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DXDSENet-CM model: an ensemble learning model based on depthwise Squeeze-and-Excitation ConvMixer architecture for the classification of multi-class skin lesions

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Multimedia Tools and Applications 2025年第12期84卷 9903-9938页

作者： Fırat, Hüseyin Üzen, Hüseyin Department of Computer Engineering Faculty of Engineering Dicle University Diyarbakır Turkey Department of Computer Engineering Faculty of Engineering and Architecture Bingol University Bingol Turkey

Skin cancer is acknowledged as the most prevalent form of cancer on a global scale. Failure to detect it in its initial phases can lead to fatality, underscoring the significance of early diagnosis. While visible to the naked eye, the considerable similarities between different classes and the variations within a single class create substantial hurdles for accurate detection. Given the widespread occurrence of this ailment, numerous deep learning-driven automated systems have been created to aid dermatologists in promptly identifying skin anomalies. Within this investigation, an ensemble learning architecture based on Xception, DenseNet201 and Depthwise Squeeze-and-Excitation Network and ConvMixer (DSENet-ConvMixer) architectures is presented for skin lesion classification. Xception, a pre-trained CNN network, utilizes depthwise separable convolutions and residual connections, offering efficient feature extraction from skin lesion images. DenseNet201’s parameter efficiency, feature reuse, and multi-scale information capture make it a valuable addition. DSENet-ConvMixer integrates these components, efficiently merging low-level and high-level features to enhance training and network efficiency. ConvMixer excels at feature extraction, enhancing spatial detail capture. ISIC 2018 and 2019 datasets were employed to assess the classification performance of the proposed architecture. Individual accuracies for Xception, DenseNet201, and DSENet-ConvMixer in ISIC 2018 were 85.91%, 85.71%, 82.02%, respectively, and in ISIC 2019 were 80.93%, 80.50%, 79.08%, respectively. However, the ensemble architecture with these three architectures achieved 88.21% for ISIC 2018 and 92.34% for ISIC 2019, demonstrating superior performance. Literature reviews on both datasets also confirmed the proposed architecture’s effectiveness compared to recent studies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Energy-efficient modeling in WSN-assisted IoT based on software defined network

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Computing 2025年第6期107卷 1-35页

作者： Hajian, Elham Khayyambashi, Mohammad Reza Movahhedinia, Naser Computer Engineering Department Faculty of Engineering University of Bojnord Bojnord Iran Department of Computer Architecture Faculty of Computer Engineering University of Isfahan Isfahan Iran Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

Consolidating the Internet of Things (IoT) and Software Defined Networks (SDN) has been a great concern among researchers. In IoT, Wireless Sensor Network (WSN) is important communication component. Due to the large volume of data generated in IoT and the limitations of WSN, load distribution in these networks is a serious challenge. The Base Station (BS) in these networks may experience a lot of delay due to high processing. Also, data of applications in these networks must inform to users with the lowest delay. Therefore, in addition to load distribution, reducing response time is also one of the factors that must be considered. Therefore, load distribution among BS’s seems to be crucial Considering several BSs and their relationships with the network nodes and preventing them from overloading through load distribution may solve the problem. This can be implemented by applying the common nodes that belong to several BSs. That is, to reduce the load of the overloaded BS, the common node in the cluster corresponding to BS and the other BSs is selected to send common node load to the other BSs. The common node is selected through the load-balancing node-finder algorithm. Load transfer is done through the forwarding node, which is specified in the proposed routing process. The Colored Petri Nets (CPN) s are applied to implement the proposed method. Here, queue length, residual energy, nodes, BSs, and delay time are simulated in three scenarios. The results show that most of the nodes are applied in the proposed algorithm to implement load balancing between the nodes and BSs. The results show the proposed SDN-based algorithm reduces the residual energy to 18%, the queue length to 9.5%, and the delay to 35%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.

