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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rajiv Dwivedi, Avinash Joshi, Shivani Tripathi, Vikas Chandra Nvidia Ai Learning & Research Center G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments. © 2024 IEEE.

关键词： Brain Tumor Classification Convolutional Neural Network identification Image Augmentation MRI Scans

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Handwritten Paragraph Text Recognition using OCR 7

Handwritten Paragraph Text Recognition using OCR

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Venkateshwarlu, M. Javeed, Sk. Shreya, S. Muthukumaran, N. Vilas, B. Pranay Vignan Institute of Technology And Science Department of Computer Science And Engineering Telangana Nalgonda508284 India Sri Eshwar College of Engineering Centre For Computational Imaging And Machine Vision Department of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359299

The recognition of unlimited signatures is an ongoing challenge for computer vision systems. Traditionally, in paragraph text recognition, two separate models are used for character splitting and text character recognition. In this regard, this study proposes an integrated end-to-end framework and a new hybrid thinking approach to address the existing challenge. The prototype is specifically intended to handle a splitting picture per align iteratively, with three different modules. Initially, the encoder creates maps of features derived from the whole passage picture. Then, a concept module produces typically a mask with weights, which enables focused analysis of current text line features, especially implicit line splitting Finally, the decoder module is used for performing spectrum detection in each text line feature, ending with comprehensive detection of the entire paragraph offers a new solution. © 2024 IEEE.

关键词： Character recognition

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A Novel Transfer Learning Approach for Eye Tumour Detection using Pre-Trained CNN Models 3

A Novel Transfer Learning Approach for Eye Tumour Detection ...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Prabaharan, G. Rajasekar, Vani Sarveshwaran, Velliangiri B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narasapur India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Erode India Srm Institute of Science and Technology Department of Computational Intelligence TamilNadu Kattankulathur India

ISBN: (纸本)9798350370867

According to a recent study, using retinal imaging to identify the tumour in the eye is difficult. When an image is analyzed, the affected region of the lesion impacted retinal picture is not discovered in the conventional system due to uneven illumination, low contrast, and blurring, which are symptoms of an eye disease. The proposed methodology was created to identify the eye tumour in the earlier stage with enhanced image quality and increased accuracy. The conventional system has a few drawbacks such as lower accuracy and more computational time to analyze the image. Convolutional neural networks (CNN) have recently demonstrated its result of their capacity to learn various category image denotions that aid in image classification in various sectors. These networks have a tremendous ability to extract the proper features from input images with training on millions of images, which produces accurate categorization. The deep learning-based transfer learning approach is suggested for earlier detection of eye tumours. The five pre-trained models such as AlexNet, ResNet, DensNet, VGG 19 and Inception model were used for efficient eye tumour classification. The performance analysis of the proposed approach has shown that higher accuracy of 98.63% and this model shows enhanced performance in better classification. © 2023 IEEE.

关键词： Tumors

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Deep Fake Detection Using computer Vision-Based Deep Neural Network with Pairwise Learning

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Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

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Classification of Leukemia White Blood Cell Cancer 7

Classification of Leukemia White Blood Cell Cancer

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Vineela, M. Reddy, Gudibandi Durga Sravani Karthik, Ganji Muthukumaran, N. Al Deen, Shaik Hussam Vignan Institute of Technology And Science Department of Computer Science And Engineering Telangana Nalgonda508284 India Sri Eshwar College of Engineering Centre For Computational Imaging And Machine Vision Department of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359299

Leukemia, a pervasive blood cancer, poses a significant global health challenge. This research study presents a comprehensive system for the classification of leukemia white blood cell cancer, utilizing advanced Image Processing and Machine Learning techniques implemented in MATLAB. Achieving a remarkable accuracy rate of 98%, the system employs the K-Nearest Neighbors (KNN) algorithm to analyze distinctive features extracted from leukemia cell images. The workflow encompasses key stages including Image Capture, Data Enhancement, Cell Isolation, Feature Identification, and Categorization. Preprocessing techniques such as normalization, contrast enhancement, and noise removal enhance the quality of input data, while segmentation methods reveal cancer cell nucleus regions with precision. Feature extraction encompasses statistical color features and texture features, facilitating precise classification into leukemia cell types such as ALL-L1, ALL-L2, AML-M2, and AML-M5. Additionally, the system provides audio outputs corresponding to identified leukemia cancer types, enhancing interpretability and usability. This paper plays a pivotal role in advancing precise leukemia diagnosis by emphasizing the potential of integrated image processing and machine learning methodologies to address critical healthcare challenges. © 2024 IEEE.

