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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Nurhusna Salsabila Apri Siswanto Luhur Bayuaji Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. The system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. The architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. The results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. This innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security.

关键词： Software testing Face recognition System performance Smart homes Chatbots Real-time systems Software reliability Security Face detection Solenoids

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MedScale-Former: Self-guided multiscale transformer for medical image segmentation

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Medical Image Analysis 2025年 103卷 103554页

作者： Karimijafarbigloo, Sanaz Azad, Reza Kazerouni, Amirhossein Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods for medical image segmentation face significant challenges due to their reliance on extensive labeled training data. To address this limitation, our novel approach introduces a dual-branch transformer network operating on two scales, strategically encoding global contextual dependencies while preserving local information. To promote self-supervised learning, our method leverages semantic dependencies between different scales, generating a supervisory signal for inter-scale consistency. Additionally, it incorporates a spatial stability loss within each scale, fostering self-supervised content clustering. While intra-scale and inter-scale consistency losses enhance feature uniformity within clusters, we introduce a cross-entropy loss function atop the clustering score map to effectively model cluster distributions and refine decision boundaries. Furthermore, to account for pixel-level similarities between organ or lesion subpixels, we propose a selective kernel regional attention module as a plug and play component. This module adeptly captures and outlines organ or lesion regions, slightly enhancing the definition of object boundaries. Our experimental results on skin lesion, lung organ, and multiple myeloma plasma cell segmentation tasks demonstrate the superior performance of our method compared to state-of-the-art approaches. © 2025

关键词： Self-supervised learning

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Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

arXiv

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arXiv 2025年

作者： Taha, Ahmed M. Aly, Salah A. Darwish, Mohamed F. Dept. of CE Faculty of Engineering Egypt University of Informatics Cairo Egypt Faculty of Computing and Data Science Badya University Giza Egypt Faculty of Science Fayoum University CS&Math Branch Fayoum Egypt Dept. of Pathology Faculty of Medicine Badya University Giza Egypt

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuraies;and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics. © 2025, CC0.

关键词： Magnetic resonance imaging

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Optimizing hyperparameters for dual-attention network in lung segmentation

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Intelligence-Based Medicine 2025年 11卷 100221-100221页

作者： Wahyuningrum, Rima Tri Fadillah, Rizki Abdil Yunita, Indah Satoto, Budi Dwi Muntasa, Arif Sari, Amillia Kartika Rahardjo, Paulus Dewi, Deshinta Arrova Bauravindah, Achmad Department of Informatics Engineering Faculty of Engineering University of Trunojoyo Madura Indonesia Department of Health Faculty of Vocational Studies Universitas Airlangga Indonesia Department of Computer Science and Technology Faculty of Data Science and Information Technology INTI International University Malaysia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Indonesia

Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis. © 2025 The Authors

关键词： Chest X-ray images DANet Epoch Hyperparameter Learning rate Lung segmentation Process innovation

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN Computer science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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FLTrans-Net: Transformer-based feature learning network for wheat head detection

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Computers and Electronics in Agriculture 2025年 229卷

作者： Yousafzai, Samia Nawaz Nasir, Inzamam Mashood Tehsin, Sara Fitriyani, Norma Latif Syafrudin, Muhammad Department of Computer Science HITEC University Taxila Taxila Pakistan Seoul05006 Korea Republic of Faculty of Informatics Kaunas University of Technology Kaunas51368 Lithuania Department of Artificial Intelligence and Data Science Sejong University Seoul05006 Korea Republic of

The accurate identification of wheat heads is critical for assessing production and managing agricultural fields effectively. One major challenge lies in detecting overlapping and small wheat spikes amidst complex backgrounds. Recent advancements in deep learning, specifically Convolutional Neural Networks (CNNs), have been employed to address this challenge. However, conventional CNNs often struggle with capturing global interdependencies due to their focus on local features and limited scale invariance. To overcome these limitations, this study proposes a Transformer-based Feature Learning Network (FLTrans-Net). FLTrans-Net aims to learn discriminative features while reducing background noise. The methodology comprises three key components: a Multi-Scale Fusion Block, a Spatial Attention Block, and a Lightweight RetinaNet Detection Block. The Multi-Scale Fusion Block integrates high-scale and low-scale features to extract multi-scale representations, essential for detecting small-sized wheat heads in complex backgrounds. The Spatial Attention Block utilizes transformer encoders to highlight salient object characteristics, enhancing the model's performance. Additionally, a Lightweight RetinaNet Detection Block is incorporated to capture potentially valuable features extracted by preceding blocks. Experimental results demonstrate FLTrans-Net's effectiveness in wheat head detection in challenging field environments, indicating its potential for real-time deployment on resource-constrained devices. Overall, FLTrans-Net represents a significant advancement in wheat head detection, offering both efficacy and efficiency in handling complex agricultural landscapes. © 2024 Elsevier B.V.

