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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Chadi Altrjman Dibyahash Bordoloi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Mersin Turkey Waterloo University Canada Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun RS CSE Graphic Era University Dehradun

Machine learning has the ability to dramatically improve sustainable systems by anticipating needs, maximizing resource use, raising output, and lowering waste. An overview of earlier studies on the incorporation of machine learning into sustainable systems is presented together with a case study of how machine learning was used to lower energy use in a residential structure. The results show that machine learning may be used to increase energy efficiency and save a lot of money. Wearable technology has added a whole new dimension to the already vast field of personal electronics. The mobile phone gave devices their true individuality. Because so many services are designed around mobile phones, the market has opened up for a brand-new personalized experience utilizing wearable technologies. Fabric sensors may now be combined with wearable microcontrollers like the flora and lily pad to monitor stretch, pressure, bend, and even the direction that the body is being braced. The connections between them are based on conductive threads that follow the curve of the fabric. We'll examine how different teams used their in-depth understanding of wearable technologies to accomplish their goals in this study.

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The ways in which Artificial Intelligence improves several facets of Cyber Security-A survey

The ways in which Artificial Intelligence improves several f...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

With the increase in computer use for the last 10 years, it increases the size of cyber-attacks as well. Since most computers are connected to the network, they may suffer cyber-attacks. Because of these cyber threats, Cyber Security is becoming a really important area in the information technology area. Cyber Security is an area which is mainly responsible for dealing with cyber-attacks by hackers. The improvements and advancements in the information technology area allow cyber criminals to do really complicated and dangerous cyber-attacks. Cyber-attack methods that are being used today are much more effective than the ones in the past. Because of that, there is always a war between cyber security specialists and cyber attackers or hackers. Cyber security analysts, on the other hand, try to come up with new solutions to the methods developed by cyber criminals or hackers. The competition between them continuously advances and improves cyber security technologies every day. The improvements can be either in cyber security or cybercrime side. These days, traditional cyber security solutions are enough to fight against cyber-attacks, but can these solutions be further developed? This is the question everyone in the cyber security area is trying to answer. Of course, yes. Thanks to ai, which is one of the most popular technologies in the information technology area, it is used in many areas ranging from e-commerce, advertising, human resource & recruiting, video games, transportation, surveillance systems etc. ai has been used in cyber security over the last years. Although it also has negative effects, we are going to mostly talk about its positive sides in this article. We are going to go deep into how and why ai is used in cyber security, how ai improves and enhances the current traditional cyber security methods. Then, we will also talk about which ai techniques play an important role in the cyber security area.

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A novel deep learning model for early diabetes risk prediction using attention-enhanced deep belief networks with highly imbalanced data

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International Journal of Information Technology (Singapore) 2025年第4期17卷 1933-1955页

作者： Olabanjo, Olusola Wusu, Ashiribo Olabanjo, Olufemi Asokere, Mauton Afisi, Oseni Akinnuwesi, Boluwaji Center for Equitable AI and Machine Learning Systems (CEAMLS) Morgan State University Baltimore 21251 MD United States Department of Mathematics Morgan State University Baltimore MD United States Department of Computer Science Lagos State University Lagos Nigeria Department of Mathematics Lagos State University Lagos Nigeria Department of Cybersecurity University of Derby Derby United Kingdom Department of Philosophy Lagos State University Lagos Nigeria Department of Computer Science Faculty of Science and Engineering University of Eswatini Kwaluseni Swaziland

Diabetes mellitus is a prevalent chronic illness with severe complications that demand timely diagnosis. This study introduces an attention-enhanced Deep Belief Network (DBN) for early diabetes risk prediction, designed to address challenges associated with highly imbalanced datasets. Using a dataset from Sylhet Diabetes Hospital, which includes symptom and demographic information from patients, we applied an ensemble feature selection approach to identify critical predictors. To address the class imbalance, Generative Adversarial Networks (GANs) were used to generate synthetic data, ensuring the model’s robustness in identifying underrepresented cases. Additionally, a hybrid loss function combining cross-entropy and focal loss was implemented to improve classification, especially for hard-to-detect instances. Our results show that the attention-based DBN model, augmented with synthetic data from GANs and optimized with a hybrid loss function, achieves an AUC of 1.00, F1-score of 0.97, precision of 0.98, and recall of 0.95, outperforming several baseline models. This research offers a novel and effective approach for early diabetes detection, demonstrating potential for use as a clinical tool in preventive healthcare settings. © The Author(s) 2025.

