Relevant bibliographies by topics / DeepaK Ganesh

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'DeepaK Ganesh'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "DeepaK Ganesh"

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Shivaprakash, H. S., Manorama Tripathy, J. Sreenivasamurthy, and Manu V. Devadevan. "Book Reviews." Indian Theatre Journal 3, no. 1 (2019): 57–65. http://dx.doi.org/10.1386/itj_00006_5.

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Kannada Theatre History 1850–1950: A Sourcebook, K. V. Akshara (ed.), B. R. Venkataramana Aithala and Deepa Ganesh (2018)Manipal: Manipal University Press, 288 pp.,ISBN 978-93-82460-84-8, p/bk, Rs. 250Theatre of the Earth – The Works of Heisnam Kanhailal: Essays and Interviews, Heisnam Kanhailal (2016)Calcutta: Seagull Books, 235 pp.,ISBN 978-8-17046-353-5, p/bk, Rs. 450Shakespeare: Kannada Spandana (in Kannada), Nataraj Huliyar (ed.) (2016)Bangalore: Kuvempu Bhasha Bharati Pradhikara, 330 pp.,ISBN 555-1-23408-868-3, p/bk, Rs. 150Chathirangam (in Malayalam), Ashok D’Cruz (ed.), C. K. Namboothi
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Yadaigiri, Ganesh, Kalpana Deepa Priya Dorayappan, Lakshmi Narasimhan Chakrapani, et al. "Abstract 1772: A novel small molecule inhibitor, HO-4200, modulates macrophage polarization and suppresses MDSCs to enhance antitumor immune responses in ovarian cancer." Cancer Research 85, no. 8_Supplement_1 (2025): 1772. https://doi.org/10.1158/1538-7445.am2025-1772.

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Abstract Introduction: Ovarian cancer (OC) remains the deadliest gynecologic malignancy, with minimal improvement in the 5-year survival rate over the past three decades despite advancements in treatment. High-grade serous ovarian cancer (HGSOC) is characterized by aggressive progression, driven by elevated secretion of extracellular vesicles (EVs), tumor-associated macrophage (TAM) activation, and increased myeloid-derived suppressor cell (MDSC) populations. These factors contribute to immune suppression, metastasis, and poor patient outcomes. Here, we report on a novel small-molecule inhibit
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Chakrapani, Lakshmi Narasimhan, Kalpana Deepa Priya Doraiyappan, Ganesh Yadagiri, et al. "Abstract 3327: Obesity-mediated extracellular vesicle secretion: A potential target for preventing endometrial hyperplasia and cancer initiation." Cancer Research 85, no. 8_Supplement_1 (2025): 3327. https://doi.org/10.1158/1538-7445.am2025-3327.

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Abstract Introduction: Endometrial cancer (EC) is the most common gynecologic malignancy in the U.S., with obesity contributing to 57% of cases. This study investigates the molecular mechanisms linking obesity to EC, focusing on extracellular vesicle (EV) secretion and oncogenic protein regulation in adipose and uterine tissues. Understanding these pathways could lead to innovative preventive and therapeutic strategies for obesity-related EC. Methods: Endometrial hyperplasia and cancer were induced in immunocompetent mice using high-fat diets (HFD; 45% or 60% kcal from fat) for 25 weeks. Molec
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Deepak, G., A. Dinakara Rao Dr., and Babu Khan Mohammad. "Identifying Potential Bace-1 Enzyme Inhibitors /Lead Small Molecules: Relevance to Alzheimer's Diseases." Identifying Potential Bace-1 Enzyme Inhibitors /Lead Small Molecules: Relevance to Alzheimer's Diseases 10, no. 10 (2022): D586—D587. https://doi.org/10.6084/m9.figshare.21436593.

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   It is unclear what causes Alzheimer's disease. However, genetic data support the amyloid hypothesis, which states that abnormal beta-amyloid protein aggregation initiates the illness process. There is a lengthy pre-clinical stage of Alzheimer's disease. The terrible consequences of amyloidogenic diseases like Alzheimer's. More than 7000 tiny molecules have been screened from ZINC subsets and exposed to 165 compounds and 16. From a research perspective, I have examined numerous databases like the ZINC Subset, PubChem compound database, and Drug bank database. For dockin
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P., Kamakshi Thai, Kalige Sathvik, Nikhil Ediga Sai, and Chougoni Lokesh. "A survey on deepfake detection through deep learning." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 2214–17. https://doi.org/10.5281/zenodo.14175452.

