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40th IEEE International Conference on data engineering, ICDE 2024

作者： Jensen, Christian S. Yang, Bin Guo, Chenjuan Hu, Jilin Torp, Kristian Aalborg University Department of Computer Science Aalborg Denmark School of Data Science & Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350317152

The unprecedented availability of new types of data coupled with the invention of new technologies combine to enable entirely new or higher-resolution services that in turn enable more rational and data-driven processes. We consider the overall process of vehicular transportation and, more specifically, the process of deciding which route to follow when having to reach a destination. Early solutions modeled a road work as a graph, used sparse in-road sensor data to assign weights to graph edges, and then applied improved versions of Dijkstra's algorithm to find routes with the lowest sums of edge weights. Since then, massive vehicle trajectory data has become available. When coupled with new technologies, this data enables entirely new and higher-resolution routing services that in turn enable better routing. For more than a decade, the authors have engaged in research aimed at exploiting trajectory data to enable better routing. The resulting technologies were developed outside a DBMS. Here, we cover aspects of this research. Further, we challenge the community to develop DBMS support for these and other aspects of routing. © 2024 IEEE.

关键词： database systems

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NR-Router+: Enhanced Non-Regular Electrode Routing With Optimal Pin Selection for Electrowetting-on-Dielectric Chips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第9期43卷 2606-2619页

作者： Pan, Youlin Liu, Genggeng Huang, Xing Li, Zipeng Huang, Hsin-Chuan Liang, Chi-Chun Wang, Qining Kim, Chang-Jin Ho, Tsung-Yi Northwestern Polytechnical University School of Computer Science Xi'an710072 China Fuzhou University College of Computer and Data Science Fuzhou350116 China Apple Inc. Silicon Engineering Group CupertinoCA95014 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan University of California at Los Angeles Mechanical and Aerospace Engineering Department Los AngelesCA90095 United States The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are proposed, which allow more reliable droplet operations and facilitate the integration of optical sensors for many biochemical applications. Furthermore, nonregular electrode designs are utilized in EWOD chips, e.g., interdigitated electrodes for more reliable droplet manipulation, custom shaped electrodes for specific applications like concentric heating, etc. However, due to the technical challenges of fabricating multilayer interconnection on the glass substrate, e.g., unreliable process and high cost, both control electrodes and wires are fabricated with a single-layer configuration, which poses significant challenges to pin selection for nonregular electrodes. In this article, we propose a minimum-cost flow-based routing algorithm called NR-Router+ that features efficient and robust routing for single-layer EWOD chips with nonregular electrodes. To the best of our knowledge, this is the first work that overcomes the aforementioned challenges. We construct a minimum-cost flow algorithm to generate optimal routing paths followed by a light-weight model to handle flow capacity. A grid reduction strategy is proposed to reduce the computational overhead. Additionally, a flow collocation algorithm based on integer linear programming is presented to efficiently prevent wire overlapping. Experimental results show that NR-Router+ achieves 100% routability while minimizing wirelength with shorter run time. Moreover, NR-Router+ can generate mask files feasible for manufacturing via adjustments of design parameters, thus demonstrating its robustness and efficiency. © 1982-2012 IEEE.

关键词： Electrodes

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A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 990-1004页

作者： Xiaohua Ge Qing-Long Han Jun Wang Xian-Ming Zhang IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Computer Science and School of Data Science City University of Hong KongChina

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external *** central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction *** this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle ***,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is *** is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is *** simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

关键词： Adaptive control collision avoidance distributed formation control multi-vehicle systems neural networks obstacle avoidance repulsive potential

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Robustness of Explainable AI Algorithms for Disease Biomarker Discovery from Functional Connectivity datasets

Robustness of Explainable AI Algorithms for Disease Biomarke...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Girish, Deepank Chan, Yi Hao Gupta, Sukrit Xia, Jing Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore Indian Institute of Technology Department of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350351552

Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies focused on improving model performances for disease detection. An increasing number of such studies use their trained models to detect and evaluate salient features that could be potential biomarkers of these neuro-logical conditions. The evaluation of these salient features is often qualitative and limited to cross-referencing existing literature for similar findings. In this study, we present objective quantitative metrics to evaluate the robustness of these salient features. Building upon existing generic evaluation metrics, we propose metrics that capture topological properties known to be characteristic of brain functional connectomes. Using existing and newly proposed measures on a set of baselines and state-of-the-art graph neural networks (GNN) models, we found that when GNNExplainer is used with models that incorporate attention, the scores produced are relatively more robust than other combinations. On datasets of patients with Autism Spectrum Disorder (ASD) or Attention-deficit Hyperactivity Disorder (ADHD), our proposed metrics highlighted that salient features identified in both disorders are highly involved in functional specialization, while salient ASD features expressed stronger functional integration than ADHD. We package these existing and novel metrics together in the RE-CONFIRM framework that holds promise to set the foundations for the quantitative evaluation of salient features detected by future studies. © 2024 IEEE.

