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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network

NOMA Cooperative Spectrum Sharing Average Capacity Improveme...

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sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia & Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

One of the most effective approaches to satisfying the requirements of the next generation of wireless services is non-orthogonal multiple access (NOMA). Spectrum efficiency and average capacity are two aspects of wireless communication that have become increasingly important as the number of mobile services in use has increased. Using both traditional MIMO and Massive MIMO (M-MIMO), this study proposes two new methods for increasing the average capacity of a 5G network's downlink (DL) NOMA power domain (PD) in conjunction with a Cooperative Cognitive Radio Network (CCRN). The initial method involves NOMA users competing for available rivalry channels (R-CH) on the CCRN. The second technique creates a dedicated channel (D-CH) for NOMA users. The MATLAB program is utilized to evaluate the proposed methods in three scenarios with varying distances, power location coefficients, and transmission power. Successive interference cancellation (SIC) and unstable channel conditions are also considered while assessing the analysis of the proposed system under the assumption of Rayleigh fading. Using DL NOMA PD systems, The results reveal that the higher average capacity of best at transmit power DL NOMA with CCRN (dedicated channel) improved U4 average capacity performance by M-MIMO DL NOMA with CCRN (D-CH) improved by when compared with convention DL NOMA. Formulas in closed form were found for average capacity. The resulting formulas are verified using a Monte Carlo simulation.

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Leveraging Temporal Causal Discovery Framework to Explore Event-Related EEG Connectivity 24th

Leveraging Temporal Causal Discovery Framework to Explore Ev...

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24th International Conference on Human-computer Interaction, HCII 2022

作者： Fang, Shao-Xun Chiu, Tsai-Feng Huang, Chih-Sheng Chuang, Chun-Hsiang Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan College of Artificial Intelligence and Green Energy National Yang Ming Chiao Tung University Taipei City Taiwan College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei City Taiwan Artificial Intelligence Research and Development Department ELAN Microelectronics Corporation Hsinchu Taiwan Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9783031196782

Deciphering the cause-and-effect relationships between brain regions not only can provide insights into the mechanism of brain networking but also facilitate the development of the brain-computer interface. Numerous studies have adopted effective connectivity measurements such as transfer entropy or multivariate autoregressive model to investigate the synchronous neuronal coupling across brain regions. Recent successes with representation learning in deep neural networks deserve further investigation on their capability for causal discovery. To this end, this study modified the Temporal Causal Discovery Framework (TCDF) with a sliding-window approach to explore the event-related intra-brain electroencephalogram (EEG) dynamics. The TCDF used a convolution neural network with an attention mechanism to predict causal direction among multi-channel time series data. The resultant array of attention scores was used to gauge the causality magnitude. The TCDF was first validated through a simulation study, followed by real EEG data collected in a virtual reality-based multitasking experiment. Results showed that the time-varying causality magnitudes were positively correlated with the predefined regression coefficients of simulated data, and the TCDF could successfully capture the coupling between brain regions, particularly for adjacent channels. However, some issues were raised in modeling as the number of channels increased or the causal order became larger. The current findings might shed some light on the development of deep learning-based causality analysis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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An Exploratory Case Study for Turkish Sentiment Classification Using Graph Convolutional Neural Networks 6

An Exploratory Case Study for Turkish Sentiment Classificati...

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6th International Conference on computer science and engineering, UBMK 2021

作者： Kiliç, Yasir Büyükeke, Ahmet Department of Computer Engineering Adana Alparslan Turkes Science and Technology University Adana Turkey Department of Management Information Systems Adana Alparslan Turkes Science and Technology University Adana Turkey

ISBN: (纸本)9781665429085

Graph Convolutional Neural Networks (GCNs) are highly popular in recent years. It gives very successful results for various natural language processing (NLP) tasks such as sentiment classification. It has recently been shown to be effective and successful models to solve sentiment classification problem of texts. However, there is no research demonstrating the performance of this model on Turkish texts. In this study, we observe performance of the GCN model on the sentiment classification problem of Turkish texts as first research. Since the structure of Turkish language is agglutinative, different preprocessing approaches are presented and performance results on three real-world Turkish sentiment datasets are shown . It is observed that the TripAdv dataset, which was used in this study, yielded a 0.76 F-measure value. This can be considered a reasonable success for a sentiment classification with three sentiment classes. On the other hand, this study is presented as an exploratory case study in preparation for more detailed and extensive research in the future. © 2021 IEEE