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Imagine watching a video where Tom Hanks delivers a rousing speech, but you suspect it might be fabricated. This growing concern stems from the rise of "DeepFakes," hyper realistic manipulated videos created using deep learning algorithms. These tools can seamlessly stitch together faces, voices, and body movements, blurring the lines between reality and fiction. While DeepFakes hold promise for entertainment and creative expression, their potential for misuse is significant. Malicious actors could leverage them to spread misinformation, damage reputations, or even influence elections. Thankfu
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Ambu, Karthik, Shetty Jyoti, G. Shobha, and Dev Roger. "Implementation of generative adversarial networks in HPCC systems using GNN bundle." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 374–81. https://doi.org/10.11591/ijai.v10.i2.pp374-381.

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HPCC systems, an open source cluster computing platform for big data analytics consists of generalized neural network bundle with a wide variety of features which can be used for various neural network applications. To enhance the functionality of the bundle, this paper proposes the design and development of generative adversarial networks (GANs) on HPCC systems platform using ECL, a declarative language on which HPCC systems works. GANs have been developed on the HPCC platform by defining the generator and discriminator models separately, and training them by batches in the same epoch. In ord
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Vishnu, Asutosh Dasu, and Manohar T.K. Midhush. "[Re] GANSpace: Discovering Interpretable GAN Controls." ReScience C 8, no. 2 (2022): #10. https://doi.org/10.5281/zenodo.6574645.

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Evaline, Bosch, Ettes Rutger, Korporaal Daan, and van Meer Gijs. "[Re] Replication study of 'Explaining in Style: Training a GAN to explain a classifier in StyleSpace'." ReScience C 8, no. 2 (2022): #21. https://doi.org/10.5281/zenodo.6574667.

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Joseph, Nnaemeka Chukwunweike, Yussuf Moshood, Okusi Oluwatobiloba, Oluwatobi Bakare Temitope, and J. Abisola Ayokunle. "The role of deep learning in ensuring privacy integrity and security: Applications in AI-driven cybersecurity solutions." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 1778–90. https://doi.org/10.5281/zenodo.14865286.

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This article explores the critical role of deep learning in developing AI-driven cybersecurity solutions, with a particular focus on privacy integrity and information security. It investigates how deep neural networks (DNNs) and advanced machine learning techniques are being used to detect and neutralize cyber threats in real time. The article also considers the implications of these technologies for data privacy, discussing the potential risks and benefits of using AI to protect sensitive information. By examining case studies and current research, the piece provides insights into how organiz
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Diljith, M. S., C. P. Emilyn, Afitha Abu T. Fathimathul, and KS Salkala. "Deepfake Technology: An Overview, Applications, Detection, and Future Challenges." Journal of Advancement in Architectures for Computer Vision 1, no. 1 (2025): 45–53. https://doi.org/10.5281/zenodo.15152176.

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<em>Deepfake technology, powered by artificial intelligence, has revolutionized digital media by enabling the creation of highly realistic synthetic videos, images, and audio. While it offers numerous benefits in fields such as entertainment, education, and accessibility, deepfake technology also raises significant ethical, legal, and security concerns. This report explores the methods used to generate deepfakes, including Generative Adversarial Networks (GANs) and autoencoders, and highlights key deepfake techniques such as face-swapping, lip-syncing, and voice cloning. It further examines bo
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Dissertations / Theses on the topic "DeepaK Ganesh"

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Ma, Yufeng. "Going Deeper with Images and Natural Language." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/99993.

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One aim in the area of artificial intelligence (AI) is to develop a smart agent with high intelligence that is able to perceive and understand the complex visual environment around us. More ambitiously, it should be able to interact with us about its surroundings in natural languages. Thanks to the progress made in deep learning, we've seen huge breakthroughs towards this goal over the last few years. The developments have been extremely rapid in visual recognition, in which machines now can categorize images into multiple classes, and detect various objects within an image, with an ability t
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Lacan, Alice. "Transcriptomics data generation with deep generative models." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG010.