关键词： Wireless sensor networks

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Implementation of Safety-Related Intrusion Detection for an Industrial Robotic Arm

Implementation of Safety-Related Intrusion Detection for an ...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Haydar, Georgio Abou Telawi, Samer Borcsok, Josef Architecture and System Programming University of Kassel Department of Computer Kassel Germany

ISBN: (纸本)9798331529604

Ensuring the safety of human workers collaborating with industrial robots is paramount. This research work presents a novel approach by developing a safety-related intruder detection system for the operational zones of industrial robots, following the IEC 61508 standard and adopting the related redundant architecture. The hardware integrates light curtains and radar sensors in a dual-layer configuration, while the software utilizes a safety system-on-chip to implement real-time diagnostics and fault tolerance. Protecting employees and ensuring a smooth and safe work environment are significant concerns for all companies. As a result, numerous standards and regulations have been established to provide safe collaboration between humans and machines. However, despite these measures, accidents continue to occur, resulting in varying degrees of injuries. The proposed system is depicted through a comprehensive schematic, emphasizing the integration of safety measures within the robotic environment. The detailed prototype demonstrates practical application, show-casing the system's ability to detect intrusions, initiate emergency responses, and transition to fail-safe states during component failures. This research work advances safety protocols in human-robot collaboration within industrial settings, fostering a safe working environment through a well-defined safety framework. © 2025 IEEE.

关键词： Industrial robots

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A Study on Efficiency Improvements of DNN Accelerators via Denormalized Arithmetic Elimination 38

A Study on Efficiency Improvements of DNN Accelerators via D...

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38th International Conference on VLSI Design, VLSID 2025

作者： Kharitonov, Alexander Kundu, Sandip Institute of Computer Architecture & Computer Engineering University of Stuttgart Stuttgart Germany University of Massachusetts Department of Electrical & Computer Engineering Amherst United States

ISBN: (纸本)9798331522445

Deep Neural Networks (DNNs) excel in tasks such as vision and speech processing, driving their widespread use. A key operation in DNNs is convolution, which is often implemented using General Matrix Multiplication (GEMM). Modern CPUs incorporate hardware accelerators for faster GEMM operation. Reduced precision floating-point (RPF) formats such as bfloat16 and TensorFloat32 are popular in machine learning, offering a balance between classification accuracy, dynamic range for training gradients, and hardware efficiency compared to traditional IEEE 754 32-bit or 64-bit floating-point numbers. While RPF is well-studied in the context of DNNs, a major complexity arises from handling denormalized numbers. This paper investigates their impact on DNNs and finds that removing denormalized number support from hardware accelerators does not affect classification accuracy, while allowing significant improvements in hardware efficiency and performance. With proliferation of data centers to accommodate the growing number of DNN applications, power reductions at the GEMM level can significantly improve cluster-wide efficiency, promoting sustainability. Our results indicate that eliminating denormalized arithmetic leads to significant improvements in multiplier circuit efficiency, with reductions of up to 46% in area (LUT count), 28% in longest path delay, and 59% in power consumption, all while maintaining DNN classification accuracy. © 2025 IEEE.

关键词： Deep neural networks

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Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods

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IEEE Access 2025年 13卷 71611-71646页

作者： Emre Ergun, Ahmet Can, Ozgu Kantarcioglu, Murat Ege University Graduate School of Natural and Applied Sciences Bornova35100 Turkey Izmir Katip Celebi University Faculty of Engineering and Architecture Department of Computer Engineering Cigli35620 Turkey Ege University Department of Computer Engineering Bornova35100 Turkey Virginia Tech Department of Computer Science BlacksburgVA24061 United States

The rapid proliferation of Internet of Things (IoT) devices has led to a substantial increase in network packet traffic, raising significant privacy concerns. Although traffic encryption is employed to protect the privacy of IoT devices, attackers can still leverage Machine Learning (ML) and Deep Learning (DL) techniques to classify device types by analyzing packet characteristics, such as size and timing. The main challenges in the state of the art are the lack of effective methods for exposing privacy violations in encrypted IoT traffic, and the absence of robust defense mechanisms to mitigate privacy breaches caused by network traffic analysis. Considering these challenges, this study presents two key contributions: (i) a novel vector-based classification method that enhances device-type identification from encrypted IoT traffic using advanced ML and DL techniques, and (ii) a robust defense mechanism based on Differential Privacy (DP) and advanced padding techniques against traffic analysis attacks. Therefore, the study examines privacy risks associated with sequential IoT device data and evaluates the effectiveness of ML algorithms using two datasets. The results demonstrate that the proposed vector-based classification method significantly improves the attacker’s classification accuracy, even when privacy-preserving techniques, such as padding, are used to obscure device-type classification. For this purpose, the study evaluates eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for IoT traffic classification, achieving an accuracy rate of 99.61% with XGBoost, 96.74% with LSTM, and 96.94% with GRU. Additionally, the Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and GRU classification algorithms are also evaluated and compared with the XGBoost and LSTM classifiers for the proposed attack model. As a defense mechanism, DP is applied using the Fourier Perturbation Algorithm (FPA) to optimize padd