关键词： Diseases

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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Exploring the Predictive Capabilities of AlphaFold Using Adversarial Protein Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3384-3392页

作者： Alkhouri, Ismail R. Jha, Sumit Beckus, Andre Atia, George Jha, Susmit Ewetz, Rickard Velasquez, Alvaro OrlandoFL32816 United States Michigan State University Computational Mathematics Science and Engineering Department East LansingMI48824 United States University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI48109 United States Florida International University Knights Foundation School of Computing and Information Sciences MiamiFL33199 United States Air Force Research Laboratory Rome Information Directorate NY13441 United States Department Computer Science Department OrlandoFL32816 United States SRI International Computer Science Laboratory Menlo ParkCA94025 United States Electrical and Computer Engineering Department OrlandoFL32816 United States University of Colorado Computer Science Department BoulderCO80309 United States

Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been fully explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations to noncritical residues of a protein sequence do not typically lead to drastic changes in the protein structure. Our study demonstrates that, similar to adversarial methods in machine learning, small changes to protein sequences can result in significant differences in the predicted protein structures using AlphaFold as determined by large distance measures. Despite this, our findings using multiple protein sequences suggest that AlphaFold is able to accurately predict the domain structure and folding regions of a protein. To gauge structural differences, we employ two alignment-based measures [root-mean-square deviation (RMSD) and the global distance test (GDT) similarity], and one alignment-free measure, which is an effective graph-based structure representation (GraSR) method. We prove that the problem of minimally perturbing protein sequences is NP-complete. Based on the well-established block substitution matrices (BLOSUM62) sequence alignment scoring matrix, we generate adversarial sequences. In our experimental evaluation, we consider 111 proteins (including 29 COVID-19 sequences) in the Universal Protein resource (UniProt), a central resource for protein data. Our findings suggest that, despite the high RMSD values returned by AlphaFold, it is capable of handling the BLOSUM adversarial sequences considered in our analysis, as evidenced by the preservation of the folded regions and the GraSR results. © 2020 IEEE.

关键词： Neural networks

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HRNetO:Human Action Recognition Using Unified Deep Features Optimization Framework

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computers, Materials & Continua 2023年第4期75卷 1089-1105页

作者： Tehseen Ahsan Sohail Khalid Shaheryar Najam Muhammad Attique Khan Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Riphah International UniversityPeshawar Rdnear Hajj ComplexI-14IslamabadIslamabad Capital Territory46000Pakistan Departmnt of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action *** is more appealingbecause it has a wide range of applications in computer vision,such asvideo surveillance and smart *** attempts have been made in theliterature to develop an effective and robust framework for ***,theprocess remains difficult and may result in reduced accuracy due to severalchallenges,such as similarity among actions,extraction of essential features,and reduction of irrelevant *** this work,we proposed an end-toendframework using deep learning and an improved tree seed optimizationalgorithm for accurate *** proposed design consists of a *** the first step,frame preprocessing is *** the second step,two pre-trained deep learning models are fine-tuned and trained throughdeep transfer learning using preprocessed video *** the next step,deeplearning features of both fine-tuned models are fused using a new ParallelStandard Deviation Padding Max Value *** fused features arefurther optimized using an improved tree seed algorithm,and select the bestfeatures are finally classified by using the machine learning *** was carried out on five publicly available datasets,including UTInteraction,Weizmann,KTH,Hollywood,and IXAMS,and achieved higheraccuracy than previous techniques.

关键词： Action recognition features fusion deep learning features selection

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey 17

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chaudhuri, Arunava Shukla, Shubhi Bhattacharya, Sarani Mukhopadhyay, Debdeep Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur India

ISBN: (纸本)9798331531195

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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