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Cette thèse explore l'utilisation de modèles génératifs profonds pour améliorer la génération de données transcriptomiques, répondant aux défis de rareté des données dans la classification de phénotypes de cancers. Nous évaluons la capacité des Autoencodeurs Variationnels (VAEs), des Réseaux Antagonistes Génératifs (GANs) et des modèles de diffusion (DDPM/DDIM) à équilibrer réalisme et diversité sur des données tabulaires de haute dimension. Nous avons d'abord adapté des métriques d'évaluation, supervisées et non supervisées. Nous avons ensuite intégré un moduled'auto-attention basé sur les conn
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Yellapantula, Sudha Ravali. "Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91460.

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In the thesis, we aimed at building a robust UAV(drone) detection algorithm through which, one drone could detect another drone in flight. Though this was a straight forward object detection problem, the biggest challenge we faced for drone detection is the limited amount of drone images for training. To address this issue, we used Generative Adversarial Networks, CycleGAN to be precise, for the generation of realistic looking fake images which were indistinguishable from real data. CycleGAN is a classic example of Image to Image Translation technique, and we this applied in our situation wher
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Lai, Matteo. "Conditional MR image synthesis with Auxiliary Progressive Growing GANs." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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L'addestramento di algotritmi di deep learning (DL) richiede una grande quantità di dati, che però spesso non sono disponibili in ambito medico. In questa tesi viene proposto un modello per la generazione di dataset sintetici etichettati nell'ambito dell'imaging medico ad alta risoluzione. Dopo aver presentato vantaggi e limiti dell'uso delle tecniche di DL in radiologia, vengono proposte le Generative Adversarial Networks (GANs) come possibile soluzione per superare tali limiti. Illustrando lo stato dell'arte relativo alle GAN, viene focalizzata l'attenzione sulle Progressive Growing GAN, ca
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Xing, Luo Oscar. "Deep Learning for Speech Enhancement : A Study on WaveNet, GANs and General CNN-RNN Architectures." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260351.

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Clarity and intelligiblity are important aspects of speech, especially in a time of misinformation and mistrust. The breakthrough in generative models for audio files has brought massive improvements for speech enhancement. Google’s WaveNet architecture has been modified for noise reduction in a model called WaveNet denoising and has proven to be state-of-the-art. Another competitor on the market would be the Speech Enhancement Generative Adversarial Network (SEGAN) which adapts the GAN architecture into applications on speech. While most older models focus on feature extraction and spectrogra
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Grechka, Asya. "Image editing with deep neural networks." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS683.pdf.

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L'édition d'images a une histoire riche remontant à plus de deux siècles. Cependant, l'édition "classique" des images requiert une grande maîtrise artistique et nécessitent un temps considérable, souvent plusieurs heures, pour modifier chaque image. Ces dernières années, d'importants progrès dans la modélisation générative ont permis la synthèse d'images réalistes et de haute qualité. Toutefois, l'édition d'une image réelle est un vrai défi nécessitant de synthétiser de nouvelles caractéristiques tout en préservant fidèlement une partie de l'image d'origine. Dans cette thèse, nous explorons di
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Villanueva, Aylagas Mónica. "Reconstruction and recommendation of realistic 3D models using cGANs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231492.

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Three-dimensional modeling is the process of creating a representation of a surface or object in three dimensions via a specialized software where the modeler scans a real-world object into a point cloud, creates a completely new surface or edits the selected representation. This process can be challenging due to factors like the complexity of the 3D creation software or the number of dimensions in play. This work proposes a framework that recommends three types of reconstructions of an incomplete or rough 3D model using Generative AdversarialNetworks (GANs). These reconstructions follow the d
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Hammond, Patrick Douglas. "Deep Synthetic Noise Generation for RGB-D Data Augmentation." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7516.

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Considerable effort has been devoted to finding reliable methods of correcting noisy RGB-D images captured with unreliable depth-sensing technologies. Supervised neural networks have been shown to be capable of RGB-D image correction, but require copious amounts of carefully-corrected ground-truth data to train effectively. Data collection is laborious and time-intensive, especially for large datasets, and generation of ground-truth training data tends to be subject to human error. It might be possible to train an effective method on a relatively smaller dataset using synthetically damaged dep
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Gruneau, Joar. "Investigation of deep learning approaches for overhead imagery analysis." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232208.

