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Spatial variability of soil properties using geostatistical approach: a case study in alluvial soils of Samastipur district of Bihar, India

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Arabian Journal of Geosciences 2025年第5期18卷 1-13页

作者： Nidhi Katiyar, Mohit Laik, Ranjan Dept. of Statistics & Computer Applications CBS&H Dr. Rajendra Prasad Central Agricultural University Pusa Samastipur India Dept. of Soil Science PGCA Dr. Rajendra Prasad Central Agricultural University Pusa Samastipur India

In a developing country like India, the research on precision agriculture is concentrated on precision nutrient management. However, in intensive agriculture, crop production is hugely affected by micronutrient deficiency. In the intensively cultivated regions of India, the spatial variability of key micronutrients has not been adequately captured, and there is a dearth of such information. Since the availability of micronutrients is directly connected to plant uptake, it is. crucial to develop an understanding of the variations of soil micronutrients across the fields of any region to establish management zones. The present study is an attempt to evaluate the spatial variability of major soil fertility parameters, namely, pH, electrical conductivity (EC), organic carbon (OC), soil available zinc (Zn), and soil available copper (Cu) in Samastipur district of Bihar, India. The spatial variability of micronutrient availability is expected to be high in the district due to small farms and varied management. The coefficient of variation value was highest for EC (77.7%) and lowest for soil pH (6.3%). The use of geostatistical analyses has been made to determine the spatial variation structure of all the parameters, followed by the generation of surface maps through kriging. The nugget/sill ratio obtained from the experimental semivariogram indicated a moderate degree of spatial dependence (25–75%) for all the studied soil fertility parameters. The maps developed by kriging were observed to be superior to assuming the mean of the observed value for any unsampled location. These maps may help farmers to adopt need-based variable rates of fertilizer application, thus optimizing resource utilization.

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SPOC: A Scale for Potential Operation Consequences of UI Interactions

SPOC: A Scale for Potential Operation Consequences of UI Int...

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Artificial Intelligence x Multimedia (AIxMM), Conference on

作者： Florian Eggenkemper Teresa Rehers Jana Swerew Robert Mertens Centre for Cognitive Science & Institute of Psychology Technical University of Darmstadt Germany Dept. of Computer Science and Engineering HSW University of Applied Sciences Germany BHH University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350390971

ISBN: (纸本)9798350390988

Many approaches rely on a semantic understanding of screen contents to perform further actions on this information. This is relevant for test automation, robotic process automation, or context based assistive tasks (digital agents). Most of these approaches just analyze basic information about user interface elements but fall short in predicting the consequences of single user interface actions. Possible consequences of UI actions include (permanent) data loss, effects on the reputation of people (e.g. posting on social media), or other irreversible consequences. This makes it necessary to identify and analyze the potential consequences of operation on user interfaces to prevent automatic systems from making possibly fatal mistakes with a high impact on the human user. This paper presents a Scale for Potential Operation Consequences (SPOC) and demonstrates an automatic rating system based on Large Language Models (LLMs), that can be performed locally or in cloud implementations. The LLM-based ratings were tested against a human control group based on a questionnaire (n=35), and proved to perform as well as their human counterparts, making SPOC a robust and automation-friendly rating system for user interface element actions.

关键词： Intelligent automation Social networking (online) Large language models Semantics User interfaces

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*

Automating Neural Model Selection in Spiking Neural Networks...

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Learning and Technology Conference (L&T)

作者： Sanaullah Hirotada Honda Kaushik Roy Axel Schneider Joachim Waßmuth Thorsten Jungeblut Dept. of Mathematics and Engineering University of Applied Sciences and Arts Bielefeld Germany Dept. of Information Networking Toyo University Tokyo Japan Dept. of Computer Science A&T State University North Carolina USA

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks.

