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Innovative Research in Applied Science, engineering and Technology (IRASET),Conference on

作者： Oussama El Azzouzy Tarik Chanyour Said Jai Andaloussi Department of Mathematics and Computer Science Laboratory of Data Engineering and Intelligent Systems(LIDSI) Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Modern logistics is facing increasing challenges due to the growing complexity of supply chains, high consumer expectations, and economic and environmental pressures. While predictive analytics, powered by artificial intelligence (AI) and massive data, has transformed the ability to forecast trends and risks, its application remains limited by the lack of effective mechanisms to convert these predictions into actionable decisions in dynamic and constrained environments. This paper proposes an innovative hybrid approach that combines advanced predictive models, notably the Temporal Fusion Transformer (TFT), with prescriptive algorithms, such as linear programming and deep reinforcement learning (DRL). The TFT demonstrated superior performance in terms of predictive accuracy (significant reduction in MAE, RMSE, and MAPE), while the prescriptive algorithms optimized complex logistics decisions in real time, even in uncertain environments. This integrated methodology was validated through use cases simulating critical logistics scenarios, such as delivery route optimization. Experimental results show a substantial improvement in operational efficiency, with reductions in costs, lead times and resource utilization. The prescriptive approach made it possible to automate complex decisions, while taking into account multiple constraints, such as costs, lead times and sustainability objectives. By integrating real-time data collected via IoT devices and logistics management systems, this architecture offers a robust, adaptable and scalable solution. It thus meets the requirements of modern logistics by enabling proactive, optimized and resilient decision-making. This research makes a significant contribution by bridging the gap between predictive and prescriptive analysis, while laying the foundations for new applications in the field of intelligent logistics.

关键词： Costs Supply chains Predictive models Prediction algorithms Linear programming Transformers Real-time systems Thin film transistors Resource management Optimization

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Comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism 8th

Comprehensive Review of Capsule Networks with a Case Stud...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Depth Perception in Virtual Reality: The Impact of Spatial Interaction and Color-Based Cues

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SSRN 2025年

作者： Sun, Yusi Guan, Haoyan Chan, Leith K.Y. Department of Data and Systems Engineering the University of Hong Kong Hong Kong The Chinese University of Hong Kong Hong Kong

Depth perception is crucial for accurate spatial interaction in both physical and virtual environments. However, in Virtual Reality (VR), users often experience perceptual distortions, such as foreshortening, which impair tasks requiring precise depth judgments, including 3D pointing and object manipulation. Traditional depth cues, such as occlusion and relative size, often fail in VR, highlighting the need for alternative visual cues. Among these, contrast and hue have shown potential in influencing depth perception, yet their independent effects remain unclear. This paper presents two experimental phases to systematically investigate depth perception distortions in VR. Phase 1 quantifies axis-specific perceptual biases using 3D sketching tasks, revealing that reconstruction errors in the depth dimension are twice as large as those along other axes. Phase 2 examines the impact of color attributes on depth perception using CIELAB-based perceptual matching, showing that hue differences within a specific range improve depth discrimination accuracy, whereas excessive saturation creates cue competition, reducing accuracy. Our findings provide empirical evidence of axis-specific depth distortions in VR and suggest design guidelines that prioritize contrast over hue variations to enhance spatial perception. These insights contribute to VR interface improvements, refining depth perception mechanisms for applications requiring precise spatial awareness. © 2025, The Authors. All rights reserved.

关键词： Virtual environments

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Network-based pick-up and drop-off location optimization for shared autonomous vehicles

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Urban Informatics 2025年第1期4卷 1-12页

作者： Wang, Sunyu Ding, Fangyi Wang, Yineng Gar on Yeh, Anthony Huang, Guan Department of Urban Planning and Design The University of Hong Kong Hong Kong China Department of Data and Systems Engineering The University of Hong Kong Hong Kong China College of Civil and Transportation Engineering Shenzhen University Shenzhen China

Shared autonomous vehicles (SAVs) are reshaping the mobility system, with ride-hailing services being regarded as a pioneering application market. Despite its benefits, SAVs inevitably exacerbate the competition for limited curb space, which is already a scarce resource. Designated pick-up and drop-off sites, if reasonably planned, present a promising solution to this challenge. In this paper, we propose a novel network-based data-driven approach to optimize PUDO locations for SAVs. Network kernel density estimation (NKDE) is used to measure the PUDO demand along the road network based on historical ride-hailing data. The results are fed into a maximal coverage location problem (MCLP) model to optimize the spatial distribution of PUDO sites by maximizing the demand they can cover. The spatial optimization model is used both prescriptively and descriptively, assessing transport policies and enhancing the operations of SAVs. Results show that our model outperforms the baseline models. We also reveal that a suitable PUDO site density is about 20/10 km2, with denser sites in bustling urban centre and sparser sites in suburbs. The framework is highly generalizable and reproducible and can be extended to future studies.

