ACM Books’ Post

The Handbook of Multimodal-Multisensor Interfaces, Volume 2 is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. Get it here: https://bit.ly/3WHpFQ0 Authors: Sharon Oviatt, Incaa Designs, Bjoern Schuller, University of Passau and Imperial College London, Philip R. Cohen, VoiceBox Technologies, Daniel Sonntag, German Research Center for Artificial Intelligence, Gerasimos Potamianos, University of Thessaly, Antonio Kruger, German Research Center for Artificial Intelligence #mulimodal #signal #processing #architectures #machinelearning #traitrecognition #cognitiveload #BehavioralSignals #socialsignals #Classifying #Multimodal #Data #AffectDetection ACM, Association for Computing Machinery

The Handbook of Multimodal-Multisensor Interfaces, Volume 2 is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals. This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. Get it here: https://bit.ly/3WHpFQ0 Authors: Sharon Oviatt, Incaa Designs, Bjoern Schuller, University of Passau and Imperial College London, Philip R. Cohen, VoiceBox Technologies, Daniel Sonntag, German Research Center for Artificial Intelligence, Gerasimos Potamianos, University of Thessaly, Antonio Kruger, German Research Center for Artificial Intelligence. #mulimodal #signal #processing #architectures #machinelearning #traitrecognition #cognitiveload #BehavioralSignals #socialsignals #Classifying #Multimodal #Data #AffectDetection ACM, Association for Computing Machinery

The Handbook of Multimodal-Multisensor Interfaces, Volume 2 is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals. This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. Get it here: https://bit.ly/3WHpFQ0 Authors: Sharon Oviatt, Incaa Designs, Bjoern Schuller, University of Passau and Imperial College London, Philip R. Cohen, VoiceBox Technologies, Daniel Sonntag, German Research Center for Artificial Intelligence, Gerasimos Potamianos, University of Thessaly, Antonio Kruger, German Research Center for Artificial Intelligence. #mulimodal #signal #processing #architectures #machinelearning #traitrecognition #cognitiveload #BehavioralSignals #socialsignals #Classifying #Multimodal #Data #AffectDetection ACM, Association for Computing Machinery

Thanks Engineering Applications of Artificial Intelligence, Q1, IF:8, Elsevier, for the #trust of being chosen as a #reviewer 3 times in 6 months. #engineering #artificialintelligence #machinelearning #deeplearning #datascience #elsevier

🚀💫 I'm thrilled and excited to announce the publication of my Survey paper, "Advancements in Generative Modeling: A Comprehensive Survey of GANs and Diffusion Models for Text-to-Image Synthesis and Manipulation," in IEEE Xplore! This paper delves into the two dominant forces in Generative Al (Gen AI): Generative Adversarial Networks (GANs) and Diffusion Models. Here's a glimpse into what you'll find: •In-depth exploration of GANs: We delve into the architecture, optimization techniques, and fascinating challenges like mode disintegration and instability. •Demystifying Diffusion Models: This section explores how these models, combining noise diffusion and denoising, rewrite the generative narrative. We discuss their high-fidelity photo generation, precise distribution coverage, and scalability advantages. •The Power of Collaboration: The paper sheds light on the interaction between GANs and Diffusion Models, highlighting scenarios where each excels and how their collaboration unlocks even more powerful capabilities. •A Roadmap for the Future: We conclude by identifying key areas ripe for exploration and development in the ever-evolving landscape of generative modeling. This paper serves as a comprehensive resource for researchers and practitioners who want to navigate the exciting world of GANs and Diffusion models. Feel free to download the paper here: https://lnkd.in/d-bS7GdA and reach out if you have any questions! Primary Author: PRIYANSHU DESHMUKH Author 2: Pranav Ambulkar Author 3: Pranoti Sarjoshi Author 4: Harshal Dabhade Author 5 & Guide: Mr. Saurabh Shah. CSE PRMCEAM, Badnera #GAN's #DiffusionModel #GenAI #IEEE #Xplore #Publish #SurveyPaper