关键词： Attention mechanism Deep belief networks (DBN) Diabetes risk Generative adversarial networks (GANs) Hybrid loss function Imbalanced data

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Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance

Fairness-Sensitive Policy-Gradient Reinforcement Learning fo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jie Zhu Mengsha Hu Amy Zhang Ruoming Jin Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science George Washington University Department of Computer Science Kent State University

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robots assist humans in various activities, from daily living to collaborative manufacturing. Because they have biased learning sources (e.g., data, demonstrations, human feedback), robots inevitably have discriminatory performance regarding individual differences (e.g., skin color, mobility, appearance); discriminatory performance will undermine robots’ service quality, causes request ignorance and response delay, and even cause emotional offenses. Therefore, mitigating biases is critically important for delivering fair robotic services. In this paper, we design a bias-mitigation method – Fairness-Sensitive Policy Gradient Reinforcement Learning (FSPGRL), to help robots self-identify and correct biased behaviors. FSP-GRL identifies bias by examining the abnormal updates along particular gradients and updates the policy network to provide fair decisions. To validate FSPGRL’s effectiveness, we designed a human-centered service scenario: a robot serving people in a restaurant. With a user study involving 24 humans and 1,000 service demonstrations, FSPGRL has proven effective in maintaining fairness during robot services.

关键词： Image color analysis Design methodology Collaboration Reinforcement learning Skin Manufacturing Delays Robots

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Holistic Construction Automation With Modular Robots: From High-Level Task Specification to Execution

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IEEE Transactions on Automation Science and engineering 2025年 22卷 16716-16727页

作者： Jonathan Külz Michael Terzer Marco Magri Andrea Giusti Matthias Althoff Department of Computer Engineering Technical University of Munich Munich Germany Munich Center for Machine Learning (MCML) Munich Germany Robotics and Intelligent Systems Engineering Unit Fraunhofer Italia Research Bolzano Italy

In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices. This work proposes a holistic framework for construction task specification, optimization of robot morphology, and mission execution using a mobile modular reconfigurable robot. Users can specify and monitor the desired robot behavior through a graphical interface. In contrast to existing, monolithic solutions, we automatically identify a new task-tailored robot for every task by integrating Building Information Modeling (BIM). Our framework leverages modular robot components that enable the fast adaption of robot hardware to the specific demands of the construction task. Other than previous works on modular robot optimization, we consider multiple competing objectives, which allow us to explicitly model the challenges of real-world transfer, such as calibration errors. We demonstrate our framework in simulation by optimizing robots for drilling and spray painting. Finally, experimental validation demonstrates that our approach robustly enables the autonomous execution of robotic drilling. Note to Practitioners—Construction sites are unpredictable and constantly changing, making automation difficult. To tackle this, we propose a modular robotic system that can adapt to specific tasks. In this work, we address the challenge of automating robotic drilling and introduce a general framework that also supports other construction tasks. We use Building Information Modelling (BIM) data to automatically determine the best robot configuration by optimizing factors such as setup time and robustness to positioning errors. Through an intuitive interface, workers can specify tasks, after which they assemble the robot following the provided instructions. The result of this work is a robotic system that can be deployed on diverse construction sites while requiring only r

关键词： Robots Optimization Service robots Drilling Automation Morphology Robustness Robot control Navigation Calibration

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RSMA-Enabled Interference Management for Industrial Internet of Things Networks With Finite Blocklength Coding and Hardware Impairments

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1319-1340页

作者： Mohamed, Nahed Belhadj Hassan, Md. Zoheb Kaddoum, Georges Université Laval Department of Electrical and Computer Engineering QuébecQCG1V 0A6 Canada MontrealQCH3C 1K3 Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut03797751 Lebanon

