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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Arifuddin, Lukman Nurba, Hardy Purnama Sarief, Ivany Setiawan, Asep Trisna Munastha, Ketut Abimanyu Lestari, Nina Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Universitas Bandar Lampung Faculty of Computer Science Lampung Indonesia

ISBN: (纸本)9798331527686

The demand for more efficient and reliable elevator monitoring systems has increased due to security and safety issues in high-mobility buildings. This paper proposes a microservice architecture for an elevator monitoring system to enhance scalability, flexibility, and fault tolerance by decomposing the system into more minor, loosely coupled services. This paper will discuss the proposed system's design, implementation, and evaluation. The system, equipped with ESP32Cam and ultrasonic sensors, excels in real-time monitoring. Data is processed and analyzed using Python and OpenCV, allowing the system to not only count the number of people in the elevator but also detect any anomalies, ensuring the safety of the occupants. LED notification alerts, managed by NodeMCU8266, are triggered based on ultrasonic sensor data. MongoDB handles user management and analytical data, while the dashboard offers a user-friendly interface for real-time monitoring and system management provide essential information for elevator operators. The architecture's use of MQTT ensures efficient communication between sensors and services, creating a robust, scalable, and efficient solution. © 2024 IEEE.

关键词： Ultrasonic sensors

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Pedestrian Crossing Intention Prediction Using Transformer-Based Feature Fusion 10

Pedestrian Crossing Intention Prediction Using Transformer-B...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Salehi, Yasaman Ezoji, Mehdi Mohammadpourmir, Farzam Babol Noshirvani University of Technology Faculty of Electrical and Computer Engineering Babol Iran Electrical Power and Computer Science and Research Branch Islamic Azad University Faculty of Mechanics Tehran Iran

ISBN: (纸本)9798331532543

Pedestrian intention prediction can be used in Advanced Driver Assistance Systems to prevent pedestrian-vehicle collision in case of driver distractions. The use of these tools will reduce pedestrian fatalities in traffic accidents. In addition, predicting pedestrians' intention and avoiding collisions are parts of the functions of autonomous vehicles. Improving these models will lead to safer vehicles and higher levels of automation. In most studies, inspired from human behavior, sequences of spatial features, e.g. pedestrian's location, are considered as inputs. For more accurate prediction, temporal features are also extracted from these sequences of spatial features. We have realized that it is important to represent key objects to ensure model's perception of the scene. In this work, a spatio-temporal approach has been studied that predicts pedestrians' intention using spatial features of cropped images. We use the same strategy as our reference model for temporal features and feature fusion. We have modified the masking model of the reference model to reduce inference time, while we improve the performance. With inference time of 58 ms, we achieved accuracy, AUC and F1-Score of 0.90, 0.87, and 0.75 on JAAD dataset, respectively. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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The Lawn Mowing Problem: From Algebra to Algorithms 31

The Lawn Mowing Problem: From Algebra to Algorithms

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31st Annual European Symposium on Algorithms, ESA 2023

作者： Fekete, Sándor P. Krupke, Dominik Perk, Michael Rieck, Christian Scheffer, Christian Department of Computer Science TU Braunschweig Germany Faculty of Electrical Engineering and Computer Science Bochum University of Applied Sciences Bochum Germany

ISBN: (纸本)9783959772952

For a given polygonal region P, the Lawn Mowing Problem (LMP) asks for a shortest tour T that gets within Euclidean distance 1/2 of every point in P;this is equivalent to computing a shortest tour for a unit-diameter cutter C that covers all of P. As a generalization of the Traveling Salesman Problem, the LMP is NP-hard;unlike the discrete TSP, however, the LMP has defied efforts to achieve exact solutions, due to its combination of combinatorial complexity with continuous geometry. We provide a number of new contributions that provide insights into the involved difficulties, as well as positive results that enable both theoretical and practical progress. (1) We show that the LMP is algebraically hard: it is not solvable by radicals over the field of rationals, even for the simple case in which P is a 2 × 2 square. This implies that it is impossible to compute exact optimal solutions under models of computation that rely on elementary arithmetic operations and the extraction of kth roots, and explains the perceived practical difficulty. (2) We exploit this algebraic analysis for the natural class of polygons with axis-parallel edges and integer vertices (i.e., polyominoes), highlighting the relevance of turn-cost minimization for Lawn Mowing tours, and leading to a general construction method for feasible tours. (3) We show that this construction method achieves theoretical worst-case guarantees that improve previous approximation factors for polyominoes. (4) We demonstrate the practical usefulness beyond polyominoes by performing an extensive practical study on a spectrum of more general benchmark polygons: We obtain solutions that are better than the previous best values by Fekete et al., for instance sizes up to 20 times larger. © Sándor P. Fekete, Dominik Krupke, Michael Perk, Christian Rieck, and Christian Scheffer.

