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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Praveena, K.N. Mishra, Awakash Kamalraj, R. Belwal, S. Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

This paper gives a predictive intelligence-enhanced fuzzy version for underwater network optimization. It combines predictive intelligence technology with fuzzy common sense, which can lessen the functionality gap of underwater community optimization. This proposed version is implemented in an underwater cooperative tracking community. With the proposed model, objectives can be tracked with sensors within the network, and their national statistics can also be appropriately anticipated. Similarly, the version has excessive scalability and robustness because of the mixing of predictive intelligence. In the end, simulation experiments suggest that the proposed model correctly improves the precision of tracking challenges and improves the scalability of the network. The Predictive Intelligence superior Fuzzy model (PIE-FUZMO) for underwater network optimization is a singular method developed to cope with the complex optimization issues confronted through underwater networks. The PIE-FUZMO uses a couple of laptops with imaginative and prescient sample recognition techniques and fuzzy good judgment to enhance the underwater community performance while decreasing the running fee. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scalability

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Climatic Condition–Based Comparative Study of Deep Learning Models for Yield Forecasting in Smart Agriculture

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 365-374页

作者： Dhanke, Jyoti A. Khandekar, Sachin Dadu Raj, Roop Chunduru, Madhava Rao Shahade, Aniket K. Mani, P. Balaram, Allam Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Civil Engineering Sinhgad College of Engineering Maharashtra Pune India Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Vaddeswaram Guntur District Andhra Pradesh Guntur India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of ECE K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The novel idea of “smart farming” employs cutting-edge information technology to boost agricultural productivity. Modern developments in networking, robotics, and AI allow farmers to keep a closer eye on every process and use treatments calculated by machines with an accuracy level comparable to that of a person. Engineers, data scientists, and farmers are all persistent in their pursuit of methods that will enable them to maximize the efficiency of human labor in farming. Smart farming becomes an ever-improving learning system as vital information resources continue to improve. Artificial intelligence (AI) techniques from deep learning (DL), including CNN, RNN, and GAN, have been extensively researched and used in several industries recently, including agriculture. Software frameworks are often used by agricultural researchers without a thorough examination of a technique’s concepts and operations. Failure of crops owing to low rainfall, soil infertility, and other similar issues have been plaguing farmers recently. The planned effort aids in determining how to intelligently manage harvests and crops in light of the environmental changes occurring. A person may use it as a guide for smart farming. The main objective of this project is to provide a means for a person to effectively grow crops, allowing them to achieve great output while keeping costs down. The entire cost of cultivation may also be better estimated with its aid. A comprehensive approach in farming would be achieved if this helped a person to pre-plan the actions before growing. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Algorithmic neural networks Deep learning Machine learning Models of artificial intelligence Smart farming

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Enhancing Image Understanding in Automatic Captioning using Spatially-Aware Transformer Architectures

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Procedia computer science 2025年 258卷 2081-2090页

作者： Varun Prakash Saxena Malaram Kumhar Jitendra Bhatia Sudeep Tanwar Sudesh Rani Mayank Gupta Rushi H. Prajapati Department of Computer Engineering Govt. Women Engineering College Ajmer India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Punjab Engineering College (Deemed to be University) Chandigarh India Sahana Systems Limited Ahmedabad

Automatic captioning of images (ACI) is a sophisticated methodology combining image analysis and text generation, with the attention mechanism playing a critical role in identifying key image elements for elaboration. While transformer-based architectures have proven effective in text analysis and translation, their application to image captioning has been challenged by the structural disparity between image semantics typically identified by object detection models and sentence words. To bridge this gap, we introduce the Image Transformer, a novel model featuring a reformed encoding transformer tailored for spatial relationships among image regions and an implicit decoding transformer. This adaptation significantly enhances the standard transformer architecture, making it more suitable for image structures. Our model sets new state-of-the-art performance benchmarks on both online and offline MS COCO dataset testing platforms by utilizing regional features as inputs, representing a substantial advancement in ACI. Experimental results show that our spatially-aware transformer architecture achieved a BLEU-4 score of 38.4, a CIDEr score of 128.4, and a METEOR score of 27 on the MS COCO dataset, outperforming baseline methods significantly. Additionally, the model demonstrated robust performance with a 4.2% accuracy increase on the ImageNet dataset, validating its effectiveness across diverse scenarios. Its robust performance across diverse scenarios demonstrates its potential for broad application and substantial advancements in automatic image captioning.

