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Using time-aware graph neural networks to predict temporal centralities in dynamic graphs 24

Using time-aware graph neural networks to predict temporal c...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Franziska Heeg Ingo Scholtes Chair of Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science (CAIDAS) Julius-Maximilans-Universität Würzburg

ISBN: (纸本)9798331314385

Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities like closeness or betweenness can give misleading results about the true importance of nodes in a temporal graph. To address this issue, temporal generalizations of betweenness and closeness have been defined that are based on the shortest time-respecting paths between pairs of nodes. However, a major issue of those generalizations is that the calculation of such paths is computationally *** this issue, we study the application of De Bruijn Graph Neural Networks (DBGNN), a time-aware graph neural network architecture, to predict temporal path-based centralities in time series data. We experimentally evaluate our approach in 13 temporal graphs from biological and social systems and show that it considerably improves the prediction of betweenness and closeness centrality compared to (i) a static Graph Convolutional Neural Network, (ii) an efficient sampling-based approximation technique for temporal betweenness, and (iii) two state-of-the-art time-aware graph learning techniques for dynamic graphs.

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A Comparative Study of DenseNet121 and InceptionResNetV2 model for DeepFake Image Detection

A Comparative Study of DenseNet121 and InceptionResNetV2 mod...

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Applied artificial intelligence and Computing (ICAAIC), International Conference on

作者： R Manoranjitham Somu Santhi Swaroop Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The rise of deep fake technology poses a significant challenge to societal integrity, necessitating robust detection methods. This research study investigates the effectiveness of DenseNet121 and InceptionResNetV2 deep learning models in detecting deep fake images. The study starts with the curation of the dataset and then applies different preprocessing methods to improve the quality of the dataset like scaling, normalization, and data augmentation. The DenseNet121 and InceptionResNetV2 models are trained to differentiate authentic and manipulated images based on distinctive features. DenseNet121’s dense connectivity facilitates feature reuse, enabling effective extraction of discriminative features crucial for identifying manipulated content. In contrast, InceptionResNetV2’s sophisticated architecture exploits both inception and residual connections, enhancing its ability to capture intricate patterns indicative of deep fake manipulation. The comparative analysis of performance metrics reveals InceptionResNetV2’s superiority, achieving an impressive accuracy rate of 99.78% compared to DenseNet121. These findings underscore the efficacy of leveraging existing architectures for identifying deep fake images, thereby contributing to the ongoing efforts to combat misinformation and safeguard digital content integrity.

关键词： Measurement Deep learning Deepfakes Accuracy computer architecture Feature extraction Data augmentation

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Bread Quality Assessment Using Deep learning with shape and Volume Metrics

Bread Quality Assessment Using Deep Learning with shape and ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Rohan Joel J Ruth Moly Benjamin Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

With the goal to guarantee customer satisfaction and optimize processes for manufacturing in the baking sector, bread quality evaluation is essential. Conventional techniques for evaluating bread quality are frequently time-consuming, subjective, and labor-intensive. In this study, we suggest a novel method for evaluating bread quality that combines shape and volume metrics with deep learning techniques, namely the YOLOv5 algorithm. We enable the YOLOv5 model to properly recognize and categorize bread quality attributes by training it on a dataset that consists of photos of various types of bread with corresponding quality labels. To provide a more thorough assessment of bread quality, we now include shape and volume metrics in addition to the conventional image-based quality assessment. Bakeries may increase productivity, save expenses, and guarantee a constant level of product quality by automating the quality assessment process. Furthermore, the suggested method may be modified for use in tasks other than evaluating the quality of bread, which would promote automation and quality control in the food sector.

关键词： Deep learning Measurement YOLO Productivity Ethics Shape Quality control Real-time systems Quality assessment Standards

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Ensemble of Vision Transformers and CNNs for Accurate Diabetic Foot Ulcer Classification

Ensemble of Vision Transformers and CNNs for Accurate Diabet...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： Arnold Anand Pagadala Salaja Silas Emmanuel Joy Division of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Diabetic Foot Ulcers (DFU) are a significant diabetes complication that can lead to lower limb amputation. Currently, 537 million suffer from diabetes worldwide and it is anticipated to rise to 783 million by 2045. Given the speed at which DFU is developing, prompt action is necessary to avoid the serious consequences of amputation and associated various medical conditions. With the evolution of image-based ML algorithms, automated methods for identifying and assessing DFUs are becoming more common. Existing research works on visual computing techniques concerns tissue classification and detects the DFU's visual appearance. This research study combines ResNet50 with Vision Transformers (ViT) and MobileNet with Vision Transformers (ViT) to develop an ensemble model to classify the existence or absence of a Diabetic Foot Ulcer (DFU). Experimental results of the proposed model when tested on challenging datasets achieved a validation accuracy of 98.6%.

关键词： Visualization Computational modeling Neural networks Transformers Diabetes Classification algorithms Cognitive robotics

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters 3

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, M. Siva Saranya, N. Gopan, Gokul Baig, Rahmath Ulla Josha Daniel, S. Babu, M. Sns College of Technology Department of Ece India Karpagam College of Engineering Department of Artificial Intelligence and Data Science India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Hindustan College of Engineering and Technology Department of Eee India Hindusthan College of Engineering and Technology Master of Engineering in Embedded Systems India

ISBN: (纸本)9798331509675

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment. © 2025 IEEE.

