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Detection of diabetic retinopathy and age-related macular degeneration using DenseNet based neural networks

Multimedia Tools and Applications 2025年第1期84卷 289-316页

作者： Singh, Manpinder Dalmia, Saiba Ranjan, Ranjeet Kumar School of Computing DIT University Dehradun248009 India CSE Department Thapar Institute of Engineering & Technology Patiala147004 India

The eyes are the organ of sight and one of the most highly developed sensory organs in our body which covers a larger part of the brain. One of the most common problems that are spreading from kids to adults is an eye disorder which can be defined as abnormal functioning of the eye that can lead to vision disturbance. Examples of major eye problems are cataracts, glaucoma, etc. Artificial Intelligence has helped benefit research in the medical field. Ocular diseases can be detected automatically through Computer Vision and Deep Learning models when high-quality medical eye fundus images are provided to them. Inspired by this, we proposed three deep learning models based on the DenseNet pre-trained model for Ocular Disease Detection. Since the amount of eye scans being performed is rapidly growing at a much faster rate than the interpretation of the scan results, our proposed models could help automate the process thus increasing speed and efficiency. The proposed model performed the detection with an accuracy of 77%, 86%, and 98% for Ocular Disease Detection, Diabetic Retinopathy, and Age-Related Macular Degeneration tasks respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computer vision

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A Green Cloud-Based Framework for Energy-Efficient Task Scheduling Using Carbon Intensity Data for Heterogeneous Cloud Servers

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IEEE Access 2025年 13卷 73916-73938页

作者： Beena, B.M. Prashanth, C.S.R. Manideep, Thotapalli Sri Surya Saragadam, Sneha Karthik, Garikipati Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India University of Windsor WindsorON Canada

Managing modern data centre operations is increasingly complex due to rising workloads and numerous interdependent components. Organizations that still rely on outdated, manual data management methods face a heightened risk of human error and struggle to adapt quickly to shifting demands. This inefficiency leads to excessive energy consumption and higher CO2 emissions in cloud data centres. To address these challenges, integrating advanced automation within Infrastructure as a Service (IaaS) has become essential for IT industries, representing a significant step in the ongoing transformation of cloud computing. For data centres aiming to enhance efficiency and reduce their carbon footprint, intelligent automation provides tangible benefits, including optimized resource allocation, dynamic workload balancing, and lower operational costs. As computing resources remain energy-intensive, the growing demand for AI and ML workloads is expected to surge by 160% by 2030 (Goldman Sachs). This heightened focus on energy efficiency has driven the need for advanced scheduling systems that reduce both carbon emissions and operational expenses. This study introduces a deployable cloud-based framework that incorporates real-time carbon intensity data into energy-intensive task scheduling. By utilizing AWS services, the proposed algorithm dynamically adjusts high-energy workloads based on regional carbon intensity fluctuations, using both historical and real-time analytics. This approach enables cloud service providers and enterprises to minimize environmental impact without sacrificing performance. Designed for seamless integration with existing cloud infrastructures—including AWS, Google Cloud, and Azure—this scalable solution utilizes Kubernetes-based scheduling and containerized workloads for intelligent resource management. By combining automation, real-time analytics, and cloud-native technologies, the framework significantly enhances energy efficiency compared to traditional

关键词： Infrastructure as a service (IaaS)

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Emotion detection: Facial emotion detection using ML 4th

Emotion detection: Facial emotion detection using ML

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4th International Conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Sharma, Sharad Chauhan, Ritik Vajpayee, Pooja School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Cognitive perception of images is an intense task, like guessing the truth of a thought or a mystery. In this process, we use different methods to solve the need to know the job. In recent years, emotional intelligence has received widespread attention due to its important role in improving human-computer interaction and emotional analysis. Because of its processes for extracting visual features, deep neural networks-particularly convolutional neural network (CNN)-are frequently employed in FER. A lot of research has been done on CNN, but only a few areas have solved the facial recognition (FER) problem. After many reports and studies, the shortcoming of most existing models is that they often consider the front profile and ignore the side profile of the current image, although different angular views are also important for the perspective model. This helps you make informed decisions about personal needs, providing support, or security threats. However, this proved to be too complex for computers, and feature extraction needed a wide range of image processing methods. Numerous machine learning techniques are appropriate for this particular purpose. To use machine learning for any kind of detection, an algorithm must first be trained and then tested on relevant data. This article examines some machine learning algorithms and strategies for extraction that aid in the analysis of human emotion. This review contains a comprehensive review of a variety of materials, methods, and measures commonly used in psychology research. It also highlights the challenges and limitations of current models, including dealing with uncertainties and cultural differences. Face recognition in large datasets poses additional challenges due to the diversity of images as well as the differences between side and front views. © 2025 the Author(s).

