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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation 4

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kolekar, Samruddhi Maheshkumar Yadav, Agnesh Chandra Yadav, Suchita Dash, Daba Prasad Thaper Institue of Engineering and Technology Dept. Electrical Engineering Patiala India India Instite of Technology Patna Dept. Electrical Engineering Patna India Bennett University Dept. of Computer Science and Engineering Gr. Noida India

ISBN: (纸本)9798350364729

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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AI-Driven Power Gating for Enhanced Energy Efficiency in Superscalar Processors 31

AI-Driven Power Gating for Enhanced Energy Efficiency in Sup...

引用

31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Kalra, Naman Tudu, Jaynarayan T. Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798331509118

Amid the rising demand for efficient processors, the challenge has always been to reduce power consumption without compromising performance. FinFET technology has significantly reduced leakage power issues, but dynamic power consumption at lower nodes has reemerged as a concern. Methodologies such as power/clock gating, DVFS, and voltage biasing have been proposed in the past. However, next-generation complex SoC designs at © 2024 IEEE

关键词： System-on-chip

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Smart Attendance Management System Using Face Recognition 1

Smart Attendance Management System Using Face Recognition

引用

1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rawat, Shivam Goyal, Shweta Rawat, Nikita Graphic Era Hill University Dept. Computer Science & Engineering Dehradun India Graphic Era deemed to be University Dept. of Electrical Engineering Dehradun India

ISBN: (纸本)9798331518578

Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This paper presents a Smart Attendance Management System that leverages facial recognition technology to automate the attendance process. By using cameras to capture images of students, the system recognizes their faces and marks their attendance in real time. Developed in Python, the system employs the K-Nearest Neighbours (KNN) algorithm for accurate face recognition. Once attendance is recorded, it is stored in a CSV file and can be visualized using the flask web application. The proposed system significantly reduces manual effort, ensures accuracy, and operates efficiently in real-time environments. © 2025 IEEE.

关键词： Nearest neighbor search

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Intelligent Resource Allocation for RIS-Assisted Cell-Free Massive MIMO Systems 22

Intelligent Resource Allocation for RIS-Assisted Cell-Free M...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Mollah, Gloria Haider, Majumder Rawat, Danda B. Ahmed, Imtiaz Howard University Dept. of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798331508050

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme. © 2025 IEEE.

关键词： Deep neural networks

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Futuristics Technologies for Reduction of Plastic Waste 13

Futuristics Technologies for Reduction of Plastic Waste

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Dhiman, Riya Sharma, Ashish Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Electrical Engineering Mohali India

ISBN: (纸本)9798350380583

Plastic plays a vital role in daily life most of the food items, Baggies, vegetables are packed in plastic material. As plastic is non-biodegradable there are harming effect on human beings and animals. Due to these global warming also increases. Although 80% Fishes, 88% Vertebrates, 56% Mammals and 52% Reptiles are affected due to plastic waste. In this work with the various techniques proposed like Eco-bricks, Furniture, Replace Plastic By Bamboo and also Doormats are made through the Plastic Waste. It is seen that by using above techniques significantly reduce the harmful effect of plastic and there is improvement in life of human beings and animals also the alternative techniques to reuse the plastic and to replace plastic are proposed with which there is a increase in the earning of poor peoples. Above techniques are helpful to decrease the employment and also by using these techniques more than 60% of plastic reuse, with this the temperature is also decreased. © 2024 IEEE.

关键词： Invertebrates

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening 3

Bio-inspired Heuristic Optimization-based Cascaded Network f...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Shuvo, Eshat Ahmad Rahman, Wahidur Hossain, Md Shakhawat Islam, Md. Tarequl Iqbal, Md. Sadiq Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Center Independent University Dept. of Computer Science & Engineering & RIoT Bangladesh Khwaja Yunus Ali University Dept. of Computer Science & Engineering Sirajganj Bangladesh

ISBN: (纸本)9798350388282

Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features;the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening. © 2024 IEEE.

关键词： Large datasets

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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22nd International Learning and Technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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