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Analysis of overhead imagery has a great potential to produce real-time data cost-effectively. This can be an important foundation for decision-making for businesses and politics. Every day a massive amount of new satellite imagery is produced. To fully take advantage of these data volumes a computationally efficient pipeline is required for the analysis. This thesis proposes a pipeline which outperforms the Segment Before you Detect network [6] and different types of fast region based convolutional neural networks [61] with a large margin in a fraction of the time. The model obtains a predict
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Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

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Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generative models is proposed together with a method for quantitatively evaluating the quality of the generated data sets. Using this framework, two data sets for pupil localization was generated with different generative models, including both well-established models and a novel model proposed for this pu
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Books on the topic "DeepaK Ganesh"

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Griffiths, Paul. Criminal London. Edited by Malcolm Smuts. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199660841.013.33.

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This chapter is divided into two sections: the first tries to trace Shakespeare’s steps and what we know about where he lived to describe and discuss the experience of criminality with its associated dangers and troubles that he might have faced each day of his London life; the second reconstructs the nature of crime more generally at this time to more deeply explore fear and danger in Shakespeare’s city. In doing so the chapter also contrasts sensationalist depictions of a criminal underworld of cut-throats, thieves and prostitutes, with organized gangs and a distinctive cant speech, depicted
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Malka, Adam. Men of Mobtown. University of North Carolina Press, 2018. http://dx.doi.org/10.5149/northcarolina/9781469636290.001.0001.

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What if racialized mass incarceration is not a perversion of our criminal justice system’s liberal ideals, but rather a natural conclusion? Adam Malka raises this disturbing possibility through a gripping look at the origins of modern policing in the influential hub of Baltimore during and after slavery’s final decades. He argues that America’s new professional police forces and prisons were developed to expand, not curb, the reach of white vigilantes, and are best understood as a uniformed wing of the gangs that controlled free black people by branding them—and treating them—as criminals. The
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Jones, D. Marvin. Dangerous Spaces. ABC-CLIO, LLC, 2016. http://dx.doi.org/10.5040/9798400637704.

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An eye-opening, unapologetic explanation of what “racial profiling” is in modern-day America: systematic targeting of communities and placing of suspicion on populations, on the basis of not only ethnicity but also certain places that are linked to the social identity of that group. In 21st-century, post-civil rights era America, “race” has become complex and intersectional. It is no longer simply a matter of color—black versus white—contends author D. Marvin Jones, but equally a matter of space or “geographies of fear,” which he defines as spaces in which different groups are particularly vul
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Johansen, Bruce, and Adebowale Akande, eds. Nationalism: Past as Prologue. Nova Science Publishers, Inc., 2021. http://dx.doi.org/10.52305/aief3847.

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Nationalism: Past as Prologue began as a single volume being compiled by Ad Akande, a scholar from South Africa, who proposed it to me as co-author about two years ago. The original idea was to examine how the damaging roots of nationalism have been corroding political systems around the world, and creating dangerous obstacles for necessary international cooperation. Since I (Bruce E. Johansen) has written profusely about climate change (global warming, a.k.a. infrared forcing), I suggested a concerted effort in that direction. This is a worldwide existential threat that affects every living t
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Book chapters on the topic "DeepaK Ganesh"

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Catling, David. "Agroecology of the Lower Ganges-Brahmaputra Basin." In Rice in Deep Water. Palgrave Macmillan UK, 1992. http://dx.doi.org/10.1007/978-1-349-12309-4_16.

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Gürsakal, Necmi, Sadullah Çelik, and Esma Birişçi. "Introduction to GANs." In Synthetic Data for Deep Learning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8587-9_3.

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Catling, David. "Deepwater Rice Cultures in the Ganges-Brahmaputra Basin." In Rice in Deep Water. Palgrave Macmillan UK, 1992. http://dx.doi.org/10.1007/978-1-349-12309-4_17.

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Michelucci, Umberto. "Generative Adversarial Networks (GANs)." In Applied Deep Learning with TensorFlow 2. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8020-1_11.

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Catling, David. "Socioeconomic Aspects of Deepwater Rice Culture in the Ganges-Brahmaputra Basin." In Rice in Deep Water. Palgrave Macmillan UK, 1992. http://dx.doi.org/10.1007/978-1-349-12309-4_18.

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Stamm, Matthew C., and Xinwei Zhao. "Anti-Forensic Attacks Using Generative Adversarial Networks." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_17.

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AbstractThe rise of deep learning has led to rapid advances in multimedia forensics. Algorithms based on deep neural networks are able to automatically learn forensic traces, detect complex forgeries, and localize falsified content with increasingly greater accuracy. At the same time, deep learning has expanded the capabilities of anti-forensic attackers. New anti-forensic attacks have emerged, including those discussed in Chap. 10.1007/978-981-16-7621-5_14 based on adversarial examples, and those based on generative adversarial networks (GANs). In this chapter, we discuss the emerging threat
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Zhou, Bolei. "Interpreting Generative Adversarial Networks for Interactive Image Generation." In xxAI - Beyond Explainable AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_9.

