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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhagyalaxmi, K. Dwarakanath, B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Ramapuram India Srm Institute of Science and Technology Department of Information Technology Tamilnadu Ramapuram India

ISBN: (纸本)9798331529635

Brain tumors, characterized by abnormal cell growth within the brain, present significant challenges for early detection and accurate classification due to their complex and heterogeneous nature. Manual evaluation of MRI scans, though critical, is often laborintensive and prone to errors, highlighting the need for advanced, automated diagnostic methods. Convolutional Neural Networks (CNNs) have demonstrated strong potential in this domain by effectively learning hierarchical features from raw medical images to detect and classify brain tumors. However, single CNN models often encounter difficulties in handling the variability in tumor appearance, such as differences in shape, size, and intensity. To address these challenges, this research proposes an ensemble model that integrates pretrained CNN architectures-ResNet50, Inception, and Xception. The ensemble technique strengthens the adaptability and accuracy of brain tumor identification and categorization by incorporating the advantages of these distinct models. The proposed model was trained and evaluated using a large dataset of 5,316 MRI images, representing a range of brain tumor types, including gliomas, pituitary, meningiomas, and cases no tumor. With a high classification accuracy of 99.39%, our ensemble model proved to be useful in enhancing the reliability of diagnoses and assisting surgeons in making decisions on the treatment of brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging Machine Learning Models for Proactive Disaster Forecasting 1

Leveraging Machine Learning Models for Proactive Disaster Fo...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Affan, Daimi Syed Ahmed, Askari Arslaan Maaz, Ahmed Syed Asma, Iqbal Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350355611

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure. © 2025 IEEE.

关键词： Risk management

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Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition

Synergistic Spotting and Recognition of Micro-Expression via...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zou, Bochao Guo, Zizheng Qin, Wenfeng Li, Xin Wang, Kangsheng Ma, Huimin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Micro-expressions are involuntary facial movements that cannot be consciously controlled, conveying subtle cues with substantial real-world applications. The analysis of micro-expressions generally involves two main tasks: spotting micro-expression intervals in long videos and recognizing the emotions associated with these intervals. Previous deep-learning methods have primarily relied on classification networks utilizing sliding windows. However, fixed window sizes and window-level hard classification introduce numerous constraints. Additionally, these methods have not fully exploited the potential of complementary pathways for spotting and recognition. In this paper, we present a novel temporal state transition architecture grounded in the state space model, which replaces conventional window-level classification with video-level regression. Furthermore, by leveraging the inherent connections between spotting and recognition tasks, we propose a synergistic strategy that enhances overall analysis performance. Extensive experiments demonstrate that our method achieves state-of-the-art performance. The codes are available at https://***/zizheng-guo/ME-TST. © 2025 IEEE.

关键词： Long videos Micro-expression analysis State space model Synergistic spotting and recognition

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Using Machine Learning to Detect and Classify URLs: A Phishing Detection Approach 4

Using Machine Learning to Detect and Classify URLs: A Phishi...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mahesh Ananth Dheepthi Hindustan Institute of Technology and Science Computer Science and Engineering India

ISBN: (纸本)9798350300093

It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. The training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. This study has implemented a parallel data processing pipeline so that handle the large dataset. This pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users. © 2023 IEEE.

关键词： Statistical tests

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks 1

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Mouli, S.Sudhamsu Veeraiah, T. Mahesh, K. Singh, M.P. Ecole Centrale School of Engineering Mahindra University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350349658

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency. © 2024 IEEE.

关键词： Inductive power transmission

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Light People:*** May Lau,newly elected US NAE member in Hong Kong,talks about future of photonics and women in science

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Light(science & Applications) 2024年第4期13卷 602-608页

作者： Yating Wan Chenzi Guo Electrical and Computer Engineering the ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology23955-6900 ThuwalSaudi Arabia Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina

Editorial Photonics technology remains a driving force in today’s scientific landscape,marked by continuous innovation and crossdisciplinary *** an enlighting conversation with Light:science&Applications,*** May Lau,a pioneer in photonics research,shares her deep insights on the evolution of technologies of LEDs,lasers,challenges of hetero-epitaxy,and the future of micro-LEDs and quantum dot *** honored as a member of the US National Academy of engineering(NAE)for her significant contributions to photonics and electronics using III-V semiconductors on silicon,*** stands out as the sole Hong Kong scholar inducted into the NAE this year,joining 114 new and 21 international *** this exclusive Light People interview,*** shares her journey as a pioneering woman in engineering,her commitment to mentorship and academia,and her perspective on advancing female representation in *** summary provided is distilled from ***’s thoughtful responses during the *** a deeper exploration of ***’s experiences and advice,the full interview is available in the Supplementary material.