关键词： Decision trees

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Methods for Pitch Analysis in Contemporary Popular Music: Deviations From 12-Tone Equal Temperament in Vitalic’s Work

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AES: Journal of the Audio Engineering Society 2025年第5期73卷 256-281页

作者： Deruty, Emmanuel Arbez-Nicolas, Pascal Meredith, David Sony Computer Science Laboratories Paris France Citizen Records Dijon France Department of Architecture Design and Media Technology Aalborg University Denmark

Aims: Twelve-tone equal temperament (12-TET) is recognized as the modern standard tuning for music. However, contemporary popular music may exhibit significant deviations from this framework. This study investigates such deviations in the music of the electronic musician Vitalic and others. Methods: The study examines relations between signal features and perceived pitches. The artist’s involvement ensures that the analysis focuses on aspects relevant to the music. Results: Deviations from 12-TET can be observed as a result of 1) tuning of quasiharmonic tones outside of 12-TET, 2) audible mistuned partials within an otherwise quasiharmonic context, and 3) subsets of partials perceived as distinct pitches outside 12-TET. Examples from other artists suggest that the use of pitches outside 12-TET is not limited to Vitalic’s music. Conclusions: The deviations from 12-TET are deliberate and pervasive. They are often linked to the acoustic properties of the tones, suggesting 1) a continuum between timbre and pitch and 2) the concept of a "resulting pitch" analogous to the idea of "resulting harmony" in Renaissance polyphonic music. Such conclusions challenge the traditional view of musical pitch. © 2025 Audio Engineering Society. All rights reserved.

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Design of a Robust IEEE Compliant Floating - Point Divide and Square Root Using Iterative Approximation 16

Design of a Robust IEEE Compliant Floating - Point Divide an...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Sager, Carson Stine, James E. Oklahoma State University Vlsi Computer Architecture Research Group Electrical and Computer Engineering Department StillwaterOK74078 United States

ISBN: (纸本)9798331522124

In this paper, we discuss an IEEE 754 compliant normalized floating-point divide and square root unit that utilizes iterative approximation. We provide a robust architecture that allows multiple formats and all IEEE 754 rounding modes while still exhibiting high-performance. Moreover, we also adhere to the IEEE 754 2019 standard and demonstrate methods for rounding results to all five rounding modes using iterative approximation. Performance, Power, and Area estimates are determined from physical synthesis using ARM-based standard cells in a TSMC 28nm process. This paper also presents comparisons versus other implementations and demonstrates the efficient of the approach presented here. © 2025 IEEE.

关键词： Integrated circuit design

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Enhancing Elderly Care with Socially Assistive Robots: A Holistic Framework for Mobility, Interaction, and Well-Being

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IEEE Access 2025年 13卷 82698-82717页

作者： Naseer, Fawad Nasir Khan, Muhammad Tahir, Muhammad Addas, Abdullah Kashif, Hasnain Beaconhouse International College Computer Science and Software Engineering Department Faisalabad38000 Pakistan Government College University Lahore Electrical Engineering Department Lahore54000 Pakistan Sir Syed University of Engineering and Technology Department of Software Engineering Karachi75300 Pakistan Prince Sattam Bin Abdulaziz University College of Engineering Department of Civil Engineering Al-Kharj11942 Saudi Arabia King Abdulaziz University Faculty of Architecture and Planning Landscape Architecture Department Jeddah21589 Saudi Arabia University of South Asia Department of Computer Science Lahore54000 Pakistan