关键词： Hands Accuracy Computational modeling Neurons Spiking neural networks computer architecture Manuals Automated machine learning Reliability engineering Image classification

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Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing

arXiv

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arXiv 2025年

作者： Ahmed, Bestoun S. Baader, Ludwig Otto Bayram, Firas Jagstedt, Siri Magnusson, Peter Dept. Mathematics and Computer Science Karlstad University Karlstad Sweden Dept. Mathematics Informatics and Statistics Ludwig Maximilian University Munich Munich Germany CTF Service Research Center Karlstad University Karlstad Sweden

This paper presents a comprehensive framework for testing and evaluating quality characteristics of Large Language Model (LLM) systems enhanced with Retrieval-Augmented Generation (RAG) in tourism applications. Through systematic empirical evaluation of three different LLM variants across multiple parameter configurations, we demonstrate the effectiveness of our testing methodology in assessing both functional correctness and extra-functional properties. Our framework implements 17 distinct metrics that encompass syntactic analysis, semantic evaluation, and behavioral evaluation through LLM judges. The study reveals significant information about how different architectural choices and parameter configurations affect system performance, particularly highlighting the impact of temperature and top-p parameters on response quality. The tests were carried out on a tourism recommendation system for the Värmland region, utilizing standard and RAG-enhanced configurations. The results indicate that the newer LLM versions show modest improvements in performance metrics, though the differences are more pronounced in response length and complexity rather than in semantic quality. The research contributes practical insights for implementing robust testing practices in LLM-RAG systems, providing valuable guidance to organizations deploying these architectures in production environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Advanced Cryptocurrency Price Prediction: Incorporating Time-Reversed ARIMA and BVIC 19

Advanced Cryptocurrency Price Prediction: Incorporating Time...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Luc, Minh Tuan Senkerik, Roman Dang, Tran Khanh Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava-Poruba Czech Republic Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh city Viet Nam Institute for Experiential Technology Van Lang University Ho Chi Minh city Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh city Viet Nam

ISBN: (纸本)9798331507817

The highly volatile nature of cryptocurrencies, particularly Bitcoin, exhibits extreme price volatility, making accurate price prediction a challenging task for traditional forecasting models. This paper proposes an enhanced ARIMA model, called ARIMA_T, which incorporates time-reversed data and optimizes model selection using the Backward Validated Information Criterion (BVIC). The ARIMA_T model aims to improve forecasting accuracy by capturing both linear and non-linear patterns in the data that standard ARIMA models often overlook. We apply this model to predict Bitcoin prices over short and medium-term horizons, specifically 1-day, 7-days, and 30-days ahead predictions. The dataset spans from April 2013 to February 2024. Experimental results demonstrate that ARIMA_T consistently outperforms the traditional ARIMA model across all forecast horizons, as evidenced by lower Root Mean Square Error (RMSE) values. The ARIMA_T model provides more accurate and robust predictions, particularly for short-term forecasting, making it a valuable tool for navigating the volatile cryptocurrency market. This study also discusses the limitations of both models in handling longer-term predictions and suggests future work in integrating ARIMA_T with machine learning techniques for further improvements. © 2025 IEEE.

关键词： Prediction models

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Exploring Local Interpretable Model-Agnostic Explanations for Speech Emotion Recognition with Distribution-Shift

Exploring Local Interpretable Model-Agnostic Explanations fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maja J. Hjuler Line H. Clemmensen Sneha Das CNRS Grenoble INP LIG University Grenoble Alpes Grenoble France School of Computer Science Queensland University of Technology Brisbane QLD Australia Dept. of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We introduce EmoLIME 1 , a version of local interpretable model-agnostic explanations (LIME) for black-box Speech Emotion Recognition (SER) models. To the best of our knowledge, this is the first attempt to apply LIME in SER. EmoLIME generates high-level interpretable explanations and identifies which specific frequency ranges are most influential in determining emotional states. The approach aids in interpreting complex, high-dimensional embeddings such as those generated by end-to-end speech models. We evaluate EmoLIME, qualitatively, quantitatively, and statistically, across three emotional speech datasets, using classifiers trained on both hand-crafted acoustic features and Wav2Vec 2.0 embeddings. We find that EmoLIME exhibits stronger robustness across different models than across datasets with distribution shifts, highlighting its potential for more consistent explanations in SER tasks within a dataset.

关键词： Emotion recognition Explainable AI Closed box Speech recognition Signal processing Predictive models Acoustics Robustness Speech processing

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Efficient lightweight image compression approach for Multimedia Internet of Things 5

Efficient lightweight image compression approach for Multime...