关键词： interview lighting driving

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EnsemBEP: A Robust Ensemble Model for Enhanced B-cell Epitope Prediction 8

EnsemBEP: A Robust Ensemble Model for Enhanced B-cell Epitop...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Karthiga, M. Shiv Navin, V.B. Cibin Kumar, R.M. Sathiya, B. Satheesh Kumar, A. Priyanga, M.A. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Erode India

ISBN: (纸本)9798350367904

B-cell epitope prediction has found its use in understanding B-cell recognition spots on a number of antigens that is vital in vaccine design and immunotherapy. These fields urgently need powerful prediction models, but the existing methods often fail to satisfy the requirement of both high sensitivity and high specificity. In response, this manuscript presents an ensemble model named EnsemBEP that combines Random Forest, Gradient Boosting, and XGBoost for improving the performance of B-cell epitope prediction. New model for epitope prediction, named EnsemBEP shows better performance in comparison with other models including EpiDope and BepiPred-2.0. The proposed EnsembEP model demonstrates superior performance compared to existing models such as EpiDope and BepiPred-2.0. Through rigorous evaluation, EnsemBEP achieved an AUC of 0.945, accuracy of 0.885, sensitivity of 0.823, specificity of 0.926, and an MCC of 0.743. All these findings point out that EnsemBEP yield better results than competing models thereby confirming that it is more accurate and reliable in predicting B-cell epitopes. The results provide evidence for the significance of ensemble learning methodologies in attaining higher predictive performance in immunoinformatic. © 2024 IEEE.

关键词： Prediction models

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Accurate and Efficient Code Matching Across Android Application Versions Against Obfuscation 31

Accurate and Efficient Code Matching Across Android Applicat...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Feng, Runhan Zhang, Zhuohao Zhou, Yetong Yan, Ziyang Zhang, Yuanyuan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350330663

In an effort to enhance the attractiveness of apps, developers consistently and frequently release updates to introduce new features and address known issues. Although frequent updates are beneficial for improving user experience, they also increase the workload for reverse engineers since existing analysis results may become obsolete after the release of a new version. Matching code across app versions can help reverse engineers quickly migrate existing analysis results to new versions, verifying whether their prior findings still hold in the new version. This allows them to focus more on the modified portions of the code, thus increasing reverse engineering efficiency. Nevertheless, existing techniques cannot effectively match the code of apps protected by obfuscation techniques, which are pervasively adopted in prac-tice. To address the challenges introduced by code obfuscation, this study presents MatchScope, a novel automated approach designed to match code at the method level across versions of Android app binaries. MatchScope effectively leveraging different levels of fine-grained code features, including class structures and method opcodes, etc., for similarity comparison, thus achieving high accuracy. To further enhance the matching efficiency, we design an index-aware matching algorithm, significantly reducing the scope and number of pairwise comparisons required compared with existing work. The critical insight of our algorithm lies in that the obfuscation tools usually rely on an incrementing index to generate obfuscated names for classes in a deterministic way. Our evaluation on 20 open-source and 60 real-world apps demonstrates the effectiveness of MatchScope. The precision and recall of MatchScope on the ground truth achieve 97.49 % and 92.34 %, respectively, which are 19.50 % and 30.74 % higher than the state-of-the-art tool. © 2024 IEEE.

关键词： Reverse engineering

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Machine Learning-based Citrus Plant Disease Detection and Management System using computer Vision 4

Machine Learning-based Citrus Plant Disease Detection and Ma...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Srinivasa Revanth Krishna, A. Sri Siva Sandeep Reddy, T. Dheepthi, R. Hindustan Institute of Technology and Science Computer Science and Engineering India

ISBN: (纸本)9798350300093

This article presents a novel method for identifying and managing diseases that affect citrus fruits by utilizing cutting-edge computer vision and machine learning techniques. The method is presented in this article. The proposed system utilizes a Convolutional Neural Network (CNN) model to extract features from images and classify them as healthy or diseased. Following this, a Random Forest algorithm is used to make the final prediction based on the extracted features. The system enables farmers to easily upload images of their citrus fruits from mobile or web-based platforms, enabling instant diagnosis of diseases and providing effective management plans to address the issue. The design of the proposed system is centered on enhancing the performance of the model in recognizing patterns that are associated with healthy and diseased fruits. In addition to this, it features an automatic management plan generation and alert system, both of which are designed to prompt farmers to take prompt actions for the prevention and control of diseases. The purpose of the proposed system is to deliver an instrument that is both effective and simple to use for the diagnosis and treatment of diseases, which will ultimately result in increased crop yields and increased profitability for citrus growers. The agricultural sector as a whole intends to gain significantly from the successful implementation of this system, which has the potential to bring about significant improvements. © 2023 IEEE.

关键词： computer vision

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection

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computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： Oriented object detection transformer deep learning

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