Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of Computer Science & Engineering Aditya College of Engineering Andhra Pradesh Surampalem India Symbiosis Institute of Business Management Hyderabad India Pune India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798350361155

The difficulty in differentiating between the normal and cancerous cells in the brain through the frequent magnetic resonance imaging approaches is one of the major obstacles to realization of diagnostic precision. The presented work reveals a new MRI image processing technology, which includes an original software that contains complex algorithms and trained machine learning models, as the programs that make the images much better than before. Carefully calibrated computer vision dataset which features brain scans is subjected to the well-defined novel approach, whose performance is compared with the traditional MRI methods by calculating multiple metrics such as classification accuracy, sensitivity, specificity, ROC are and so on. This paper is the climax of a deep cognition of various cell types revealed by the newest MRI method which presents a better contrast than the old techniques. Next, the quality in the detection and recognition of the tumour after, comparison of these modalities displays that the modality of higher resolution, the ability to detect the tumour earlier and better. Such technological improvements in MRI machines will enable the surgeons to identify the growth of tumors at the early stages that will lay the right groundwork for the design of personalized treatment plans and also will have positive impact on the lives of the patients. Through this effect, the level of quality followed by MR imaging has been improved as well as arising new alliances between major imaging companies and machine learning technologies. It can be thought as the border - eraser diagnostics and imaging which will obey medical laws. Therefore, it states that the present hypothetical world should be improved while the advanced and proven diagnostics systems should be developed. The daily clinical applications of these advanced MRIs may well be the beginning of a new era in diagnostic oncology, which will be a very important way forward in improving treatment combined wi

关键词： Magnetic resonance imaging

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Automatic target detection utilizing an Edge IR vision transformer (EIR-viT) 33

Automatic target detection utilizing an Edge IR vision trans...

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Conference on Automatic Target Recognition XXXIII

作者： Adams, Ethan R. Depoian, Arthur C., II Kurz, Aidan G. Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510661561;9781510661578

The detection and recognition of targets within imagery and video analysis is vital for military and commercial applications. The development of infrared sensor devices for tactical aviation systems imagery has increased the performance of target detection. Due to the advancements of infrared sensors capabilities, their use for field operations such as visual operations (visops) or reconnaissance missions that take place in a variety of operational environments have become paramount. Many techniques implemented stretch back to 1970, but were limited due to computational power. The AI industry has recently been able to bridge the gap between traditional signal processing tools and machine learning. Current state of the art target detection and recognition algorithms are too bloated to be applied for on ground or aerial mission reconnaissance. Therefore, this paper proposes Edge IR vision Transformer (EIR-viT), a novel algorithm for automatic target detection utilizing infrared images that is lightweight and operates on the edge for easier deployability.

关键词： Automatic Target Detection vision Transformer Infrared Imaging Object Detection FLIR image FLIR dataset Edge Computing

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Granular Privacy Control for Geolocation with vision Language Models

Granular Privacy Control for Geolocation with Vision Languag...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Mendes, Ethan Chen, Yang Hays, James Das, Sauvik Xu, Wei Ritter, Alan Georgia Institute of Technology United States Carnegie Mellon University United States

ISBN: (纸本)9798891761643

vision Language Models (vLMs) are rapidly advancing in their capability to answer information-seeking questions. As these models are widely deployed in consumer applications, they could lead to new privacy risks due to emergent abilities to identify people in photos, geolocate images, etc. As we demonstrate, somewhat surprisingly, current open-source and proprietary vLMs are very capable image geolocators, making widespread geolocation with vLMs an immediate privacy risk, rather than merely a theoretical future concern. As a first step to address this challenge, we develop a new benchmark, GPTGEOCHAT, to test the capability of vLMs to moderate geolocation dialogues with users. We collect a set of 1,000 image geolocation conversations between in-house annotators and GPT-4v, which are annotated with the granularity of location information revealed at each turn. Using this new dataset we evaluate the ability of various vLMs to moderate GPT-4v geolocation conversations by determining when too much location information has been revealed. We find that custom fine-tuned models perform on par with prompted API-based models when identifying leaked location information at the country or city level, however fine-tuning on supervised data appears to be needed to accurately moderate finer granularites, such as the name of a restaurant or building. © 2024 Association for Computational Linguistics.

关键词： visual languages

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applications of Topological Data Analysis to Natural Language processing and Computer vision

Applications of Topological Data Analysis to Natural Languag...

