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Feature selection using hybridized Genghis Khan Shark with snow ablation optimization technique for multi-disease prognosis

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Intelligence-Based Medicine 2025年 11卷 100249-100249页

作者： Zaitoon, Ruqsar Asiya Begum, Shaik Salma Mohanty, Sachi Nandan Jose, Deepa Department of Information Technology Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning) Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India School of Computer Science & Engineering (SCOPE) VIT-AP University Andhra Pradesh Amaravati India Department of Electronics and Communication KCG College of Technology Karapakkam Chennai India

The exponential growth in medical data and feature dimensionality presents significant challenges in building accurate and efficient diagnostic models. High-dimensional datasets often contain redundant or irrelevant features that degrade classification performance and increase computational burden. Feature selection (FS) is therefore a critical step in medical data analysis to enhance model accuracy and interpretability. While many recent FS techniques rely on optimization algorithms, tuning their parameters and avoiding early convergence remain major challenges. This study introduces a novel hybrid optimization technique—Hybridized Genghis Khan Shark with Snow Ablation Optimization (HyGKS-SAO)—to identify the most informative features for multi-disease classification. The raw medical datasets are first pre-processed using a Tanh-based normalization method. The HyGKS-SAO algorithm then selects optimal features, balancing exploration and exploitation effectively. Finally, a multi-kernel support vector machine (SVM) is employed to classify diseases based on the selected features. The proposed framework is evaluated on six publicly available medical datasets, including breast cancer, diabetes, heart disease, stroke, lung cancer, and chronic kidney disease. Experimental results demonstrate the effectiveness of the proposed method, achieving 98 % accuracy, 97.99 % MCC, 96.31 % PPV, 97.35 % G-mean, 98.03 % Kappa Coefficient, and a low computation time of 50 s, outperforming several state-of-the-art approaches. © 2025 The Authors

关键词： Feature selection Genghis Khan Shark optimizer Machine learning Medical diagnosis Multi-kernel support vector machine. biomedical informatics Optimization Snow ablation optimizer

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Surgical Tool Detection: A Comparative Study of Supervised and Semi-Supervised Learning Approaches 10th

Surgical Tool Detection: A Comparative Study of Supervised ...

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10th International Conference on Health informatics and Medical Systems, HIMS 2024 and 10th International Conference on Biomedical engineering, BIOENG 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Shokouhmand, Shiva Rahman, Md Motiur Bhatt, Smriti Panigrahi, Suranjan Faezipour, Miad School of Engineering Technology Electrical and Computer Engineering Technology Purdue University West LafayetteIN United States Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

ISBN: (纸本)9783031859076

Surgical tool detection and classification is of great importance in enhancing the efficacy, safety of surgical procedures, and providing advanced surgical assistance, especially applicable to robotic/automatic surgery. Current methodologies predominantly rely on supervised techniques necessitating extensive annotated datasets. However, the availability of labeled datasets remains a persistent challenge. In this research, the objective is to detect and classify surgical instruments while evaluating the performance of supervised machine learning and deep learning models against a semi-supervised approach in image classification. Also, we investigate and contrast the significance of preprocessing, transfer learning, and semi-supervised learning techniques. Our findings demonstrate that while thorough preprocessing mechanisms can significantly impact the performance of machine learning models, employing transfer learning with deep learning models yields superior results. Additionally, training data on semi-labeled datasets exhibits comparable, or in some cases better performance than supervised deep learning methods, particularly in scenarios where labeled data is scarce. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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Cross-Modal Haptic Generation for Emergency Rescue in Internet of Robotic Things

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IEEE Internet of Things Journal 2025年

作者： Liu, Hengfa Wei, Xin Gao, Yun Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China University of Nebraska-Lincoln Department of Electrical and Computer Engineering OmahaNE68588 United States