关键词： Convolutional neural networks

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Split-net: Dual transformer encoder with splitting scene text image for script identification

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Pattern Recognition Letters 2025年 196卷 100-108页

作者： Ayush Roy Shivakumara Palaiahnakote Umapada Pal Cheng-Lin Liu Department of Computer Science and Engineering State University of New York Buffalo United States School of Science Engineering and Environment University of Salford Manchester United Kingdom Computer Vision and Pattern Recognition Indian Statistical Institute Kolkata India State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of the Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Script identification is vital for understanding scenes and video images. It is challenging due to high variations in physical appearance, typeface design, complex background, distortion, and significant overlap in the characteristics of different scripts. Unlike existing models, which aim to tackle the script images utilizing the scene text image as a whole, we propose to split the image into upper and lower halves to capture the intricate differences in stroke and style of various scripts. Motivated by the accomplishments of the transformer, a modified script-style-aware Mobile-Vision Transformer (M-ViT) is explored for encoding visual features of the images. To enrich the features of the transformer blocks, a novel Edge Enhanced Style Aware Channel Attention Module (EESA-CAM) has been integrated with M-ViT. Furthermore, the model fuses the features of the dual encoders (extracting features from the upper and the lower half of the images) by a dynamic weighted average procedure utilizing the gradient information of the encoders as the weights. In experiments on three standard datasets, MLe2e, CVSI2015, and SIW-13, the proposed model yielded superior performance compared to state-of-the-art models.

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Towards reliable data-based optimal and predictive control using extended DMD

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IFAC-PapersOnLine 2023年第1期56卷 169-174页

作者： Manuel Schaller Karl Worthmann Friedrich Philipp Sebastian Peitz Feliks Nüske Technische Universität Ilmemau Institute of Mathematics Optimization-based Control group Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees.

关键词： Approximation error data-based dictionary size eDMD estimation error finite data Koopman predicted control projection error optimal control

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Direct Truncation and Routh Approximation Assisted Reduced Order Modelling of Islanded Microgrid

Direct Truncation and Routh Approximation Assisted Reduced O...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Richa Chaudhary V. P. Singh Akhilesh Mathur U. K. Yadav Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Microgrid (MG) networks, with their diverse energy sources and storage systems, represent complex higher-order systems (HOS). Analyzing such networks is challenging due to the uncertainty associated with energy sources like solar and wind power. To address this complexity, reduced-order models (ROMs) are essential, achieved through order reduction techniques. This article presents a method to reduce the eighth-order transfer function of an islanded microgrid to a second-order model. The reduction involves truncation reduction technique (TRT) for the numerator and Routh approximation reduction technique (RART) for the denominator. A comparative analysis is conducted between the original higher-order islanded microgrid system and its ROM. The comparison includes various error indices to determine the effectiveness of the ROM. Graphical representations of impulse, Nichols, step, Nyquist, and Bode responses are provided for both the original system and the ROM to visually compare their performances. This study demonstrates the practical application of mixed reduction techniques to simplify complex microgrid models, making them more manageable for analysis and design purposes.

关键词： Uncertainty Industry applications Transfer functions Microgrids Wind power generation Reduced order systems Complexity theory Time-domain analysis Read only memory Energy storage

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Algorithmic Pluralism A Structural Approach To Equal Opportunity

arXiv

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arXiv 2023年

作者： Jain, Shomik Suriyakumar, Vinith M. Creel, Kathleen A. Wilson, Ashia C. Institute for Data Systems and Society MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Philosophy and Religion Khoury College of Computer Sciences Northeastern University United States

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin’s theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each decision-point or "bottleneck" limits access to opportunities with some degree of severity and legitimacy. We adopt Fishkin’s structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In our proposal of algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic *** Codes 68-06 © 2023, CC BY.