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AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adap...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Gangul, Ranita Kolekar, Sagar Chintakunta, Advaitha Naidu Mungara, Sudeep Bangalore India Institute of Technology Department of Data Science Stevens HobokenNJ07030 United States Delaware State University Computer Science & Engineering Bangalore India Department of Civil Engineering Pune India University of North Carolina at Charlotte Department of Computer Science CharlotteNC28223 United States Masters in Information Systems Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9798331508685

With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity. © 2025 IEEE.

关键词： Risk assessment

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On the Interaction Between a Flow Instability and Inertial Particles in Supersonic Jet Plume-Surface Interaction

On the Interaction Between a Flow Instability and Inertial P...

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

作者： Rubio, J. Sebastian Qi, Yinghe Diaz-Lopez, Miguel X. Gorman, Matthew T. Ni, Rui Rodrigues, Neil S. Department of Mechanical Engineering Johns Hopkins University 3400 N. Charles St BaltimoreMD21218 United States Sandia National Laboratories AlbuquerqueNM87185 United States ETH Zürich Switzerland Advanced Measurement Systems and Data Systems Branch NASA Langley Research Center HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Extraterrestrial landings often require spacecraft to deploy supersonic jets directed at dusty planetary surfaces to ensure controlled descents. This interaction, termed plumesurface interaction (PSI), critically influences crater formation and the dynamics of ejected particles, which can obscure vision, damage onboard sensors, and impact nearby infrastructure with significant particle velocities. The physics of PSI remain poorly understood due to the complexities in simulating the involved multiphysics and experimentally replicating reduced atmospheric pressure conditions. This article discusses a collaborative effort to investigate PSI in reduced atmospheric pressure environments using a Mach 5.3 jet impinging on a granular bed. The results highlight observations of an azimuthal pattern in the ejecta distribution, characterized by alternating ejecta streaks of varying particle concentrations. This phenomenon suggests the presence of a low Reynolds number instability, a notable discovery given the supersonic nature of the jet. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reynolds number

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM

Hybrid Approach for Electricity Price Forecasting using Alex...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Bosubabu Sambana Kotamsetty Geethika Devi Bandi Rajeswara Reddy Galeti Mohammad Hussain Gownivalla Siddartha Department of Computer Science and Engineering (Data Science) School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively.

关键词： Temperature distribution Accuracy Recurrent neural networks Rain Electricity Predictive models Prediction algorithms data models Forecasting Long short term memory

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Informative Path Planning for Nano-Surgical Robot Adaptive Drug Delivery

Informative Path Planning for Nano-Surgical Robot Adaptive D...

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Annual Conference on Information Sciences and systems (CISS)

作者： Sujatha Alla Nagesh Bheesetty Hyoshin John Park Engineering Management & Systems Engineering Old Dominion University Norfolk United States Department of Data Science Old Dominion University Norfolk United States

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Nano-surgical robots are transforming precision medicine by enabling minimally invasive procedures at a microscopic scale. Informative path planning for these robots is critical for ensuring safety, efficiency, and high precision in navigating sensitive anatomical and uncertain environments. This study adapts the RRT*-IT (Rapidly Exploring Random Tree-Information Theoretic) algorithm, traditionally utilized in robotics, to address the unique challenges faced by nano-surgical robots. The proposed methodology offers significant advancements in sub-millimeter precision navigation, reducing collision risks and enhancing procedural efficiency. The adaptive path planning system optimizes a trade-off between collision avoidance and information gain while minimizing surgical trajectory. The algorithm dynamically adjusts paths by incorporating probabilistic models for obstacle detection and uncertainty reduction, accommodating changes in tissue density, obstacle dimensions, and operational constraints. The performance of proposed approach are validated through various scenarios involving dynamic uncertain environments. The findings hold promise for broader applications in automated drug delivery setting a foundation for intelligent nano-robotic systems in healthcare.

关键词： Adaptive systems Uncertainty Navigation Heuristic algorithms Medical services Robot sensing systems Drug delivery Trajectory Nanobioscience Collision avoidance

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A Comparative Analysis of Generalised Echo and Interference Cancelling and Extended Multichannel Wiener Filtering for Combined Noise Reduction and Acoustic Echo Cancellation

A Comparative Analysis of Generalised Echo and Interference ...

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

作者： Arnout Roebben Toon van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Two algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) are analysed, namely the generalised echo and interference canceller (GEIC) and the extended multichannel Wiener filter (MWF ext ). Previously, these algorithms have been examined for linear echo paths, and assuming access to voice activity detectors (VADs) that separately detect desired speech and echo activity. However, algorithms implementing VADs may introduce detection errors. Therefore, in this paper, the previous analyses are extended by 1) modelling general nonlinear echo paths by means of the generalised Bussgang decomposition, and 2) modelling VAD error effects in each specific algorithm, thereby also allowing to model specific VAD assumptions. It is found and verified with simulations that, generally, the MWF ext achieves a higher NR performance, while the GEIC achieves a more robust AEC performance.

关键词： Voice activity detection Analytical models Wiener filters Interference cancellation Echo cancellers Signal processing algorithms Detectors Signal processing Acoustics

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