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作者： Garcia, Jason S. Colorado State University

学位级别：M.S., Master of Science/Master of Surgery

Topological Data Analysis (TDA) uses ideas from topology to study the "shape" of data. It provides a set of tools to extract features, such as holes, voids, and connected components, from complex high-dimensional data. This thesis presents an introductory exposition of the mathematics underlying the two main tools of TDA: Persistent Homology and the MAPPER algorithm. Persistent Homology detects topological features that persist over a range of resolutions, capturing both local and global geometric information. The MAPPER algorithm is a visualization tool that provides a type of dimensional reduction that preserves topological properties of the data by projecting them onto lower dimensional simplicial complexes. Furthermore, this thesis explores recent applications of these tools to natural language processing and computer vision. These applications are divided into two main approaches: In the first approach, TDA is used to extract features from data that is then used as input for a variety of machine learning tasks, like image classification or visualizing the semantic structure of text documents. The second approach, applies the tools of TDA to the machine learning algorithms themselves. For example, using MAPPER to study how structure emerges in the weights of a trained neural network. Finally, the results of several experiments are presented. These include using Persistent Homology for image classification, and using MAPPER to visual the global structure of these data sets. Most notably, the MAPPER algorithm is used to visualize vector representations of contextualized word embeddings as they move through the encoding layers of the BERT-base transformer model.

关键词： Language processing Computer vision Topological data analysis machine learning algorithms

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A vision transformer approach for fusarium wilt of chickpea classification

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Multimedia Tools and applications 2024年 1-18页

作者： Erbay, Hasan Hayit, Tolga Computer Engineering Department Ostim Technical University 100. Yıl Blv Ankara06374 Turkey Department of Computer Engineering Yozgat Bozok University Ataturk Road 7th km. Yozgat66900 Turkey

Fusarium wilt disease(FWD) caused by Fusarium oxysporum f. sp. ciceris (Padwick) is the most important disease affecting chickpea yield among biotic stresses. Fusarium wilt is a vascular disease that causes permanent lack of productivity in seeds and soil, manifests itself with structural symptoms such as wilting, drooping, dull green discoloration, yellowing, browning of the vascular system, and eventually causes the collapse of the entire plant. These symptoms can be seen 20-30 days after planting and can cause yield losses of up to 90% if necessary precautions are not taken. Although control of FWD in chickpea is challenging, early detection of FWD is critical for effective control and prevention of yield losses. Thus, advanced detection mechanisms need to be implemented to determine the severity level of FWD. Traditional machine learning algorithms based on hand-crafted features normally have poor performance due to their limited ability to represent complex plant features. Herein, vision Transformers (viTs), which are state-of-the-art image processing methods, were employed in determining the infection type of FWD in chickpea. Pre-trained viTs such as viT_L_16, viT_L_32 and viT_H_14 were adopted, fine-tuned and tested. The reliability of the models was measured through both statistical performance measures and performance curves. Among the models, viT_H_14 based model performed the best, followed by viT_L_16 based. Although the overall accuracy of viT_H_14 based model is 83%, it classifies Highly-Susceptible as 97% and Highly-Resistant as 85%. Moreover, the overall specificity of viT_H_14 is 96%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Plant diseases

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Review Study on Recent Developments in Fire Sensing Methods

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IEEE ACCESS 2023年 11卷 90269-90282页

作者： Sulthana, S. Fouziya Wise, Cross T. Asha Ravikumar, C. v. Anbazhagan, Rajesh Idayachandran, G. Pau, Giovanni SRM Inst Sci & Technol Fac Engn & Technol Dept Mechatron Engn Kattankulathur 603203 Tamil Nadu India Vellore Inst Technol Sch Elect Engn Vellore 632014 India SASTRA Univ Sch Elect & Elect Engn Thanjavur 613401 India Vellore Inst Technol Sch Elect Engn Chennai 600127 India Kore Univ Enna Fac Engn & Architecture I-94100 Enna Italy

Scientific society has envisioned a considerable advancement in various fire detection methods due to the development in the field of machine learning, information technology, sensors, and signal processing technology. These intelligent processing technologies help in reducing the detection time and false alerts from the sensors. Over the past few decades, there is substantial improvement in the computing power of computers and a decrease in the cost of image sensors, enabling video-based fire detection technology for real-time applications. The ability to differentiate between fire and non-fire threats is improved with the development of the Internet of Things (IoT) or Wireless Sensor Networks (WSN). Unmanned Aerial vehicles (UAvs) are becoming a more realistic solution for monitoring and detecting fire due to their remote sensing capabilities. This paper summarizes various fire detection methods and the technologies behind them. The issues related to the present fire detection methods and future research initiatives are discussed. The primary aspects of the fire signatures like flame, smoke, ambient temperature, and surrounding gaseous levels concerning different sensors are analyzed with their benefits and drawbacks based on evaluating a range of parameters.