关键词： Approximation algorithms

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Navigating the Nexus: Exploring the Fusion of AI and Nanotechnology for Cutting-Edge Advances 1

Navigating the Nexus: Exploring the Fusion of AI and Nanotec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Izanker, Sakshi V. Dhole, Aman Kumar, Praveen Faculty of Engineering and Technology Department of Computer and Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The convergence of artificial intelligence (AI) and nanotechnology has initiated a transformative journey, introducing innovative possibilities across various fields. This review article explores the dynamic interaction between these two disciplines, emphasizing their collaborative potential, notable accomplishments, and prevailing challenges. The integration of AI algorithms with nanotechnology has expedited material discovery, nanoscale characterization, personalized medical applications, and nano- electronics. AI's predictive capabilities have revolutionized the discovery of novel nanomaterials with customized properties, the enhancement of drug delivery systems, and the design of intricate nano-electronic components. However, this synergy is accompanied by challenges such as data quality, multidisciplinary cooperation, ethical considerations, and computational complexity. The fusion of AI and nanotechnology is poised to transform industries, unlock quantum advancements, and influence sustainable materials. As AI's capacities evolve, scholars are prepared to address challenges and harness the transformative potential of AI- nanotechnology integration. © 2023 IEEE.

关键词： Medical applications

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Estimating the distance to an object from static color stereo images using convolutional neural networks 27

Estimating the distance to an object from static color stere...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Kulawik, Joanna Faculty of Mechanical Engineering and Computer Science Department of Computer Science Czestochowa University of Technology Dabrowskiego 69 Czestochowa42-201 Poland

In issues such as autonomy of movement, the vital information is the vehicle's distance from the obstacle. The research presented is devoted to the phenomenon of disparity in color stereo images. Their goal was to estimate the distance to the obstacle. A convolutional neural network regression model was used as a research method. The data set is a proprietary set of color stereo static images. Testing the learned CNN model gave excellent results. The analysis of the obtained results showed that the developed solution could be used in engineering solutions such as supporting vehicle control. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： artificial intelligence deep learning estimating distance stereo-vision

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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USING BLACK-SUN EFFECT TO IMPROVE UNSCENTED KALMAN FILTERING OF TRISAT-R SATELLITE ATTITUDE PREDICITON

USING BLACK-SUN EFFECT TO IMPROVE UNSCENTED KALMAN FILTERING...

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2024 Small Satellites Systems and Services Symposium, 4S 2024

作者： Kosanič, Nejc Kramberger, Iztok University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia

ISBN: (纸本)9781510688940

Due to the nature of monetary and spatial constrictions, larger systems on small satellites are getting replaced by smaller but more inaccurate sensors. To improve the satellite orientation estimate, multiple different optical sensors may be used. In the case of TRISAT-R, the satellite is equipped with photodiodes and small cameras (CMOS sensors) in parallel, to establish the position of the Sun and calculate the satellite attitude. Due to the intensity of solar radiation, the "black-Sun" effect can be clearly distinguished: a black circle in a photo of a CMOS sensor. The black circle is smaller than the whole Sun on the photo, which provides a more accurate estimate of Suns position. In this paper we will discuss the use of an Unscented Kalman Filtering, which uses magnetometer, gyro meter and optical sensor data, and then calculates the satellites attitude and error estimation. The accuracy of attitude estimation between photodiodes and CMOS sensor images will be compared and conclusion that CMOS sensors can be used to calibrate photodiodes in orbit will be presented. © 2025 The Authors.

关键词： CMOS integrated circuits

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Monitoring energy losses of a residential building through thermographic assessments 17

Monitoring energy losses of a residential building through t...

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17th International Conference on Development and Application Systems, DAS 2024

作者： Eugen, Iavorschi Laurențiu, Milici Dan Faculty of Electrical Engineering and Computer Science Department of Electrical Engineering "Stefan cel Mare" University of Suceava Suceava Romania

ISBN: (纸本)9798350349290

One of the significant opportunities for reducing energy waste and carbon emissions is the renovation, modernization, and rehabilitation of existing buildings. Replacing the windows of existing residential buildings with new energy-efficient ones is an easy way to upgrade an existing home into a nearly zero-energy building, NZEB (nearly zero-energy building). There is a wide variety of methods to discover the thermal weak points of a building, and the continuous innovation of construction materials provides solutions to maximize energy savings and minimize heat transfer to the exterior. This document presents a methodology for visualizing and recording the temperature distribution on the investigated surface of the building envelope to identify and locate vulnerable points where heat losses to the exterior are substantial. The objective was to highlight the need for upgrading the insulation system and replacing the existing windows of the analyzed building based on a multi-objective approach to maximize the building's energy performance and increase thermal comfort indoors. Using a Fluke Ti300+ Thermal Camera, energy losses were visualized through thermographic assessments, which could provide an adequate picture for accurately assessing the building's condition before renovation, to evaluate the need for partial renovation. For the case study, a residential building with one floor and mansard roof located in a region with hot summers and cold winters in Romania was chosen. The results showed that there are several options for significantly reducing the building's energy consumption. Thus, multiple energy efficiency solutions were proposed for the building to reduce heat losses. ©2024 IEEE.

关键词： Heat transfer

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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