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Lead-cnn: lightweight enhanced dimension reduction convolutional neural network for brain tumor classification

International Journal of Machine Learning and Cybernetics 2025年 1-20页

作者： Khan, Saif Ur Rehman Asif, Sohaib Bilal, Omair Rehman, Hafeez Ur School of Computer Science and Engineering Central South University 932 Lushan S Rd Yuelu District Hunan Changsha China School of Computing and Data Sciences Oryx Universal College with Liverpool John Moores University Doha Qatar Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou Campus of Zhejiang Cancer Hospital Taizhou Cancer Hospital Taizhou317502 China Chinese Academy of Sciences Taizhou317502 China Chinese Academy of Sciences Hangzhou310022 China

In many nations, brain tumors are among the most serious medical conditions affecting both adults and children. Timely and accurate diagnosis is crucial, as the life of a patient may be shortened if brain tumors are not identified promptly. When brain tumors are diagnosed accurately and promptly, patients can receive the appropriate treatment. One of the challenging aspects of dealing with brain tumors is their diversity, making it difficult to categorize them accurately due to their varying characteristics. Deep learning (DL) models have emerged as a valuable tool in classifying brain tumors into distinct groups. However, these models face challenges related to accuracy and computational cost, necessitating advancements in brain image classification. Our primary aim is to develop a lightweight convolutional neural network (CNN) architecture that offers high efficiency, we incorporate a modified dimension reduction block with CNN to detect brain cancers with minimal computational resources while maintaining a high level of accuracy. In this research, we introduce a novel lightweight network called LEAD-CNN, designed specifically for the accurate and reliable identification of brain cancers from magnetic resonance images (MRI). Our approach begins by evaluating the effectiveness of several pre-trained DL models, including ResNet-101, VGG-19, MobileNet-V1, and DenseNet-201, which have been recommended for the classification of medical images. Subsequently, we propose a dimension reduction base CNN architecture with a lightweight design aimed at achieving a low false-negative rate in tumor image classification for patients. To assess the performance of the proposed model, we utilize a benchmark brain MRI dataset (Kaggle) comprising 7023 images, encompassing three different types of brain tumors long with normal brain images. Our experimental results are demonstrating an overall classification accuracy of 98.70%, with an average recall, F1-score, and precision of 98.60%

关键词： Convolutional neural networks

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AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

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

作者： Tidke, Bharat Pragadeeswaran, S. Lakshmi, R. Poornima Bhuvaneswari, G. Manikandan, G. Murugan, S. University Nagpur Campus Pune India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering college Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Kavaraipettai Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities. © 2025 IEEE.

关键词： Sustainable city

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An agent-based model of citation behavior

arXiv

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arXiv 2025年

作者： Chacko, George Park, Minhyuk Ramavarapu, Vikram Grama, Ananth Robles Granda, Pablo Warnow, Tandy Siebel School of Computing and Data Science Grainger College of Engineering University of Illinois Urbana-ChampaignIL United States Department of Computer Science Purdue University United States

Whether citations can be objectively and reliably used to measure productivity and scientific quality of articles and researchers can, and should, be vigorously questioned. However, citations are widely used to estimate the productivity of researchers and institutions, effectively creating a ‘grubby’ motivation to be well-cited. We model citation growth, and this grubby interest using an agent-based model (ABM) of network growth. In this model, each new node (article) in a citation network is an autonomous agent that cites other nodes based on a ‘citation personality’ consisting of a composite bias for locality, preferential attachment, recency, and fitness. We ask whether strategic citation behavior (reference selection) by the author of a scientific article can boost subsequent citations to it. Our study suggests that fitness and, to a lesser extent, out degree and locality effects are influential in capturing citations, which raises questions about similar effects in the real world. © 2025, CC BY.