关键词： Sensors Detectors Heating systems Fires Temperature sensors Smoke detectors Gas detectors Fire sensors smoke detectors flame detectors heat detectors gas detector

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Advanced machine Learning Frameworks for Multimodal image and Speech Signal processing 3

Advanced Machine Learning Frameworks for Multimodal Image an...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ramesh Naidu, P. Shandilya, Saurabh Srividya, C.N. Gujar, Shantanu Sudhir Devi, Supriya Dankan Gowda, v. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560064 India Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India BGSIT Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of ECE B G Nagara571448 India Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of CyberSecurity RochesterNY14623 United States Alliance University Department of Mathematics Karnataka Bangalore562106 India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354171

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework;the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework's efficiency in predicting the multimodal input and demonstrate superiority over the prior methods. © 2024 IEEE.

关键词： Deep learning

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Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

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EJNMMI REPORTS 2024年第1期8卷 7页

作者： Alyami, Jaber King Abdulaziz Univ Fac Appl Med Sci Dept Radiol Sci Jeddah 21589 Saudi Arabia King Abdulaziz Univ King Fahd Med Res Ctr Jeddah 21589 Saudi Arabia King Abdulaziz Univ Smart Med Imaging Res Grp Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Modern Math Sci & its Applicat Med Imaging & Artificial Intelligence Res Unit Jeddah 21589 Saudi Arabia

Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.

关键词： Radiological images MRI Analysis Clinical research applications Cancer diagnosis Multi-organs Biopsy

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BIOINTEL: Real-Time Bacteria Identification Using Microscopy Imaging 21

BIOINTEL: Real-Time Bacteria Identification Using Microscopy...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Ramesh, Hariharan Elshinawy, Abdelrahman Ahmed, Ahmed Kassoumeh, Mohammed Adnan Khan, Muhammad Mounsef, Jinane Rochester Inst Technol Dept Elect Engn Dubai U Arab Emirates

ISBN: (纸本)9798350313345;9798350313338

In this paper, we present a novel approach to bacteria type prediction using advanced machine learning (ML) techniques, focusing on five common bacterial strains in natural environments. Unlike traditional methods that are labor-intensive and time-consuming, our solution employs state-of-the-art convolutional neural networks (CNNs), including vGG16, ResNet50, ResNet100, Inception v3, Lenet5, EfficientNet, and ConvNeXt, for rapid and accurate bacteria identification from images. We detail our methodology for image processing and model training, showcasing how these models achieve up to 99% accuracy. Our evaluation includes comprehensive testing on a highly diverse bacterial dataset, and comparisons with existing methods, emphasizing the improvements in accuracy. Additionally, we discuss the potential integration of this technology into a user-friendly device, attachable to standard smartphones, for realtime bacterial detection and reporting. This work demonstrates a significant advancement in applying computer vision for realtime image analysis in complex biological samples.

关键词： computer vision microscopy imaging bacteria detection real-time identification automated diagnosis

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Learning Enriched Features for Fast image Restoration and Enhancement

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第2期45卷 1934-1948页

作者： Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Yang, Ming-Hsuan Shao, Ling Incept Inst Artificial Intelligence Abu Dhabi 54114 U Arab Emirates Mohammed BinZayed Univ Artificial Intelligence Abu Dhabi 54114 U Arab Emirates Monash Univeristy Melbourne Vic 3800 Australia Univ Calif Merced Merced CA 95343 USA Google Mountain View CA 94043 USA Terminus Grp Beijing Peoples R China

Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computational photography, surveillance, autonomous vehicles, and remote sensing. Significant advances in image restoration have been made in recent years, dominated by convolutional neural networks (CNNs). The widely-used CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatial details are preserved but the contextual information cannot be precisely encoded. In the latter case, generated outputs are semantically reliable but spatially less accurate. This paper presents a new architecture with a holistic goal of maintaining spatially-precise high-resolution representations through the entire network, and receiving complementary contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing the following key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) non-local attention mechanism for capturing contextual information, and (d) attention based multi-scale feature aggregation. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on six real image benchmark datasets demonstrate that our method, named as MIRNet-v2, achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at https://***/swz30/MIRNetv2.

关键词： Multi-scale feature representation dual-pixel defocus deblurring image denoising super-resolution low-light image enhancement and contrast enhancement

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