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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Minimal Obstructions to C5-Coloring in Hereditary Graph Classes 49

Minimal Obstructions to C5-Coloring in Hereditary Graph Clas...

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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Goedgebeur, Jan Jooken, Jorik Okrasa, Karolina Rzążewski, Pawel Schaudt, Oliver Department of Computer Science KU Leuven Kortrijk Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Warsaw University of Technology Poland University of Warsaw Poland University of Cologne Germany

ISBN: (纸本)9783959773355

For graphs G and H, an H-coloring of G is an edge-preserving mapping from V(G) to V(H). Note that if H is the triangle, then H-colorings are equivalent to 3-colorings. In this paper we are interested in the case that H is the five-vertex cycle C5. A minimal obstruction to C5-coloring is a graph that does not have a C5-coloring, but every proper induced subgraph thereof has a C5-coloring. In this paper we are interested in minimal obstructions to C5-coloring in F-free graphs, i.e., graphs that exclude some fixed graph F as an induced subgraph. Let Pt denote the path on t vertices, and let Sa,b,c denote the graph obtained from paths Pa+1,Pb+1,Pc+1 by identifying one of their endvertices. We show that there is only a finite number of minimal obstructions to C5-coloring among F-free graphs, where F ∈ {P8,S2,2,1,S3,1,1} and explicitly determine all such obstructions. This extends the results of Kamiński and Pstrucha [Discr. Appl. Math. 261, 2019] who proved that there is only a finite number of P7-free minimal obstructions to C5-coloring, and of Dębski et al. [ISAAC 2022 Proc.] who showed that the triangle is the unique S2,1,1-free minimal obstruction to C5-coloring. We complement our results with a construction of an infinite family of minimal obstructions to C5-coloring, which are simultaneously P13-free and S2,2,2-free. We also discuss infinite families of F-free minimal obstructions to H-coloring for other graphs H. © Jan Goedgebeur, Jorik Jooken, Karolina Okrasa, Pawel Rzążewski, and Oliver Schaudt.

关键词： Graph theory

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Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 3390-3403页

作者： Gonzalez, Philippe Alstrom, Tommy Sonne May, Tobias Technical University of Denmark Department of Health Technology Lyngby2800 Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science Lyngby2800 Denmark

The acoustic variability of noisy and reverberant speech mixtures is influenced by multiple factors, such as the spectro-temporal characteristics of the target speaker and the interfering noise, the signal-to-noise ratio (SNR) and the room characteristics. This large variability poses a major challenge for learning-based speech enhancement systems, since a mismatch between the training and testing conditions can substantially reduce the performance of the system. Generalization to unseen conditions is typically assessed by testing the system with a new speech, noise or binaural room impulse response (BRIR) database different from the one used during training. However, the difficulty of the speech enhancement task can change across databases, which can substantially influence the results. The present study introduces a generalization assessment framework that uses a reference model trained on the test condition, such that it can be used as a proxy for the difficulty of the test condition. This allows to disentangle the effect of the change in task difficulty from the effect of dealing with new data, and thus to define a new measure of generalization performance termed the generalization gap. The procedure is repeated in a cross-validation fashion by cycling through multiple speech, noise, and BRIR databases to accurately estimate the generalization gap. The proposed framework is applied to evaluate the generalization potential of a feedforward neural network (FFNN), Conv-TasNet, DCCRN and MANNER. We find that for all models, the performance degrades the most in speech mismatches, while good noise and room generalization can be achieved by training on multiple databases. Moreover, while recent models show higher performance in matched conditions, their performance substantially decreases in mismatched conditions and can become inferior to that of the FFNN-based system. © 2014 IEEE.

关键词： Deep neural networks

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Closest Celestial Body Search Using KD Trees

Closest Celestial Body Search Using KD Trees

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Priya Ponnusamy, P. Shabariram, C.P. Umayal, V.R. Susmeta, A. PSG Institute of Technology and Applied Research Neelambur Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350348217

A Binary Search Tree is expanded into a KD Tree to handle the multi-dimensional key searches. A discriminator makes the KD tree different from the BST. This discriminator will take branching decisions at every level based on the key search. It can handle multi-dimensional coordinate object searching. K-d trees enable O(klogn) lookup times for the k nearest points to some point x. This is extremely useful, especially in cases where an O(n) lookup time is intractable already. In this work, a kd tree has been created based on how a celestial body is positioned in relation to the sun. It aims at finding the minimum in a particular dimension, searching for a celestial body and its nearest neighbor. © 2023 IEEE.