关键词： Autonomous agents

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BRAINGUARD: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities

arXiv

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arXiv 2025年

作者： Tian, Zhibo Quan, Ruijie Ma, Fan Zhan, Kun Yang, Yi School of Information Science and Engineering Lanzhou University China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China

Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused on training separate models for each individual to account for individual variability in brain activity, overlooking valuable cross-subject commonalities. Recent advancements have explored multisubject methods, but these approaches face significant challenges, particularly in data privacy and effectively managing individual variability. To overcome these challenges, we introduce BRAINGUARD, a privacy-preserving collaborative training framework designed to enhance image reconstruction from multisubject fMRI data while safeguarding individual privacy. BRAINGUARD employs a collaborative global-local architecture where individual models are trained on each subject’s local data and operate in conjunction with a shared global model that captures and leverages cross-subject patterns. This architecture eliminates the need to aggregate fMRI data across subjects, thereby ensuring privacy preservation. To tackle the complexity of fMRI data, BRAINGUARD integrates a hybrid synchronization strategy, enabling individual models to dynamically incorporate parameters from the global model. By establishing a secure and collaborative training environment, BRAINGUARD not only protects sensitive brain data but also improves the image reconstructions accuracy. Extensive experiments demonstrate that BRAINGUARD sets a new benchmark in both high-level and low-level metrics, advancing the state-of-the-art in brain decoding through its innovative design. © 2025, CC BY.

关键词： Differential privacy

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy 23

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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A Real Time approach for detecting and counting Vehicles using OpenCV and NumPy

A Real Time approach for detecting and counting Vehicles usi...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Suneetha, Irala Prathima, Ch. Sirisala, Srinivasulu Mahalakshmi, S.S.D.K. Balaji Nanda Kumar Reddy, K. Sambana, Bosubabu Annamacharya Institute of Technology and Sciences Ece Department Tirupati India Mohan Babu University Dept. of Ds Tirupati India Cvr College of Engineering Department of Cse Telagana India Lendi Institute of Engineering and Technology Department of Cse Vizianagaram India Annamacharya Institute of Technology and Sciences Eee Department Tirupati India Mohan Babu University School of Computing Department of Data Science Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331508685

Vehicle detection and counting using OpenCV is a crucial aspect of modern traffic management systems. This mini project aims to develop a robust solution for detecting vehicles in real-time video streams and accurately counting them as they pass through a designated area. OpenCV, a popular computer vision library, is utilized for its extensive functionality and ease of implementation. First, the video stream is captured and processed to extract individual frames. Subsequently, each frame undergoes preprocessing techniques such as resizing, grayscale conversion, and noise reduction to enhance vehicle detection accuracy. Vehicle detection is achieved through the implementation of a deep learning- based object detection algorithm, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). These algorithms are trained on large datasets to recognize vehicles with high precision. Once vehicles are detected, a tracking algorithm is employed to maintain continuity between frames and accurately count the number of vehicles passing through the specified region of interest. This tracking algorithm ensures that vehicles are not double-counted and provides a reliable count for traffic analysis purposes. © 2025 IEEE.

关键词： Noise abatement

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Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS Digital Twin

arXiv

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arXiv 2025年

作者： Al-Shareeda, Sarah Celik, Yasar Bilgili, Bilge Al-Dubai, Ahmed Canberk, Berk Department of AI and Data Engineering Istanbul Technical University Turkey BTS Group Turkey The Ohio State University United States School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time and track their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance’s location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle’s next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN’s key role in significantly reducing the witnessed gap within the HITS’s DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%. © 2025, CC BY.

关键词： Deep neural networks

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing

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IEEE Transactions on Multimedia 2025年

作者： Ji, Yuzhu Zheng, Chuanxia Cham, Tat-Jen Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China University of Oxford Department of Engineering Science Oxford United Kingdom Nanyang Technological University College of Computing and Data Science Singapore Singapore

Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in results, it remains challenging for methods with 2D body landmarks, skeleton and semantic mask to accurately capture correspondences between source and driving poses due to the large variation in motion and articulation complexity. In addition, the accuracy and precision of DensePose degrade the image quality for neural-rendering-based methods. To address the limitations and by both considering the importance of appearance and geometry for motion transfer, in this work, we proposed a unified framework that combines multi-scale feature warping and neural texture mapping to recover better 2D appearance and 2.5D geometry, partly by exploiting the information from DensePose, yet adapting to its inherent limited accuracy. Our model takes advantage of multiple modalities by jointly training and fusing them, which allows it to robust neural texture features that cope with geometric errors as well as multi-scale dense motion flow that better preserves appearance. Experimental results with full and half-view body video datasets demonstrate that our model can generalize well and achieve competitive results, and that it is particularly effective in handling challenging cases such as those with substantial self-occlusions. © 1999-2012 IEEE.

关键词： Mapping

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