Robots equipped with multi-modal sensing capabilities play an important role in the Internet of Robotic Things (IoRT), especially in emergency rescue. However, existing rescue robots pose challenges for human operators in achieving precise manipulation due to the absence of haptic signals. Additionally, limited and fluctuating bandwidth in emergency rescue renders current cross-modal haptic generation schemes ineffective. To overcome this dilemma, we propose a novel cross-modal haptic generation scheme that enhances scalability across diverse network conditions by leveraging correlations among audio-visual-haptic modalities. Specifically, we first propose an edge-device collaboration-based architecture that dynamically extracts semantics from audio-visual signals, tailored to the current network conditions, for haptic generation. This is achieved by predicting the network state at the edge and performing multi-modal encoding and fusion at the device. Next, we design a scalable cross-modal haptic generation scheme that implements the optimal generation strategy based on received audio-visual semantics of different granularities, ensuring real-time acquisition of coarse-grained or fine-grained haptic feedback. Finally, numerical experimental results conducted on a multi-modal dataset and a simulated emergency environment indicate that the proposed scheme reliably generates haptic signals in emergency rescue. © 2025 IEEE.

关键词： Robots

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Venturing ChatGPT's lens to explore human values in software artifacts: a case study of mobile APIs

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Behaviour and Information Technology 2025年

作者： Mougouei, Davoud Rafi, Saima Fahmideh, Mahdi Mougouei, Elahe Khan, Javed Ali Dam, Hoa Khanh Nurwidyantoro, Arif Chaudron, Michel School of Information Technology Deakin University Burwood Australia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Business University of Southern Queensland Springfield Australia Faculty of Computer Engineering Islamic Azad University Esfahan Iran School of Physics Engineering & Computer Science University of Hertfordshire Hertfordshire United Kingdom School of Computing and IT University of Wollongong Wollongong Australia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Software is designed for humans and must account for their values. However, current research and practice focus on a narrow range of well-explored values, e.g. security, overlooking a more comprehensive perspective. Those exploring a broader array of values rely on manual identification, which is labour-intensive and prone to human bias. Moreover, existing methods offer limited reliability as they fail to explain their findings. In this paper, we propose leveraging the reasoning capabilities of Large Language Models (LLMs) for automated inference about values. This allows for not only detecting values but also explaining how they are expressed in the software. We aim to examine the effectiveness of LLMs, specifically ChatGPT (Chat Generative Pre-Trained Transformer), in automated detection and explanation of values in software artifacts. Using ChatGPT, we investigate how mobile APIs align with human values based on their documentation. Human evaluation of ChatGPT's findings shows a reciprocal shift in understanding values, with both ChatGPT and experts adjusting their assessments through dialogue. While experts recognise ChatGPT's potential for revealing values, emphasis is placed on human involvement to enhance the accuracy of the findings by detecting and eliminating convincing but inaccurate explanations provided by the language model due to potential hallucinations or confabulations. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Groupware

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DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices

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IEEE Transactions on Network Science and engineering 2025年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Zhengzhou University Henan International Joint Laboratory of Intelligent Health Information System Zhengzhou450001 China Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Loss Functions in Deep Learning: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

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On the Performance of Uplink Pinching Antenna Systems (PASS)

arXiv

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arXiv 2025年

作者： Hou, Tianwei Liu, Yuanwei Nallanathan, Arumugam School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, three scenarios, multiple PAs for a single user (MPSU), a single PA for a single user (SPSU), and a single PA for multiple users (SPMU) are considered. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. As the further advance, closed-form expressions of approximated ergodic rate is derived when a single PA is fixed in the SPMU scenario. Our results demonstrate the following key insights: i) The proposed PASS significantly outperforms conventional Multiple-input Single-output networks by exploiting the flexibility of PAs;ii) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario;iii) Optimizing PA positions significantly enhances the ergodic sum rate performance. © 2025, CC BY.