关键词： Convolutional neural networks

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CRIS-b: A High-Speed Unified Modulo Reduction Algorithm and Hardware Architecture for CRYSTALS-Kyber 38

CRIS-b: A High-Speed Unified Modulo Reduction Algorithm and ...

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38th International Conference on VLSI Design, VLSID 2025

作者： Majumdar, Alip Shrestha, Rahul School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9798331522445

This paper presents highly reliable algorithm and high-speed hardware architecture for a unified modulo reduction for CRYSTALS-Kyber. This new architecture for modulo reduction is capable of operating at a maximum clock frequency of 351.62 MHz on Artix-7 FPGA platform, consuming a latency of 5 clock cycles, which is approximately 35.24% faster than state-of-the-art implementations. In this work, we have alleviated the size expansion problem of intermediate value of the conventional Barrett algorithm (which is one of the fastest and most reliable algorithm for modulo reduction, reported in literature) by suggesting a new technique, referred as 'Conditional R-index Switching for b-value Minimization (CRIS-b)'. We propose a unified modulo reduction design handling both positive and negative spectrum numbers for all arithmetic modulo operations in a butterfly unit of Number Theoretic Transform(NTT). © 2025 IEEE.

关键词： Quantum cryptography

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Exit options sustain altruistic punishment and decrease the second-order free-riders, but it is not a panacea

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Science China(Information Sciences) 2025年第1期68卷 278-291页

作者： Chen SHEN Zhao SONG Lei SHI Jun TANIMOTO Zhen WANG Faculty of Engineering Sciences Kyushu University School of Statistics and Mathematics Yunnan University of Finance and Economics School of Computing Engineering and Digital Technologies Teesside University School of Artificial Intelligence OPtics and Electro Nics (iOPEN) Northwestern Polytechnical University

Why do humans punish free riders at their own expense? This behavior represents an evolutionary puzzle in human societies. This study explores the role of exit strategies in fostering altruistic punishment within evolutionary game theory. We extend the traditional prisoner's dilemma model by incorporating exiters, players who opt out for a small payoff while nullifying their opponent's payoff, and altruistic punishers who cooperate and punish non-cooperators. Our findings indicate that in well-mixed populations, exiters destabilize defection but do not promote altruistic punishment. In social networks, however,exiters enable altruistic punishment via cyclic dominance among altruistic punishers, defectors, and exiters. Notably, this cyclic dominance is sensitive to exit payoffs; adjustments can lead to cyclic dominance of non-punishing cooperators, defectors, and exiters, or to a bi-stable state between these two types of cyclic dominance. These results highlight the nuanced impact of exiters on altruistic punishment, emphasizing the need for careful incentivization of exit behavior. While exiters can support altruistic punishment in networked populations, their effectiveness is not a panacea and is highly sensitive to exit payoffs, indicating limits to the voluntary participation mechanism.

关键词： evolutionary game theory altruistic punishment coexistence cyclic dominance bi-stable

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Finding Identity through Gait 6

Finding Identity through Gait

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Srinivasan, Lavanya University of West London School of Computing and Engineering London United Kingdom

ISBN: (纸本)9798331506520

Human gait identification is the recognition of a person from a series of walking images. In contrast to fingerprint or iris-based identification methods, gait identification offers the significant benefit of remote execution. Gait detection is emerging as one of the most promising biometric identification techniques. Traditional methods to identify the identity of gait are Background subtraction, Gait Energy Image, Gait Entropy, etc., human gait identification is a potential new tool for identifying individuals beyond traditional methods. The objectives of this study were to develop an automated gait detection system and examine the unique aspects of gait. This study encompassed the following: gait predictions and estimation based on HRNet 17 joint positions (locating body joints of humans in an image/video), better predictions with higher confidence scores by eliminating poor predictions, and the 14 joint coordinates and 17 joint points are fed into Graph Convolutional Network by eliminating low confidence pose to classify labels. Semi-supervised learning on graph-structured data can be accomplished via Graph Convolutional Networks. Graph Convolutional Networks is a very effective version of convolutional neural networks, which function directly on graphs. The experimental results show that the Graphical Convolutional Network identifies gait subjects with higher accuracy of normal walking and carrying a backpack with 98% and 96% respectively. This work will be extended in the future by fusing face and gait to identify various gait subjects and the gait in forensic identifications. © 2025 IEEE.