关键词： Image captioning models Object Detection Real-Time Processing R-CNN Gated Linear Unit (GLU) Natural Language Processing (NLP) Artificial Intelligence (AI) Long Short Term Memory(LSTM)

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mmAlphabet: Air Writing Alphabet Recognition System Based on mmWave FMCW Radar and Convolutional Neural Network 27th

mmAlphabet: Air Writing Alphabet Recognition System Based on...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Chao-Wang Wang, Chien-Yao Wang, Jia-Ching Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Multimedia Technologies Laboratory Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9783031781032

An air writing alphabet recognition system based on the images of temporal spectrogram such as average range-time map, average Doppler-time map and average angle-time map derived from an mmWave FMCW radar is proposed. All 26 English lowercase letters written in midair right above the radar sensor can be recognized. Two valuable radar data sets are introduced in diverse environments such as meeting room, office cubicle and living room etc. The use case is the usage scenarios of laptop computers and mobile phones. In one data set, volunteers write freely in their own handwriting styles including different hand speeds and different stroke orders. In the other data set, volunteers are asked to write according to a prescribed sequence of strokes. Then, the gestures sensed by radar are processed into images of temporal spectrogram to represent the written letter. A convolutional neural network which achieves 98.6% test accuracy is exploited as the classifier to recognize the air written alphabet. In the Leave-One-Subject-Out (LOSO) cross validation, it achieves an average test accuracy of 87.74%. The effectiveness of the proposed alphabet recognition system is extensively verified on the two created data sets for different variants of temporal spectrograms. It can be used to implement natural, intelligent noncontact human machine interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking in Unconstrained Videos 17th

BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghyeon Ju, Chan-Yang Kim, Gun-Woo Lee, D.-H. Department of Applied Artificial Intelligence Hanyang University Ansan Korea Republic of Department of Computer Science and Engineering Gyeongsang National University Jinju Korea Republic of

ISBN: (纸本)9789819609000

Multi-face tracking (MFT) is a subtask of multi-object tracking (MOT) that focuses on detecting and tracking multiple faces across video frames. Modern MOT trackers adopt the Kalman filter (KF), a linear model that estimates current motions based on previous observations. However, these KF-based trackers struggle to predict motions in unconstrained videos with frequent shot changes, occlusions, and appearance variations. To address these limitations, we propose BoT-FaceSORT, a novel MFT framework that integrates shot change detection, shared feature memory, and an adaptive cascade matching strategy for robust tracking. It detects shot changes by comparing the color histograms of adjacent frames and resets KF states to handle discontinuities. Additionally, we introduce MovieShot, a new benchmark of challenging movie clips to evaluate MFT performance in unconstrained scenarios. We also demonstrate the superior performance of our method compared to existing methods on three benchmarks, while an ablation study validates the effectiveness of each component in handling unconstrained videos. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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Rectangle Enclosed Triangular Grid Exploration with Myopic Luminous Robots 25

Rectangle Enclosed Triangular Grid Exploration with Myopic L...

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26th International Conference on Distributed Computing and Networking, ICDCN 2025

作者： Das, Raja Goswami, Pritam Mondal, Brati Sau, Buddhadeb Department of Mathematics Jadavpur University West Bengal Kolkata India Department of Computer Science and Engineering Sister Nivedita University West Bengal Kolkata India

ISBN: (纸本)9798400710629

One of the fundamental problems of distributed systems that has been extensively studied is the exploration of different network topologies. In exploration, each node of the graph network has to be visited by at least one robot within a finite time. Existing literature typically assumes on various networks like lines, rings, tori, arbitrary networks, and rectangular grids. To the best of our knowledge, no existing work has addressed the exploration problem considering triangular grid. In this work the exploration problem is considered on rectangle enclosed triangular grid (RETG) with myopic robots, where the visibility of the robot is limited to a specific distance, denoted as φ, since infinite visibility becomes impractical for a very large network. The robots are luminous and work under FSYNC scheduler. Firstly the cases where the perpetual RETG exploration is not possible are discussed in three impossibility results. Then two algorithms are provided to solve the exploration problem on RETG. The first algorithm requires two robots without common chirality, φ = 1, and three colors of light. The second algorithm requires two robots with common chirality, φ = 2, and two colors of light. Using luminous robots to decrease both visibility and the number of robots is a nice trade-off from the implementation's point of view as increasing visibility is more expensive than using robots with a light having finite colors. © 2025 Copyright held by the owner/author(s).

关键词： Microrobots

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Jogeshwar, Bhaavin Kishore Bohannon, Joshua Sevil, Hakki Erhan Department of Intelligent Systems and Robotics PensacolaFL32514 United States Department of Electrical and Computer Engineering PensacolaFL32514 United States

ISBN: (纸本)9798331504847

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies. © 2025 IEEE.