关键词： Decision making

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Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling

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computers in Biology and Medicine 2025年第Pt B期192卷 110250-110250页

作者： Folorunso, Sakinat O. Abdulwarith, Akinshipo Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Artificial Intelligence Systems Research Group Department of Computer Science Olabisi Onabanjo University Ago-Iwoye Nigeria Department of Oral and Maxillofacial Pathology and Biology Faculty of Dentistry University of Lagos Lagos Nigeria Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MIVA Open University Abuja Nigeria Department of Information Technology Durban University of Technology KZ Durban South Africa Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Nigeria

Background: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection capabilities, imbalanced datasets, and computational inefficiencies. Methods: This study proposes a novel diagnostic framework TR-ROS-BAT-ML that integrates transfer learning, random sampling, and a BAT algorithm-based optimization strategy with ensemble machine learning classifiers. A dataset comprising 1224 hematoxylin and eosin (H&E)-stained histological images (at 100x and 400x magnifications) of normal oral epithelium and Oral Squamous Cell Carcinoma (OSCC) was collected from 230 patients using a Leica ICC50 HD microscopy camera. Pre-trained deep learning models (NANSNetLarge, EfficientNetB7, EfficientNetV2L, EfficientNetV2S, EfficientNetV2M) were employed for feature extraction. To address class imbalance, random oversampling techniques were applied. The BAT algorithm, inspired by bat echolocation behavior, was used for feature selection and hyperparameter tuning. Optimized features were classified using ensemble methods, including XGBoost, AdaBoost, Extra Trees (ET), Histogram-Based Gradient Boosting (HBGC), and MultiLayer Perceptron (MLP). Results: The proposed approach achieved high diagnostic performance across multiple model combinations. The best performance was recorded with the optimized ET model using random oversampling, achieving a recall of 0.992, demonstrating its efficacy in detecting oral lesions. In contrast, the combination of EfficientNetV2S + ROS + MLP yielded the lowest accuracy at 50.8 %. These results confirm the robustness of the TR-ROS-BAT-ML framework in handling imbalanced datasets and optimizing classification performance. Conclusions: This study demonstrates the effectiveness of combining nature-inspired optimization, transfer learning, and ensemble machine learning for enhanced detecti

关键词： Adaptive boosting

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IEEE Transactions on Instrumentation and Measurement 2024年 1-1页

作者： Wu, Jin Wang, Chaoqun Li, Chong Jiang, Yi Zhang, Chengxi Huang, Yulong Qi, Yuhua Xue, Bohuan Jiao, Jianhao Fan, Rui Zhang, Wei Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Control Science and Engineering Shandong University Jinan China Department of Automation and Measurement Ocean University of China Qingdao China Department of Electrical Engineering City University of Hong Kong Hong Kong China School of Internet of Things Jiangnan University Wuxi China Department of Automation Harbin Engineering University Harbin China School of Systems Science and Engineering Sun Yat-Sen University China College of Electronics and Information Engineering Tongji University Shanghai China

Similarity transformation problems are important in robotic instrumentation and computer vision based measurements since in many cases the information of visually observed scene scale is unknown and must be restored for accurate 3-dimensional reconstruction. In existing solvers, the scale is often considered as a scalar, i.e., isotropic, which may be invalid for anisotropic-scale setups. This paper exploits some mathematical coincidences that will lead to efficient solution to these problems. Possible further applications also include hand-eye calibration and structure-from-motion. We revisit pose estimation problems within the framework of similarity transformation, the one that considers scale-stretching, rotation and translation simultaneously. Two major problems are taken into account, i.e., the scale-stretching point-cloud registration and perspective-n-points (PnP). It has been found out that these two problems are quite similar. Moreover, we solve the anisotropic-scale registration problem that is important and is a remaining unsolved one in previous literatures. To compute the globally optimal solution of these non-convex problems, algebraic solution is obtained to compute all local minima using computationally efficient methods. The designed algorithm is deployed for robotic-arm pose estimation. We also extend the algorithm for solving the problem of robust magnetometer calibration. Visual pose experiments verify the superiority of the proposed method compared with representatives, including P3P, Lambda-Twist P3P and EPnP, which can be reproduced by repository in https://***/zarathustr/APnP. IEEE

关键词： Calibration

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