关键词： MATLAB

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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Enhanced Brain Tumor Diagnosis through Multimodal Fusion and 3D Convolutional Neural Networks 2

Enhanced Brain Tumor Diagnosis through Multimodal Fusion and...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Belinda, Carmel Mary M.J. Alex David, S. Begum, Almas Kannan, E. Thangarasu, Gunasekar Department of Artificial Intelligence and Machine learning Saveetha School of Engineering Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Professional Industry Driven Education MAHSA University Saujana Putra Malaysia

ISBN: (纸本)9798350305418

Brain tumor detection has enhanced using a Multi-CNNs with Multimodal Information Fusion method has been proposed in this work. The proposed method leverages multiple modalities, like T1-weighted, T2-weighted, and FLAIR images, to create a fused representation of the input data. A deep neural network architecture using 3D Convolutional Neural Networks (3D-CNNs) is constructed to capture spatial and volumetric information present in medical images. The method also incorporates a real normalization layer and a weighted loss function to improve the learning process and prioritize accurate detection and localization of tumor lesions. Evaluating our method on a Kaggle dataset of 253 images (98 without tumor, 155 with tumor), we achieve superior results compared to existing approaches. Our proposed method attains an accuracy of 98.9%, sensitivity of 99.2%, specificity of 99.1%, recall of 98.7%, precision of 99.3%, and F1 score of 98.9%. Leveraging multimodal fusion, 3D-CNNs, and additional enhancements, our study contributes to more reliable brain tumor diagnosis, benefiting patients and healthcare providers. © 2023 IEEE.

关键词： 3D Convolutional Neural Networks (3D-CNNs) Accuracy enhancement Multi-CNNs Multimodal Tumor detection

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Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering

Chain of Condition: Construct, Verify and Solve Conditions f...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lin, Jiuheng Lai, Yuxuan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science The Open University of China China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China

ISBN: (纸本)9798891761681

Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Nutrition Tracking: Webcam-Based Fruit and Vegetable Recognition and Calorie Measurement Using Image Processing and Convolutional Neural Networks

Enhancing Nutrition Tracking: Webcam-Based Fruit and Vegetab...

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Emerging Technology (INCET), International Conference for

作者： Sam Leo S Ruth Moly Benjamin Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The “Enhancing Nutrition Tracking” wander epitomizes a groundbreaking agreeable vitality between fake bits of knowledge (AI) and food science. Through the utilization of Convenient Net convolutional neural frameworks, this creative system passes on realtime normal item affirmation and correct calorie estimations, in this way empowering clients to make taught dietary choices. Central to its value is the visual emphasis on carbohydrate substance, ensuring clients have a comprehensive understanding of their food choices and dietary intake. This project's trademark lies in its user-friendly interface, arranged to streamline the strategy of capturing characteristic item and vegetable pictures for incite dietary bits of information. By reliably joining AI advancement into conventional assignments, individuals can effortlessly get to basic wholesome information, empowering more advantageous eating affinities and way of life choices. Besides, continuous ask almost endeavors search for to refine the system's realtime characteristic item recognizable confirmation capabilities and examine the immersive potential of virtual reality for in-depth wholesome assessments. By initiating the integration of AI and virtual reality advancements, the “Enhancing Nutrition Tracking” amplify focuses to catalyze a worldview move in food watching.

关键词： Procurement Visualization Tracking Virtual reality Trademarks Inventory management Planning

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DROPEX: Disaster Rescue Operations and Probing using EXpert drones

DROPEX: Disaster Rescue Operations and Probing using EXpert ...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Kausthub Kannan Aditya N Awati Smruthi S Rao Vindhya P Malagi Department of Computer Science And Design Dayananda Sagar College of Engineering Bangalore India Department of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Disasters, both natural and man-made, pose significant risks to human life and infrastructure, necessitating swift and efficient search and rescue (SAR) operations. Traditional SAR methods often struggle to access hazardous areas, resulting in delayed responses and increased risk to rescuers. These methods are unable to run automated simultaneous rescues which lead the operations to face challenges such as difficulty in quickly assessing damage, locating survivors, and delivering aid. This paper address the problem by proposing a autonomous swarm of drones framework which improves the response time as well as increases accessibility zone of the rescue operation. The proposed framework DROPEX, is an autonomous UAV (Unmanned Aerial Vehicle) which employs a dual-dome architecture with surveillance drones to detect individuals in distress and payload drones to deliver aid. This framework ensures rapid deployment, accurate navigation, and efficient data transmission in disaster-stricken areas while minimizing the need for manual intervention. The drones are able to recognise the victims in need using object detection models such as YOLO and Detection Transformer (DETR) with thermal vision. By using Long Range Wide Area Network (LoRaWAN) and object detection models, the drones are able to reduce the response time and increase accessibility. The main focus is on creating a robust, scalable, and economical system to enhance the speed, efficiency, and effectiveness of disaster rescue operations.

关键词： Accuracy Three-dimensional displays Navigation Biological system modeling Surveillance Disasters Reinforcement learning Real-time systems Time factors Drones

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