关键词： Object detection

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Power Quality Analysis and Improvement of Power-to-X Plants Using Digital Twins: A Practical Application in Denmark

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IEEE Transactions on Energy Conversion 2025年

作者： Fathollahi, Arman Andresen, Björn Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark

This paper presents an analysis of a Power-to-X (PtX) system with a specific focus on the integration of Solid Oxide electrolyzers Cell (SOC) for renewable hydrogen production within the practical Power-to-Hydrogen (PtH) platform located in Denmark. The investigation spans the physical PtX system and includes the design of its energy conversion system's Digital Twin (DT), which models the energy conversion process from renewable energy to various types of SOC electrolyzers through different layers of power converters, referred to as the Digital Power Twin (DPT). The proposed DPT framework fed by real-time data from the physical system over short and long-term durations establishes a bidirectional relationship through a measurement layer and a LoT-based cloud. This framework proves essential for optimizing operations that harness real-time insights and ensure continuous improvement in PtX system efficiency. This research examines the energy conversion system of the PtH platform and emphasizes the pivotal role of DPT in enabling advanced predictive capabilities, real-time analysis and simulation. The experimental and simulation results first demonstrate the accuracy of the designed DPT and then address the power quality challenges, particularly harmonic distortions and voltage regulation. The study also presents a compensation approach utilizing multi-level Shunt Active Power Filters (SAPFs) and their adaptive control strategy. This paper discusses the potential effect of large-scale PtH systems on future power grids. The findings contribute to a comprehensive understanding of the complexities regarding the PtH system integration and provide solutions to boost scalability and enhance power quality. © 1986-2012 IEEE.

关键词： Adaptive control systems

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Towards Securing Water Systems Communications from Adversarial Attacks using AI Assurance 5

Towards Securing Water Systems Communications from Adversari...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Yardimci, Mehmet Oguz Batarseh, Feras A. Virginia Tech Department of Computer Science ArlingtonVA United States Virginia Tech Department of Biological Systems Engineering ArlingtonVA United States

ISBN: (纸本)9798331519957

Water systems are increasingly susceptible to cyberattacks due to their reliance on networked communications for monitoring and control. This paper introduces an AI-Assured approach to detect anomalies in water distribution systems communication layers using Long Short-Term Memory models, which are trained, tuned, and validated through four different scenarios targeting disruptions in communication patterns. Using the Secure Water Treatment and anomalies and malicious acts datasets, and by simulating both operational and malicious scenarios, Long Short-Term Memory model is trained on normal data and tested on both regular (idle) and attack situations. The LSTM model employs a sliding time-window technique to capture sequential dependencies in communication data;it identifies anomalies by comparing predicted and actual sequence-significant deviations that signal potential cyber threats. Our scenarios demonstrate an anomaly detection accuracy of 94%, with a false positive rate of 2.5%, indicating the model's capacity to identify both overt and subtle attacks. Further, we evaluate the intentionality of those outliers, whether they are cyber threats or merely a human operator error. The anomalies and malicious acts dataset scenario successfully detects multiple intentional sabotage scenarios, further validating the model's potential in real-world cyber-physical security settings. The findings suggest that integrating AI-Assured, LSTM-based anomaly detection into water systems can enhance security, enabling early threat detection and reducing the overall risk to critical infrastructures communications. © 2025 IEEE.

关键词： Artificial intelligence

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Adaptive Attitude Control of Asteroid-Orbiting Spacecraft Based on Logarithmic Manifold

Adaptive Attitude Control of Asteroid-Orbiting Spacecraft Ba...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Lee, Keum W. Singh, Sahjendra N. Department of Electronic Engineering Catholic Kwandong University Gangwon25601 Korea Republic of Department of Electrical and Computer Engineering University of Nevada Las Vegas Las VegasNV89154-4026 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a new adaptive control system for the attitude trajectory regulation of spacecraft orbiting around rotating asteroids based on a terminal logarithmic manifold. The inertia parameters of the spacecraft, and zonal and sectorial harmonic coefficients associated with the gravitational field and of the asteroid are assumed to be unknown. In the closed-loop systems, the trajectory evolves in two phases. First, the trajectory beginning from arbitrary initial state asymptotically tends to the terminal logarithmic manifold. Subsequently, it converges to the equilibrium state. Unlike, linear sliding surfaces, the trajectory can be made faster around the equilibrium state in the terminal phase. Based on the Lyapunov analysis, the asymptotic convergence of the pitch, roll, and yaw angle trajectory tracking errors is established. Simulation results are obtained for the spacecraft orbiting around asteroid 433 Eros. The responses show trajectory tracking error convergence to zero despite uncertainties in the model parameters and random disturbance inputs. Interestingly, the results show fast tracking error convergence. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Orbits

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