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AbstractSignificant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation. However, there lacks enough understanding of how a realistic image is generated by the deep representations of GANs from a random vector. This chapter gives a summary of recent works on interpreting deep generative models. The methods are categorized into the supervised, the unsupervised, and the embedding-guided approaches. We will see how the human-understandable concepts that emerge in the learned representation can be identified and used for interactive image generatio
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Tronchin, Lorenzo, Rosa Sicilia, Ermanno Cordelli, Sara Ramella, and Paolo Soda. "Evaluating GANs in Medical Imaging." In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88210-5_10.

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Anwar, Suzan, Mardin Anwer, and Daniah Al-Nadawi. "DeepFake Technology for Breast Cancer Dataset Generation Using Autoencoders and Deep Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88220-3_1.

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Abstract In the emerging field of radiogenomics, the primary challenge is the high cost of genetic testing, which restricts access to large, paired datasets of imaging and genetic information. Such datasets are essential for the effective training of machine learning algorithms in radiogenomic analyses. This research aims to bridge the gap between gene expression in tumors and their morphological representation in MRI scans of breast cancer patients. In this work an advanced autoencoder for processing gene expression data, and the derived weights from this autoencoder utilized were then employ
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Gonen, Ehud. "China and the Suez Canal—Politics, Economy, and Logistics." In Palgrave Studies in Maritime Politics and Security. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15670-0_2.

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AbstractThe relations between China and Egypt are good and open, based on common economic and political interests as well as a deep mutual cultural appreciation since both China and Egypt are part of the four great river civilizations of the ancient world (The four civilizations are China and the Yang Cha River, Egypt and the Nile River, the northwestern region of the Indian subcontinent and the Ganges River, and Mesopotamia and the Euphrates and Tigres rivers.). Egypt, even during Mao Zedong’s rule in China (1949–1976), enjoyed Chinese support as part of China’s support for the bloc of non-id
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Conference papers on the topic "DeepaK Ganesh"

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Oh, Sangeun, Yongsu Jung, Ikjin Lee, and Namwoo Kang. "Design Automation by Integrating Generative Adversarial Networks and Topology Optimization." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85506.

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Recent advances in deep learning enable machines to learn existing designs by themselves and to create new designs. Generative adversarial networks (GANs) are widely used to generate new images and data by unsupervised learning. Certain limitations exist in applying GANs directly to product designs. It requires a large amount of data, produces uneven output quality, and does not guarantee engineering performance. To solve these problems, this paper proposes a design automation process by combining GANs and topology optimization. The suggested process has been applied to the wheel design of aut
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Celebi, Naciye, Qingzhong Liu, and Muhammed Karatoprak. "A Survey of Deep Fake Detection for Trial Courts." In 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120919.

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Recently, image manipulation has achieved rapid growth due to the advancement of sophisticated image editing tools. A recent surge of generated fake imagery and videos using neural networks is DeepFake. DeepFake algorithms can create fake images and videos that humans cannot distinguish from authentic ones. (GANs) have been extensively used for creating realistic images without accessing the original images. Therefore, it is become essential to detect fake videos to avoid spreading false information. This paper presents a survey of methods used to detect DeepFakes and datasets available for de
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Yang, Liu, Mr Prabhat, George Karniadakis, et al. "Highly-scalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs." In 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS). IEEE, 2019. http://dx.doi.org/10.1109/dls49591.2019.00006.

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Srivastava, Prateek, Uma Yadav, Rajesh Ranjan, and Javalkar Dinesh Kumar. "Emerging Trends in Generative Adversarial Networks: An Analysis of Recent Advances and Future Directions." In International Conference on Cutting-Edge Developments in Engineering Technology and Science. ICCDETS, 2024. http://dx.doi.org/10.62919/uiei9828.