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An intelligent ensemble EfficientNet prediction system for interpretations of cardiac magnetic resonance images in heart failure severity diagnosis

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Intelligence-Based Medicine 2025年 11卷 100218-100218页

作者： Muthulakshmi, Muthunayagam Venkatesan, Kotteswaran Prasanalakshmi, Balaji Syarifah Bahiyah, Rahayu Divya, Vijayakumar Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai 601103 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai 601103 India Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Defense Science and Technology National Defence University Malaysia Kuala Lumpur Malaysia

Ensemble models as part of federated learning leverage the ability of individual models to learn unique patterns from the training dataset to make more efficient predictions than single predicting systems. This study aggregates the output of four best-performing EfficientNet models to arrive at the final heart failure severity prediction through federated learning. The seven variants of EfficientNet models (B0-B7) learn the features from the cardiac magnetic resonance images that are most relevant to heart failure severity. Further, the performance of every model variant has been analysed with three different optimizers i.e. Adam, SGD, and RMSprop. It has been observed that the developed ensemble prediction system provides an improved overall testing accuracy of 0.95. It is also worthy to note that the ensemble prediction has yielded significant improvement in the prediction of individual classes which is evident from sensitivity measure of 0.95, 0.88, 1.00, 0.93, and 0.98 for hyperdynamic, mild, moderate, normal and severe classes respectively. It is obvious from these results that the proposed ensemble EfficientNet prediction system would assist the radiologist in better interpretation of cardiac magnetic resonance images. This in turn would benefit the cardiologist in understanding the HF progress and planning effective therapeutic intervention. © 2025 The Authors

关键词： Cardiac magnetic resonance images Deep features Efficientnet Ensemble prediction Federated learning Heart failure severity

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Secure Beamforming for Continuous Aperture Array (CAPA) Systems

arXiv

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arXiv 2025年

作者： Sun, Mingjun Ouyang, Chongjun Wang, Zhaolin Wu, Shaochuan Liu, Yuanwei School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Electrical and Electronic Engineering UCD College of Engineering and Architecture University College DublinD04 V1W8 Ireland School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial DoFs and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on Zero-Forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a waterfilling approach to design an optimal power allocation scheme that maximizes the WSSR. In high SNR regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Mechanical behavior of waste rock-based geopolymer as a sustainable backfill material: Strength evolution and fracture mechanisms

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Polymer Testing 2025年 147卷

作者： Tian, Yu Wang, Zhile Li, Shiyuan Ji, Chuning Gong, Chuangang Wahab, Abdoul Zhang, Shengkang Ao, Zhongchen School of Environmental Science and Spatial Informatics China University of Mining and Technology Xuzhou221116 China School of Mines China University of Mining and Technology Xuzhou221116 China Department of Civil Engineering The University of Tokyo 4-6-1 Komaba Meguro Tokyo153-8505 Japan School of Geomatics Anhui University of Science and Technology Huainan232001 China Department of Civil Engineering University of Malaya Kuala Lumpur50603 Malaysia

Green backfill materials enhance slope stability and support sustainable mining in open-pit mines. However, current research mainly focuses on traditional loose fills, neglecting eco-friendly cementitious alternatives. In this study, the potential of waste rock-based geopolymers as mine backfilling materials were proved. Specifically, the compressive strength test was conducted to evaluate the effect of materials interaction, aggregate gradation index, alkali activator dosage, water-to-binder ratio and waste rock utilization rate on the strength. And acoustic emission (AE) technology was used to clarify fracture mechanisms of materials. Results demonstrated high-temperature treatment improves the compressive and flexural strength of waste rock-based paste, with calcined waste rock powder effectively enhancing fly ash reactivity. Increasing slag replacement reduces compressive strength linearly, with 5 MPa decline per 10 % increase in slag content. In addition, specimens with on-site gradation aggregate or higher alkali activator dosage exhibit superior strength. The water-binder ratio around 0.4 and 1:5 waste rock utilization ratio enhances reaction dynamic, maximizing compressive strength. Furthermore, the stress-strain process includes four distinct stages: the compaction and elastic stage, stable crack growth stage, unstable crack growth stage and the post-peak stage. Failure patterns predominantly feature single-slope shear fractures, with cracks concentrated in middle and upper areas of specimen. This study contributes to sustainable resource utilization and green mine construction. © 2025 The Authors

关键词： Open pit mining

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