关键词： Nearest neighbor search

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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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Liver Ailment Prediction Using Random Forest Model

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computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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Evaluating the Fulfilment Rate of Charging Demand for Electric Vehicles Using Open-Source Data 9

Evaluating the Fulfilment Rate of Charging Demand for Electr...

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9th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2023

作者： Elattar, Hana von Tüllenburg, Ferdinand Wöllmann, Sebastian Valdes, Javier Institute for Applied Computer Science Deggendorf Institute of Technology Technologie Campus Freyung Freyung Germany

ISBN: (纸本)9789897586491

With the shift towards electric vehicles accelerating;we are working with open-source data to estimate to which degree existing charging infrastructure is fulfilling the demand created by electric vehicles. This paper is explaining how to create such a calculation by extracting data from large public areas in the city of Lindau (Bodensee), Germany as a showcase. With this data we aim to evaluate whether charging stations located in the premises of public and commercial buildings cover the demand of electric vehicles reaching the said buildings. This research is conducted as a first step of methodologies development that aims on the long term to create a tool that supports in the optimal placement of new charging stations. The methodology chosen is inspired by two main concepts: the first is the attractiveness factor concept used for the creation of travel models, while the second is the classification of charging stations based on location to determine their rate of occupancy. They are both used to cluster buildings and charging stations respectively to be able to determine the number of users in the area of study (AOS) compared to the overall number of electric vehicles reaching the destination in a given day. This paper takes the island in the centre of the city of Lindau (Bodensee) as its area under investigation and uses open-source data along with the appropriate assumptions as a base for its calculations. Copyright © 2023 by SCITEPRESS – science and technology Publications, Lda.

关键词： Charging (batteries)

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Innovations in Agri-Tech: A Review of Artificial Intelligence Applications and Challenges in Modern Agriculture 2

Innovations in Agri-Tech: A Review of Artificial Intelligenc...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Rupanshi Bhardwaj, Isha Sharma, Ashish Kumar Sanghi, Akash Agarwal, Gaurav Invertis University Department of Computer Science and Engineering Bareilly India Kothiwal Institute of Technology and Professional Studeis Department of Applied Science and Humanities Moradabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties. © 2024 IEEE.

关键词： Smart agriculture

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Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study 1

Efficacious Routing Approaches in Vehicular Ad hoc Network: ...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Janet Kumari, J Jesy Thangam, S. Raja, A. Saleem Amrita Vishwa Vidhyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Technology and Applied Science-Shinas Department of Computer Science and Engineering Oman

ISBN: (纸本)9798350342796

VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques. © 2023 IEEE.

关键词： ant colony optimization hybrid protocol proactive reactive VANET

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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Mojeed, Hammed A. Szlapczynski, Rafal Department of Applied Computer Science Institute of Ocean Engineering and Ship Technology Gdansk University of Technology Gdansk Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Software development project requires proper planning to mitigate risk and uncertainty. Overtime planning within software project management has been receiving attention recently from search-based software engineering researchers. Multi-objective evolutionary algorithms are used to build automated tools that could effectively help Project Managers (PM) plan overtime on project schedules. Existing models however lack applicability by the PMs due to their disregard for expert knowledge in planning overtime. This study proposes a new interactive problem formulation for software overtime planning and presents a framework for building a machine learning-based interactive multi-objective optimization algorithm for overtime planning in software development projects. The framework is designed to train a priori a machine learning model to mimic the PM’s subjective judgment of overtime plans within the project schedule. The machine learning model is integrated with a memetic multi-objective optimization algorithm via an interactive module. Also, the memetic algorithm incorporates a preference-based w-dominance method for selecting non-dominated solutions. The proposed framework will be developed to assist software project managers to better plan overtime in order to prevent the expected risk of software development overrun. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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