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7th International Conference on Image Information Processing, ICIIP 2023

作者： Varalakshmi, K. Revathi, A. Srm Institute of Science And Technology Department of Computer Science And Engineering Chennai Tamilnadu Kattangulathur India Srm Institute of Science And Technology Department of Computational Intelligence Chennai Tamilnadu Kattangulathur India

ISBN: (纸本)9798350371406

Slin diseases are disorders that affect the tissues of the human skin. The common skin disease includes eczema, psoriasis, acne, moles and various fungal infections. Skin disorders contribute 1.79% of the global burden of disease across the world. The identification and categorization of skin diseases are done through advanced methodologies which include deep learning techniques with computer-aided designs. This helps the medical professional to accurately diagnose and categorize skin diseases. The initial step of skin disease identification involves image processing and feature extraction through the Convolutional Neural Network (CNN).This helps in analyzing the skin images and classifying the distinct patterns and anomalies which forms the essential for the identification of particular skin conditions. This helps in accurately recognizing various skin diseases like melanoma, psoriasis and eczema through the evaluation of large amounts of datasets. The categorization of skin disease is obtained through RNN and LSTM. This is done by analyzing the temporal characteristics and symptom progression. These skin disease exhibits unique patterns and minute changes in appearance over time. The accurate identification of skin disease progression is obtained through modelling sequential data using LSTM and RNN. This provides an accurate solution for disease progression with personalized treatment recommendations. They help the dermatologist by providing automatic decision-making techniques. Thus the integration of CNN, RNN and LSTM helps to achieve higher efficiency and accuracy in skin disease diagnosis and monitoring. This helps to obtain improved potential to reduce diagnostic errors and enhance patient outcomes. © 2023 IEEE.

关键词： Diseases

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Augmented Reality and Artificial Intelligence in Sign Language Expression 3

Augmented Reality and Artificial Intelligence in Sign Langua...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

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H-BlockEnc: A Pioneering Blockchain Integrated Cryptographic Security Model for IoT-Healthcare Systems 2

H-BlockEnc: A Pioneering Blockchain Integrated Cryptographic...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Jackulin Mahariba, A. Saravanan, T.R. Prabaharan, G. Yempally, Sangeetha Kiruthika, M. Sarveshwaran, Velliangiri Srm Institute of Science and Technology Department of Computational Intelligence TamilNadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Hyderabad Narasapur India B v Raju Institute of Technology Department of Electronics and Communication Engineering Hyderabad Narasapur India

ISBN: (纸本)9798350328202

Recent research studies have demonstrated that secured medical data sharing greatly enhances people's quality of life through improving medical treatment and care. The challenges associated with the safety of medical data make it even more difficult for the whole healthcare system to be interoperable. When it comes to striking a balance within the field of healthcare, blockchain technology has begun to emerge as a significant option. It is hard to find well-established solutions, nonetheless, due to the ongoing development of innovative Blockchain technology and the transformation of healthcare facilities. The main purpose of this paper is to develop the most straightforward and efficient security paradigm known as, Healthcare - Blockchain paired with homomorphic encryption (H-BlockEnc) framework for IoT-healthcare systems. Here, cutting-edge blockchain technology has been used to quickly and cheaply secure the safety and confidentiality of medical data. In addition, for increased security and data privacy, the homomorphic encryption standard is also utilized for cryptographic activities. The Beetle Swarm Optimization (BSO) technique is used throughout this process to generate keys as efficiently as possible. Based on processing time, energy consumption, block count, and loss rate, the performance and security level of the suggested H-BlockEnc model are verified and contrasted. © 2024 IEEE.

关键词： Blockchain

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Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis Solar Tracking System 4

Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis So...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Ravikumar, S. Eugene Berna, I. Vijay, K. Pradeep, R. Babu, R. R&d Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of College of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

The photovoltaic effect has permanently transformed the landscape of renewable sources by making it possible to convert solar energy into useable power. This concept is based on solar photovoltaic (PV) cells, but current PV technology limits us to capturing just a small percentage of the sun's energy at any given time. However, productivity can be greatly increased by using solar monitoring equipment. Creating and validating a low-priced active dual-axis solar tracking system is a breeze if you follow the steps outlined in this article. The best orientation for solar panels is perpendicular to the sun because their production is directly related to the amount of light they receive. Conventional solar panels have trouble keeping up with the ever-changing ideal angle for sunlight absorption and energy gathering. By tracing the sun's movement across the sky, solar trackers can triple the effectiveness of solar panels. This technological advancement is expected to lead to an improvement in energy generation and overall efficiency. This state-of-the-art solar tracker is powered by LEDs and an Arduino Uno. These systems work together to follow the sun's motion and maintain the solar panels facing the appropriate way. To ensure that the solar panels receive the most amount of sunshine possible, the system will automatically adjust their angle. Data processing and analysis in LabVIEW demonstrated the improved efficiency of the novel architecture. Researchers discovered a remarkable 13.44% boost in energy production compared to conventional fixed solar PV systems. © 2024 IEEE.