🚀Excited to share that our research paper, "Leveraging BERT-Enhanced MLP Classifier for Automated Stress Detection in Social Media Articles," has been presented at the IEEE-sponsored International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) and published in the conference proceedings, it is available in the IEEE Xplore digital library. This research, co-authored by Amlan Nayak, Sudatta Jana, Pratim Dasude, Utkarsh Anand, Dr. Amiya Ranjan Panda, and me, focuses on developing a BERT-enhanced Multilayer Perceptron (MLP) classifier that effectively detects stress-related patterns in social media articles. By leveraging BERT’s contextual capabilities and combining them with the power of MLPs, our model significantly enhances the accuracy and efficiency of stress detection. A big thank you to IEEE and the ACROSET conference for providing us with this amazing platform! Link of the Paper - https://lnkd.in/g5CHWZDd #MachineLearning #StressDetection #BERT #MLP #SocialMedia #AI #Research #IEEE #Publication #IEEEConference #ACROSET

🎓 Excited to share a major milestone in my academic journey! My paper, "Design of Neural Network based Approaches for Land Usage Land Cover Classification," has been published in IEEE Xplore as part of the 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). 🎉 🌍 Abstract: Land Use and Land Cover (LULC) classification plays a pivotal role in leveraging remote sensing techniques for satellite images with high resolution. In this research, I explored the power of Neural Networks for spatiotemporal analysis using models such as CNN, ResNet50/V2, ResNet152V2, VGG16, and VGG19. The Eurosat-RGB dataset served as the foundation for training, testing, and validating these models. 🔍 Key Findings: CNN achieved 95.19% accuracy ResNet50/V2 models: 95.33%/95.40% accuracy ResNet152V2: 96.62% accuracy VGG16/VGG19 models: 97.64%/97.20% accuracy The results highlight a fascinating trend: deeper architectures enhance performance, paving the way for improved land usage prediction and enhanced geographic maps. 🌟 This achievement would not have been possible without the guidance and support of Prof. (Dr.) Raghav Mehra and Dr. Hari Kunadharaju. Your mentorship has been invaluable. 🙏 📖 Read the full paper here: https://lnkd.in/gJPw8yrd 🔗 DOI: 10.1109/ICEEICT61591.2024.10718442 📍 Presented at: Trichirappalli, India I hope this work inspires further innovation in remote sensing and geographic research. Let’s connect and collaborate for a better future! 🌐 #IEEE #Research #DeepLearning #GeographicMapping #LULCClassification #NeuralNetworks

I am excited to share that our latest research titled "SynapNet: A Complementary Learning System Inspired Algorithm With Real-time Application in Multimodal Perception" has been published in the prestigious IEEE Transactions in Neural Networks and Learning Systems journal 🎉. In this work we present a Continual Learning (CL) algorithm composed of a fast learner and a slow consolidator network equipped with a VAE based generative memory, a lateral inhibition mechanism to dampen the effects of adjacent neurons using gradient masking, and a sleep phase 💤 to re-structure the learned representations. We benchmark our algorithm on several standard datasets and compare it with the SOTA CL algorithms. We also applied our algorithm in a real-time dynamic environment for object classification on a soft pneumatic gripper equipped with sensors. A special thanks to all the co-authors for their contributions Lorenzo Fruzzetti, Enrico Donato, and Egidio Falotico For more information check out the full paper here : https://lnkd.in/dVDzcGy9 Brain-Inspired Robotics Laboratory Scuola Superiore Sant'Anna #continuallearning #lifelonglearning #CLapplication #softgripper

Large-Scale Machine Mind Modeling and Design in Machine Intelligence for Control of Complex Large-Scale Distributed Adaptive Dynamical Networks

🎉 Excited to share that my research paper titled "Optimizing Facial Feature Extraction and Localization Using YOLOv5: An Empirical Analysis of Backbone Architectures with Data Augmentation for Precise Facial Region Detection" has been published! 📝💻 In this paper, we delve into the realm of computer vision, specifically focusing on enhancing facial feature extraction and localization using YOLOv5. Through empirical analysis, we explore various backbone architectures and data augmentation techniques to achieve more accurate facial region detection. This research opens doors for improved facial recognition systems, paving the way for advancements in fields such as security, healthcare, and human-computer interaction. I'm immensely grateful to my co-authors and the research community for their valuable contributions and support throughout this journey. #Research #ComputerVision #FacialRecognition #YOLOv5 #DataScience #ArtificialIntelligence #machineLearning #ResearchPaper #Published #Technology