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This paper provides an in-depth analysis of the emerging trends in Generative Adversarial Networks (GANs), highlighting recent advancements and identifying future directions in this rapidly evolving field. GANs, as a pivotal component of unsupervised learning in artificial intelligence, have shown remarkable success in generating realistic synthetic data, which has broad implications across various domains such as image generation, video enhancement, and beyond. The study reviews the latest developments in GAN architectures, training algorithms, and their applications, underscoring the challen
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Gabani, Kathan, Het Pathak, Isitbhai Thakkar, Nilesh Jain, Senthurapandi Rajendran, and Dipyaman Mukherjee. "Dynamic Terrain Generation With Deep GANs." In International Conference on Artificial Intelligence and Robotics. Machine Intelligence Research Group (MIRG), 2023. https://doi.org/10.52968/15066433.

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Exploring the realm of video game creation, procedural terrain generation has stood the test of time as a means to generate extensive new graphical content automatically. Conventional techniques often employ algorithms tailored for specific terrains, meticulously designed by human hands. This study delves into the innovative application of deep convolutional generative adversarial models (DC-GANs) for the dynamic fabrication of authentic terrain maps. Furthermore, we present an inventive methodology for feature extraction that facilitates the specification and manipulation of geographical attr
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Çelik, Mustafa, and Ahmet HaydarÖrnek. "GAN-Based Data Augmentation and Anonymization for Mask Classification." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112315.

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Deep learning methods, especially convolutional neural networks (CNNs), have made a major contribution to computer vision. However, deep learning classifiers need large-scale annotated datasets to be trained without over-fitting. Also, in high-data diversity, trained models generalize better. However, collecting such a large-scale dataset remains challenging. Furthermore, it is invaluable for researchers to protect the subjects' confidentiality when using their personal data such as face images. In this paper, we propose a deep learning Generative Adversarial Networks (GANs) which generates sy
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Zhang, Zhiwei, Shifeng Chen, and Lei Sun. "P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/448.

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One-class novelty detection is to identify anomalous instances that do not conform to the expected normal instances. In this paper, the Generative Adversarial Networks (GANs) based on encoder-decoder-encoder pipeline are used for detection and achieve state-of-the-art performance. However, deep neural networks are too over-parameterized to deploy on resource-limited devices. Therefore, Progressive Knowledge Distillation with GANs (P-KDGAN) is proposed to learn compact and fast novelty detection networks. The P-KDGAN is a novel attempt to connect two standard GANs by the designed distillation l
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Ma, Yuqing, Yue He, Fan Ding, Sheng Hu, Jun Li, and Xianglong Liu. "Progressive Generative Hashing for Image Retrieval." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/121.

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Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of sema
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Wang, Bingning, Kang Liu, and Jun Zhao. "Conditional Generative Adversarial Networks for Commonsense Machine Comprehension." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/576.

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Recently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application to deal with natural language understanding problem. This dataset contains a lot of story tests which require commonsense inference ability. Unfortunately, the training data is almost unsupervised where each context document followed with only one positive sentence that can be inferred from the context. However, in the testing period, we must make inference from two candidate sentences. To tackle this problem, we employ the generative adversarial networks (GANs) to generate fake se
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Shadab Alam, Md, Marieke Martens, and Pavlo Bazilinskyy. "Generating Realistic Traffic Scenarios: A Deep Learning Approach Using Generative Adversarial Networks (GANs)." In 13th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe1005927.

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Traffic simulations are crucial for testing systems and human behaviour in transportation research. This study investigates the potential efficacy of Unsupervised Recycle Generative Adversarial Networks (Recycle–GANs) in generating realistic traffic videos by transforming daytime scenes into nighttime environments and vice-versa. By leveraging Unsupervised Recycle-GANs, we bridge the gap between data availability during day and night traffic scenarios, enhancing the robustness and applicability of deep learning algorithms for real-world applications. GPT-4V was provided with two sets of six di
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Reports on the topic "DeepaK Ganesh"

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Almorjan, Dr Abdulrazaq, Dr Kyounggon Kim, and Ms Norah Alilwit. NAUSS Ransomware Trends Report in Arab Countries 2020-2022. Naif University Press, 2024. http://dx.doi.org/10.26735/orro4624.

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Threat actors, including infamous cybercrime groups and financially driven ransomware gangs, have focused on Arab countries› businesses and organizations as they grow and move toward digital transformation. In particular, ransomware is a very serious type of cyber-attack worldwide, and many organizations are severely affected by it. INTERPOL indicates that the ransomware gangs are targeting different regions such as Africa, Americas, Caribbean, Asia-Pacific, Europe, Middle East, and North Africa 1. The Center of Excellence in Cybercrime and Digital Forensics (CoECDF) at NAUSS has conducted a d
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