关键词： Convolutional neural networks

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A Dual-State Active Wideband Reflectarray

A Dual-State Active Wideband Reflectarray

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2025 IEEE International Workshop on Antenna Technology, iWAT 2025

作者： Elaouinate, Malak Koutinos, Anastasios G. Zekios, Constantinos L. College of Engineering and Computing Florida International University Miami United States

ISBN: (纸本)9798331527365

In our recent work, we introduced a cost-effective technique for designing wide-band reconfigurable reflectarray antennas (RAs). Building on this, we now present a simple yet elegant biasing network that reconfigures such RAs. This novel diode- enabled biasing network is designed to minimize RF interference while optimizing DC performance. Notably, a 10 × 10 element array is developed, demonstrating stable beam reconfigurability across the entire 8.85 GHz to 11.75 GHz bandwidth. © 2025 IEEE.

关键词： Reflection

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Enhancing Connectivity of AODV in Urban Scenarios for VANETs: A QoS-Aware Approach 4

Enhancing Connectivity of AODV in Urban Scenarios for VANETs...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Janet Kumari, J Jesy Thangam, S. Ghosh, Archishman Kolhe, Dhruv Vijay Ghosh, Soumish Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523923

As cities expand, vehicles and congestion become more complex. Efficient vehicle-to-vehicle contact networks are needed for road safety and efficient traffic flow. Thus, Vehicular Ad Hoc Networks are needed to overcome all the issues. The Adhoc On-Demand Distance Vector (AODV) routing protocol is used in VANETs. However, this protocol has many challenges in urban settings due to high vehicle density, rapid topology changes, and non-line-of-sight conditions. This work improves AODV by integrating Quality of Service (QoS) metrics. The prioritizing factors such as overhead and end-to-end delay are used to create a more efficient protocol for urban traffic. The SUMO simulator is used for realistic traffic patterns and NS-2 for network analysis. An evaluation of the protocol against the classical AODV is done and important metrics for real-time communication are focused. The enhanced AODV showed marked improvements. It achieved lower overhead in route finding, an increase in the number of packets sent, and lower end-to-end delay. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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SV-data2vec: Guiding Video Representation Learning with Latent Skeleton Targets

SV-data2vec: Guiding Video Representation Learning with Late...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Doždor, Zorana Hrkać, Tomislav Kalafatić, Zoran University of Zagreb Faculty of Electrical Engineering and Computing Croatia

ISBN: (纸本)9798331510831

Recent advancements in action recognition leverage both skeleton and video modalities to achieve state-of-the-art performance. However, due to the challenges of early fusion, which tends to underutilize the strengths of each modality, existing methods often resort to late fusion, consequently leading to more complex designs. Additionally, self-supervised learning approaches utilizing both modalities remain underexplored. In this paper, we introduce SV-data2vec, a novel self-supervised framework for learning from skeleton and video data. Our approach employs a student-teacher architecture, where the teacher network generates contextualized targets based on skeleton data. The student network per-forms a masked prediction task using both skeleton and visual data. Remarkably, after pretraining with both modalities, our method allows for fine-tuning with RGB data alone, achieving results on par with multimodal approaches by effectively learning video representations through skeleton data guidance. Extensive experiments on benchmark datasets NTU RGB+D 60, NTU RGB+D 120, and Toyota Smarthome confirm that our method outperforms existing RGB based state-of-the-art techniques. The code is available at github. com/zoranadozdor/SVdata2vec. © 2025 IEEE.

关键词： Self-supervised learning

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Discovering Social Media Influencers Using a Deep Regression Analysis

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SN Computer Science 2025年第4期6卷 1-18页

作者： Ramya, G.R. Bagavathi Sivakumar, P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Highly influential users (IUs) play a vital role in disseminating information on online social networks (OSNs). Recognizing IUs is crucial for brand awareness, strategic marketing and consumer engagement. Researchers have developed the temporal influential model (TIM) and incremental logistic regression (ILLR) to find out the probability of a user being influential on Twitter. However, accurately identifying IUs requires considering multiple user and tweet attributes. This study proposes a model for predicting IUs by analyzing user and trend-based attributes from tweets, calculating a weighted associated influence (WAI) score for each user and enhancing IU prediction through user similarity, influence degree, and trustworthiness metrics. The goal is to combine the TIM with deep regression (TIM-DeepReg) to improve accuracy in predicting high IUs on OSNs while handling large amounts of data and attributes. The TIM-DeepReg model combines convolutional neural network (CNN) with regression to automatically learn user and tweet attributes and enhance IU prediction efficiency. Lastly, the experimental findings show that the TIM-DeepReg model surpasses other models in IU prediction for the Tariff, Service, and Punctuality topics of airline services on Twitter and Facebook, with 87.4%, 88.9%, and 88.8% of the most influential individuals predicted. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep regression Influencer on social networks Influencer prediction from Facebook and Twitter Temporal influential model Weighted associated influence score

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