The global elderly population is projected to double by 2050, creating challenges in mobility, social isolation, and cognitive decline. Socially Assistive Robots (SARs) offer a promising solution, yet gaps remain in integrating mobility assistance with cognitive and emotional support. The research developed a comprehensive framework for SARs, emphasizing user-centered design, technological innovation, and cultural adaptability. A mixed-methods approach was employed with 200 participants aged 60+, from diverse settings, including rehab centers, retired groups, old age communities and nursing homes. Results demonstrated a 33.3% improvement in mobility scores and a 40% increase in social engagement. Health metrics revealed a 22% reduction in falls, a 15% rise in physical activity, and a 90% user satisfaction rate. Gender and age differences were evident, with males aged 71+ showing higher mobility improvements (35.6%) and females aged 71+ reporting the highest satisfaction (93%). Long-term data indicated sustained benefits, with participants retaining 80% of mobility gains and social engagement levels 38% above baseline after six months. These findings highlight the importance of culturally sensitive and user-centered SAR designs in promoting elderly well-being. The research offers a robust foundation for integrating SARs into elderly care systems, emphasizing adaptability, sustainability, and ethical considerations for future implementation. © 2013 IEEE.

关键词： Nursing

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Multimodal Input Classification for a Companion Pet Robot using Deep Learning 24

Multimodal Input Classification for a Companion Pet Robot us...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Alhouli, Sarah Yousef Almania, Nora Abdullah Sharma, Jitendra Kumar Sahoo, Deepak Ranjan Department of Computer Science Swansea University Swansea United Kingdom Shuwaikh Kuwait Department of Computer Science Shaqra University Shaqra Saudi Arabia Architecture & Town Planning Department Universite Libre de Bruxelles Bruxelles Belgium

ISBN: (纸本)9798400718014

Social robots have emerged recently as a technology that could provide social support and increase companionship, especially for older adults. Effective human-robot interaction requires social robots to interact empathically and naturally by using Artificial Emotional Intelligence (AEI), which allows them to recognise different multimodal inputs and engage with users emotionally. To this end, we developed AEI companion pet robot classification system that incorporated various multimodal inputs, including hand gestures, emotions from images, emotions from voice annotations, and speech detection. We utilised different deep learning approaches for each mode and adopted lightweight computationally efficient models. For multimodal input detection, we extended the MediaPipe framework with a mathematical approach for hand gestures, integrated an updated MobileNet transfer learning model for facial emotions, employed a Convolutional Neural Network (CNN) model for detecting emotions from voice, and used a lightweight Whisper model as an automatic speech recognition (ASR). Our findings indicated that the modified MobileNet lightweight model achieved an accuracy of 84.60% along with an efficient gesture detection system with an accuracy of 90% integrated with an effective CNN model of 86.89% accuracy. Our results imply that developing a multimodal AEI companion pet robot could promote future research in the development of emotionally intelligent companion robots. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Developing Robot Prototypes to Explore Robot-Facilitated Family Routines 20

Developing Robot Prototypes to Explore Robot-Facilitated Fam...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： He, Xinning Xu, Michael F. Cagiltay, Bengisu Mutlu, Bilge School of Architecture Tsinghua University Beijing China University of Wisconsin-Madison Department of Computer Sciences MadisonWI United States

ISBN: (纸本)9798350378931

In this late-breaking report, we present our design process motivated to build tangible, cost-effective, child- and family-friendly social robot prototypes aimed to (1) support the practical needs of families, as well as (2) serve as a feasible research platform to mitigate technical and logistical challenges faced within conducting inhome HRI studies. We apply a research through design approach, combined with participatory design methods, to produce functional prototypes. In future work, we are preparing to continue improving prototype design by conducting (1) iterative co-design sessions with artists and product designers focusing on the hardware design, (2) iterative co-design sessions with families focusing on the interaction design, and (3) long-term technology probe studies in homes to evaluate the effectiveness of the technological platform. We aim to solicit feedback from the HRI community on our design process and prototype research platform. © 2025 IEEE.

关键词： Social robots

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