关键词： Light-weight Detection Multi-Scale Fusion Spatial Attention Transformer Wheat Head Detection

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Design and evaluation of Lizard-Inspired Single-Actuated robot

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ROBOMECH Journal 2025年第1期12卷 1-17页

作者： Nansai, Shunsuke Kamamichi, Norihiro Faculty of Informatics and Data Science Akita University 1-1 Tegata-Gakuen Machi Akita010-8502 Japan Department of Robotics and Mechatronics Tokyo Denki University 5 Senju-Asahi-Cho Tokyo Adachi120-8551 Japan

The purpose of this paper is to develop a new Lizard-Inspired Single-Actuated (LISA) robot prototype capable of achieving the theoretical maximum input angle. Various morphologies for one-degree-of-freedom (1-DOF) robots have been proposed in previous studies. However, these designs face challenges due to their morphology, such as the need for toe slippage during walking. LISA introduces a new multi-legged morphology that overcomes these disadvantages, allowing propulsion and turning in 1-DOF. In this paper, we develop a new LISA prototype with the design requirement of achieving the theoretical maximum input angle while maintaining the robot’s morphology. Three key design points were identified to meet this requirement. The first is to design collision-free link geometry even at the maximum input angle. The second is to develop a push-back mechanism for posture recovery. The third is to implement balancing mechanisms. To evaluate the performance of the new prototype, we formulated the kinematics of LISA. The kinematic formulation defines the robot’s coordinate system, termed "robot coordinate," and derives key parameters such as turning angle, stride length, posture, direction of travel, and curvature. We then experimentally evaluated the new prototype through two types of tests: a wobble and mechanism evaluation, and a motion performance evaluation. In the wobble and mechanism evaluation, we verified the reproducibility of the newly designed balance mechanism, the ability to achieve the maximum input angle, and the effectiveness of the push-back mechanism. Wobble and the angles of each link during walking were quantitatively measured. The results confirmed that the new LISA prototype meets the design requirement of achieving the maximum input angle and can produce a stable gait. In the motion performance evaluation, we compared the theoretical kinematics with LISA’s actual motion. Measurements from motion capture and angle sensors were compared with the theoretica

关键词： Machine design

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DSLR-CNN: Efficient CNN Acceleration using Digit-Serial Left-to-Right Arithmetic

arXiv

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arXiv 2025年

作者： Nisar, Malik Zohaib Ibrahim, Muhammad Sohail Gorgin, Saeid Usman, Muhammad Lee, Jeong-A Department of Computer Engineering College of IT Convergence Chosun University Gwangju61452 Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany Tehran33535-111 Iran

Digit-serial arithmetic has emerged as a viable approach for designing hardware accelerators, reducing interconnections, area utilization, and power consumption. However, conventional methods suffer from performance and latency issues. To address these challenges, we propose an accelerator design using left-to-right (LR) arithmetic, which performs computations in a most-significant digit first (MSDF) manner, enabling digit-level pipelining. This leads to substantial performance improvements and reduced latency. The processing engine is designed for convolutional neural networks (CNNs), which includes low-latency LR multipliers and adders for digit-level parallelism. The proposed DSLR-CNN is implemented in Verilog and synthesized with Synopsys design compiler using GSCL 45nm technology, the DSLR-CNN accelerator was evaluated on AlexNet, VGG-16, and ResNet-18 networks. Results show significant improvements across key performance evaluation metrics, including response time, peak performance, power consumption, operational intensity, area efficiency, and energy efficiency. The peak performance measured in GOPS of the proposed design is 4.37× to 569.11× higher than contemporary designs, and it achieved 3.58× to 44.75× higher peak energy efficiency (TOPS/W), outperforming conventional bit-serial designs. © 2025, CC BY.

关键词： Convolutional neural networks

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Quadratic Sets and (3mod5)-Arcs in PG(r,5) 20th

Quadratic Sets and (3mod5)-Arcs in PG(r,5)

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20th EAI International Conference on Computer science and Education in Computer science, CSECS 2024

作者： Landjev, Ivan Kurz, Sascha Rousseva, Assia Bulgarian Academy of Sciences Institute of Mathematics and Informatics 8 Acad G. Bonchev str. Sofia1113 Bulgaria New Bulgarian University 21 Montevideo str. Sofia1618 Bulgaria Mathematisches Institut Universität Bayreuth Bayreuth95440 Germany Department of Data Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Faculty of Mathematics and Informatics Sofia University 5 J. Bourchier blvd. Sofia1164 Bulgaria

To goal of this contribution is to provide a characterization of the (3mod5)-arcs in PG(r,5), r≥4. Such arcs are either lifted or quadratic and by a construction described by Kurz-Landjev-Pavese-Rousseva. &... 详细信息

ISBN: (纸本)9783031843112

关键词： (tmodq)-arcs divisible arcs quadrics

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SINK-FREE ORIENTATIONS: A LOCAL SAMPLER WITH APPLICATIONS

arXiv

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arXiv 2025年

作者： Anand, Konrad Freifeld, Graham Guo, Heng Wang, Chunyang Wang, Jiaheng School of Informatics University of Edinburgh Informatics Forum EdinburghEH8 9AB United Kingdom State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University 163 Xianlin Avenue Jiangsu Province Nanjing China Faculty of Informatics and Data Science University of Regensburg Bajuwarenstrasse 4 Regensburg93053 Germany

For sink-free orientations in graphs of minimum degree at least 3, we show that there is a deterministic approximate counting algorithm that runs in time O((n73/Ε72) log(n/Ε)), a near-linear time sampling algorithm, and a randomised approximate counting algorithm that runs in time O((n/Ε)2 log(n/Ε)), where n denotes the number of vertices of the input graph and 0 Copyright © 2025, The Authors. All rights reserved.

关键词： Graph algorithms

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