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

作者： Idrees, Sara Kadhum Couturier, Raphael Azar, Joseph Idrees, Ali Kadhum Gechter, Franck BelfortF-90000 France University of Babylon College of Information Technology Dept. of Information Networks Babylon Iraq Université Marie et Louis Pasteur CNRS Institut FEMTO-ST BelfortF-90000 France University of Applied Science and Arts Smart Edge Lab Faculty of Computer Science Dortmund Germany SIMBIOT Team Vandoeuvre-lès-NancyF-54506 France

ISBN: (纸本)9798331519957

The development of the Internet of Things (IoT) and communication technologies has led to the emergence of the multimedia IoT. The multimedia IoT contributes to a variety of multimedia applications, that can capture and transmit large amounts of images or video sequences over an IoT network. Due to restricted resources, multimedia IoT devices must transmit less multimedia data while retaining accuracy. This can save energy and memory use and increase multimedia IoT network performance. This paper proposes an efficient lightweight image compression approach (ELiCA) for Multimedia Internet of Things. The proposed ELiCA starts dividing the captured image into equal-sized blocks and the Discrete Cosine Transform (DCT) is applied to each of them to produce the DCT coefficients. Then, the DCT coefficients are quantized. The output of the quantization process is reordered using zigzag process. Finally, the output files of the zigzag are compressed using the error-bounded lossy compression technique known as SZ developed specifically for High-Performance Computing (HPC) systems. The compressed file will be transmitted by the MIoT device. The proposed ELiCA is evaluated using real sensed images and based on real IoT devices to show the efficiency of the proposed approach. The ELiCA presents better results compared with JPEG in terms of compression ratio, PSNR, and SSIM. © 2025 IEEE.

关键词： Image compression

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RadChain Connect: Integration Between Blockchain and FreeRADIUS for Secure Authentication in Wi-Fi Networks/Web Environment

RadChain Connect: Integration Between Blockchain and FreeRAD...

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International Conference on Information Networking

作者： Eduardo Freitas Hoffmann Caciano Machado Carla Merkle Westphall dept. of Computer Science and Statistics Federal University of Santa Catarina Santa Catarina Brazil Data Processing Center Federal University of Rio Grande do Sul Porto Alegre Brazil

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

User authentication in web environments and Wi-Fi networks is crucial due to the increase in fraud and invasions. Traditional methods, such as WPA2-PSK, have vulnerabilities, making the use of more secure solutions necessary. RadChain Connect is a proposal that combines blockchain with FreeRADIUS and self-sovereign identity, aiming to provide an efficient authentication system. Initially, it focuses on web authentication in universities, enhancing security at login. In the future, there are plans to expand the solution to Wi-Fi networks, leveraging the versatility of FreeRADIUS and the security of the EAP protocol.

关键词： Smart contracts Authentication Robustness Blockchains Fraud Security Proposals Internet of Things Wireless fidelity Business

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Jiawei Tian Seungho Lee Kyungtae Kang Dept. of Computer Science and Engineering Major in Bio Artificial Intelligence Hanyang University Ansan-si South Korea Dept. of Applied Artificial Intelligence Hanyang University Ansan-si South Korea Dept. of Artificial Intelligence Hanyang University Ansan-si South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes.

关键词： Radiography Ultrasonic imaging Reviews Precision medicine Surgery Medical services Transforms Real-time systems Medical diagnostic imaging Diseases

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Exploring Local Interpretable Model-Agnostic Explanations for Speech Emotion Recognition with Distribution-Shift

arXiv

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arXiv 2025年

作者： Hjuler, Maja J. Clemmensen, Line H. Das, Sneha Dept. of Applied Mathematics and Computer Science Technical University of Denmark Lyngby2800 Denmark University Grenoble Alpes CNRS Grenoble INP LIG Grenoble38000 France School of Computer Science Queensland University of Technology BrisbaneQLD4000 Australia

We introduce EmoLIME1, a version of local interpretable model-agnostic explanations (LIME) for black-box Speech Emotion Recognition (SER) models. To the best of our knowledge, this is the first attempt to apply LIME in SER. EmoLIME generates high-level interpretable explanations and identifies which specific frequency ranges are most influential in determining emotional states. The approach aids in interpreting complex, high-dimensional embeddings such as those generated by end-to-end speech models. We evaluate EmoLIME, qualitatively, quantitatively, and statistically, across three emotional speech datasets, using classifiers trained on both hand-crafted acoustic features and Wav2Vec 2.0 embeddings. We find that EmoLIME exhibits stronger robustness across different models than across datasets with distribution shifts, highlighting its potential for more consistent explanations in SER tasks within a dataset. © 2025, CC BY.

关键词： Speech recognition

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