The increasing proliferation of industrial internet of things (IIoT) devices requires the development of efficient radio resource allocation techniques to optimize spectrum utilization. In densely populated IIoT networks, the interference that results from simultaneously scheduling multiple IIoT devices over the same radio resource blocks (RRBs) severely degrades a network's achievable capacity. This paper investigates an interference management problem for IIoT networks that considers both finite blocklength (FBL)-coded transmission and signal distortions induced by hardware impairments (HWIs) arising from practical, low-complexity radio-frequency front ends. We use the rate-splitting multiple access (RSMA) scheme to effectively schedule multiple IIoT devices in a cluster over the same RRB(s). To enhance the system's achievable capacity, a joint clustering and transmit power allocation (PA) problem is formulated. To tackle the optimization problem's inherent computational intractability due to its non-convex structure, a two-step distributed clustering and power management (DCPM) framework is proposed. First, the DCPM framework obtains a set of clustered devices for each access point by employing a greedy clustering algorithm while maximizing the clustered devices' signal-to-interference-plus-noise ratio. Then, the DCPM framework employs a multi-agent deep reinforcement learning (DRL) framework to optimize transmit PA among the clustered devices. The proposed DRL algorithm learns a suitable transmit PA policy that does not require precise information about instantaneous signal distortions. Our simulation results demonstrate that our proposed DCPM framework adapts seamlessly to varying channel conditions and outperforms several benchmark schemes with and without HWI-induced signal distortions. © 2023 CCBY.

关键词： Signal to noise ratio

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Prognostic Phenotype Clustering of Uterine Sarcoma Patients

Prognostic Phenotype Clustering of Uterine Sarcoma Patients

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Zhang, Tuao Yuan, Suzhen Zhou, Xue Wang, Wenwen Zhu, Xin Graduate School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Wuhan China Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9798350350548

Uterine sarcoma is a rare but highly aggressive malignancy with poor prognosis. In this study, prognostic phenotypes corresponding to the end point of mortality were explored by unsupervised machine learning (ML) on training (n = 1985) and validation (n = 498) datasets. The data were extracted from patients with uterine sarcoma in the Surveillance, Epidemiology, and End Results (SEER) database. A decision-making process of phenotype identification was performed and the findings were compared with existing research. Three phenotypes associated with stratified levels of mortality risks (high, intermediate and low) were identified. Kaplan-Meier survival analysis revealed significant differences among these phenotypes (pairwise comparison p © 2024 IEEE.

关键词： Contrastive Learning

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Lumbar spine localisation method based on feature fusion

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CAai Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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Reducing the Computational Cost of Transformers for Person Re-identification

Reducing the Computational Cost of Transformers for Person R...

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IEEE International Conference on robotics and Biomimetics

作者： Wen Wang Zheyuan Lin Shanshan Ji Te Li Jason Gu Minhong Wan Chunlong Zhang Zhejiang Lab and Zhejiang Engineering Research Center for Intelligent Robotics Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Canada

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset.

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Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator

arXiv

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arXiv 2025年

作者： Li, Chenhao Krause, Andreas Hutter, Marco ETH AI Center ETH Zurich Switzerland Department of Computer Science ETH Zurich Switzerland Department of Mechanical Engineering ETH Zurich Switzerland

Reinforcement Learning (RL) has demonstrated impressive capabilities in robotic control but remains challenging due to high sample complexity, safety concerns, and the sim-to-real gap. While offline RL eliminates the need for risky real-world exploration by learning from pre-collected data, it suffers from distribution shift, limiting policy generalization. Model-Based RL (MBRL) addresses this by leveraging predictive models for synthetic rollouts, yet existing approaches often lack robust uncertainty estimation, leading to compounding errors in offline settings. We introduce Offline Robotic World Model (RWM-O), a model-based approach that explicitly estimates epistemic uncertainty to improve policy learning without reliance on a physics simulator. By integrating these uncertainty estimates into policy optimization, our approach penalizes unreliable transitions, reducing overfitting to model errors and enhancing stability. Experimental results show that RWM-O improves generalization and safety, enabling policy learning purely from offline data and advancing scalable, data-efficient RL for robotics. (Image Present) Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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