关键词： Graph neural networks

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Wireless Sensor Network-Driven IoT Solution for Advanced Borewell Rescue Robotics 4

Wireless Sensor Network-Driven IoT Solution for Advanced Bor...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Meera, V. Mercia, Sharon Nesa PadmaPriya, J. Murugavalli, S. Panimalar Engineering College Artificial Intelligence And Data Science Chennai City Campus Chennai India Panimalar Engineering College Computer Science Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai City Campus Chennai India

ISBN: (纸本)9798350387063

The fact that deaths from bore wells persist in India is highly alarming, particularly when young people are involved. Since 2009, there have been more than 40 documented child deaths, and the National Disaster Response Force (NDRF) reported a worrisome 70% failure rate for rescue efforts. The solution must be found quickly and effectively. In response to this humanitarian problem, this paper has proposed a novel design for a robotics device. This proposed system is a creative illustration of how human interaction and technology may be used in tandem to swiftly evaluate the victims and guarantee their safe release. The main element of this solution is a skillfully designed robotic arm that smoothly integrates Internet of Things (IoT) devices (Rasp Pi or Arduino) and is powered by high-precision servo motors. Among the many features it offers are an advanced APR (Auto Playback Recorder) system, an infrared-based CCTV camera, a strong LED lighting system, advanced sensors (Passive Infrared, Ultrasonic Distance Measurement, Heartbeat, Temperature, and Gas), and a dual-functional mask. Artificial intelligence (AI) is another tactic that will be used in the rescue operation. This paper discusses the pressing need for safe, efficient bore well rescues and offers a comprehensive analysis of the methodology. This project uses cutting edge technology and human collaboration to stop more catastrophes and increase the speed and effectiveness of life-saving procedures in emergency situations. In an effort to contribute to the creation of a more secure and safe future, this research is presented as a critical first step in lowering the death toll related to Indian bore wells. © 2024 IEEE.

关键词： Wireless sensor networks

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Utilizing Attention-based Vision Transformer (AtViT) for Brain Tumour Detection

Utilizing Attention-based Vision Transformer (AtViT) for Bra...

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2024 International Conference on Future Technologies for Smart Society, ICFTSS 2024

作者： Chaubey, Harshit Kumar Arelli, Siri Agrawal, Karan Gopalaiyengar, Srinivasa K. Iiit Computer Science and Engineering Naya Raipur Raipur India Iiit Data Science and Artificial Intelligence Naya Raipur Raipur India

ISBN: (纸本)9798350373844

This research explores the use of Attention based Vision Transformers (AtViT), a type of neural network, for the detection of brain tumour from MRI scans. Traditional methods of tumour detection rely heavily on the expertise of neuroradiologists and often involve time-consuming processes. Our project aims to automate this process, potentially increasing both the accuracy and speed of diagnosis. We used a dataset of 3,064 MRI images that included three different types of brain tumour: Meningioma, Pituitary tumour, and Glioma. By employing a pretrained Vision Transformer model, which we fine-tuned for our specific task, we achieved a high accuracy of 99.61% with the EfficientFormer model. We compared this to the ResNet-50 model, a traditional architecture, which demonstrated the superior capability of Vision Transformers in handling complex image classification tasks like tumour detection. The findings suggest that Vision Transformers can significantly aid in the quick and accurate diagnosis of brain tumour, offering a promising tool for medical professionals. This study lays the groundwork for further exploration into the integration of advanced neural networks in medical diagnostics. © 2024 IEEE.

关键词： Diagnosis

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence-Based Student Computational Thinking Analysis Model (AI-SCTAM) for Curriculum Development 2

Artificial Intelligence-Based Student Computational Thinking...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sowmiya, S. Umar Farook, R. Giridharan, B. Shalini, A. Airtifical Intellegence and Data Science Karpagam Academy Higher Education Coimbatore India Airtifical Intellegence and Data Science Karpagam Academy Higher Education India Computer Science and Engineering Karpagam Academy Higher Education Coimbatore India

ISBN: (纸本)9798331505745

Computational thinking (CT) has been widely used to improve students' comprehension of mathematical and scientific subjects. However, there is a scarcity of information available regarding the challenges faced by students and the development of their conceptual knowledge in these learning environments, particularly in relation to the integrated curriculum. There is a need to help with course-level curriculum development to improve teaching and learning. Hence, this paper proposes an artificial intelligence-based student computational thinking analysis model (AI-SCTAM) to predict student engagement in learning and improve curriculum development. This research attempted to determine the association between student engagement measures using the machine learning (ML) method to determine the degrees of student involvement. The ML divides students into two groups: highly engaged and poorly engaged, according to the criteria used to measure their degree of involvement. The findings of the correlation study showed a positive association between engagement measures and performance. Analyzing the relationship between student engagement and performance will guide the development of course materials and activities. STEM education metrics based on engagement levels will be proposed to personalize students' learning experiences. © 2025 IEEE.

关键词： Curricula

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3D E-commerce Using AI Depth Algorithm Augmented Reality 8th

3D E-commerce Using AI Depth Algorithm Augmented Reality

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Silviya, S. H. Annie Devi, S. Renuka Sriman, B. Aswinrani, M. Raja, S. Sunitha, T. Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9789819784219

In today's dynamic world of online shopping, augmented reality (AR) integration has emerged as a game-changing innovation. It transcends the limitations of traditional online shopping by harnessing AR technologies, such as Google's ARCore, to create immersive 3D product models. These models empower customers to virtually interact with and visualize products in their own environment, effectively bridging the experiential gap with in-store shopping. Employing AR software development kits and game engines, these 3D models seamlessly come to life on customers’ devices, offering a more engaging and informative shopping experience. This not only bolsters customer satisfaction but also helps online retailers manage operational costs more efficiently. With AR’s ability to let customers virtually try on clothes, visualize furniture placements, and inspect products from the comfort of their homes, it's poised to reshape the e-commerce landscape. The ongoing technological revolution is characterized by increasingly immersive, personalized online shopping, setting new benchmarks for customer expectations and retail success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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