关键词： Solar panels

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Detecting VPN Traffic in Real-Time with Active Probing 22

Detecting VPN Traffic in Real-Time with Active Probing

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Tian, Yuan Cao, Zechun Huang, Shou-Hsuan Stephen Department of Computer Science University of Houston HoustonTX United States Dept of Computational Engineering and Mathematical Sciences Texas A&M University-San Antonio San AntonioTX United States

ISBN: (纸本)9798331510183

Malicious attackers often exploit anonymity networks like VPNs to conceal their identities. This paper introduces a novel real-time detection algorithm based on discrepancies in packet round-trip times (RTTs) and active probing of VPN servers. When a client accesses a target server via a VPN, two distinct RTTs emerge anchored on the target: target-VPN and target-VPN-client. The proposed detection algorithm determines whether one or two significantly different RTT clusters are present, with two clusters indicating the use of a VPN. The focus of this study is the detection of Secure Shell (SSH) connections through VPNs. While measuring the RTT between the target and the client is relatively straightforward due to the packet exchange of the TCP/SSH protocol, estimating the RTT between the target and the VPN presents challenges. An active probing method is proposed to calculate the RTT, accounting for the potential involvement of a malicious or compromised VPN. By employing machine learning to analyze these RTTs, the system can effectively detect intruders utilizing VPNs. This research serves as a foundation for developing secure and efficient VPN detection systems, which are critical for preserving the integrity and privacy of digital resources. ©2024 IEEE.

关键词： Network intrusion

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Evaluation of Deep Neural Operator Models Toward Ocean Forecasting

Evaluation of Deep Neural Operator Models Toward Ocean Forec...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Rajagopal, Ellery Babu, Anantha N. S. Ryu, Tony Haley, Patrick J. Mirabito, Chris Lermusiaux, Pierre F. J. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States Center for Computational Science and Engineering Massachusetts Institute of Technology Department of Mechanical Engineering CambridgeMA United States

ISBN: (纸本)9798218142186

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications. © 2023 The Marine Technology Society (MTS).

关键词： Flow of fluids

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization

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Physical Review Materials 2024年第10期8卷 L101001页

作者： Anubhab Haldar Quentin Clark Marios Zacharias Feliciano Giustino Sahar Sharifzadeh Department of Electrical and Computer Engineering Department of Philosophy Oden Institute for Computational Engineering and Sciences Department of Physics Division of Materials Science and Engineering

We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.

关键词： Electron-phonon coupling Electronic structure 2-dimensional systems Machine learning

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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ADAPTIVE RANK ALLOCATION: SPEEDING UP MODERN TRANSFORMERS WITH RANA ADAPTERS

arXiv

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arXiv 2025年

作者： Garcia, Roberto Liu, Jerry Sorvisto, Daniel Eyuboglu, Sabri Institute of Computational and Mathematical Engineering Stanford University United States Department of Computer Science Stanford University United States

Large Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron (MLP) layers, offer some speedups but suffer from limitations in modern Transformers. These include reliance on sparse activations, incompatibility with attention layers, and the use of costly neuron masking techniques. To address these issues, we propose the Adaptive Rank Allocation framework and introduce the Rank and Neuron Allocator (RaNA) adapter. RaNA adapters leverage rank adapters, which operate on linear layers by applying both low-rank matrix decompositions and adaptive masking to efficiently allocate compute without depending on activation sparsity. This enables RaNA to be generally applied to MLPs and linear components of attention modules, while eliminating the need for expensive maskers found in neuron-adaptive methods. Notably, when compared to neuron adapters, RaNA improves perplexity by up to 7 points and increases accuracy by up to 8 percentage-points when reducing FLOPs by ∼44% in state-of-the-art Transformer architectures. These results position RaNA as a robust solution for improving inference efficiency in modern Transformer architectures. © 2025, CC BY.

关键词： Neurons

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