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Transfer Learning for Speaker Verification with Short-Duration Audio 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Fathima, Noor Simha, J. B. Abhi, Shinu REVA Univ REVA Acad Corp Excellence RACE Bangalore Karnataka India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

Speaker verification or identifying the legitimacy of a speaker's identification from their voice is a fundamental problem in speech processing and biometrics. The growing requirement for efficient and secure speaker verification systems, particularly in applications such as voice assistants, authentication, and communications, has increased the necessity for reliable verification even with short-duration audio samples. Short-duration audio provides a considerable barrier because of the limited information available for processing. In this research, it is investigated to make use of the transfer learning strategies to meet this difficulty and improve speaker verification systems' performance. The primary goal of this research is to investigate how transfer learning can be used to improve speaker verification while working with short-duration audio. To get started, a varied dataset of brief audio samples from a variety of speakers, languages, and settings is gathered. To improve the quality and diversity of the dataset, these audio samples are then put through preprocessing procedures such as noise reduction, feature extraction, and data augmentation. To validate speakers based on data samples collected from a few speakers, during this process, speech signals from each speaker are collected for roughly 20-30 s. The speakers are enrolled using a pre-trained 3D convolutional neural network (3D-CNN) at the enrollment step. Speaker verification is performed on the data samples by enrolling the necessary speakers and creating a speaker model for each speaker. The speakers are then validated at the authentication stage.

关键词： Speaker recognition Speaker verification Speaker identification Three-dimensional convolutional neural network (3D-CNN)

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Comparative Efficiency Study of Protective Relays Schemes in Wind Energy Conversion systems 19th

Comparative Efficiency Study of Protective Relays Schemes in...

引用

19th International Conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Sobolewski, Robert Adam Bialystok Tech Univ Fac Elect Engn Wiejska 45D St PL-15351 Bialystok Poland

ISBN: (纸本)9783031618567;9783031618574

Since the performance of wind energy conversion systems is unsatisfactory, one of the main concerns about systems planning and operation should be better reliability of the components and their operation. Among others, the attention should affect protection systems, and relays incorporated to. Relays have two alternative ways in which they can be unreliable, i.e., may fail to operate when they are expected to, or may operate when it is undesired. This leads to a two-pronged definition of protective relay, i.e., it must be dependable and secure. One of a few options of relays reliability enhancement can be use of logical redundancy, i.e. series, parallel, 2-out-of-3. The advisability of using a given type of redundancy (instead of single relay), can be evaluated relying on efficiency of relays operation. Both the structural reliability and the operation reliability must be taken into consideration. The efficiency of different types of redundancy can be measured based on economical criterion and incorporate the costs of incorrect operation, i.e., failure to trip caused by undetected fault, undesired trip, and repair of faulted relay(s). The efficiency of different protective relays scheme can be investigated relying on semi-Markov models. In the paper, four models are investigated in detail. As an example, the comparative efficiency study of these schemes are presented.

关键词： Protective relay Efficiency Wind energy Reliability Semi-Markov model

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From Data to Decision: Exploring Machine Learning's Impact on Shaping Smart Cities 4th

From Data to Decision: Exploring Machine Learning's Impact o...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Adraoui, Meriem Diop, El Bachir Azmi, Rida Chenal, Jerome Abdem, Seyid Abdellahi Ebnou Mohammed VI Polytech Univ UM6P Ctr Urban Syst CUS Benguerir Morocco Ecole Polytech Fed Lausanne EPFL Urban & Reg Planning Community CEAT Lausanne Switzerland

ISBN: (纸本)9783031686528;9783031686535

In today's era of rapid urbanization, smart cities are emerging as a beacon of efficiency and sustainability. At the heart of this evolution is the integration of Machine Learning (ML), which can transform vast streams of data into informed decisions. This article examines through a literature review the transformative potential of machine learning in urban environments, highlighting advances and challenges in the key smart city domains: Infrastructure, Environment, Mobility and Transport. The review highlights both the promising breakthroughs achieved and the challenges ahead in this interdisciplinary field. Our analysis aims to provide readers with a comprehensive understanding of how machine learning not only improves data processing capabilities, but also leads to actionable and impactful decisions in the urban context. By tracing the path from raw data to informed urban solutions, this paper highlights the central role of machine learning in shaping the smart cities of the future.

关键词： Smart Cities Machine Learning Infrastructure Environment Mobility and Transport Sustainability Urban Data

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Applying DevOps Practices for Machine Learning: Case Study Predicting Academic Performance 12th

Applying DevOps Practices for Machine Learning: Case Study P...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Valdiviezo-Diaz, Priscila Guaman, Daniel Univ Tecn Particular Loja Dept Comp Sci San Cayetano Alto Loja 1101608 Ecuador

ISBN: (纸本)9783031602269;9783031602276

This paper presents the application of DevOps practices in the development and implementation of a machine learning model to predict the academic performance of students enrolled in a Higher Education Institution in Ecuador. The study encompasses three key phases: 1) Dataset preparation to build and validate the model, 2) Experimental evaluation of machine learning algorithms for academic performance prediction, and 3) Implementation of DevOps practices to design and build a software application using Microservices architecture, that supports the prediction model and visualize the results to take decisions. The dataset employed for the model and software application incorporates socio-demographic data and academic records of undergraduate students within the Information Technology career. Logistic Regression, Random Forest, and Neural Network algorithms are applied directly to the student dataset. Compared with other algorithms, experimental results present favorable outcomes with Random Forest, particularly in precision, sensitivity, and f-score metrics.

关键词： academic performance software application microservices machine learning prediction

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Electric Vehicle Issues, Challenges and Technological Solutions: A Systematic Review 7th

Electric Vehicle Issues, Challenges and Technological Soluti...

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7th International Conference on Logistics Operations Management (GOL)

作者： Jraif, Amine El Idrissi, Adiba El Bouzekri Messaoud, Elhassania Chouaib Doukkali Univ Natl Sch Appl Sci Sci Engineer Lab Energy El Jadida Morocco Hassania Sch Publ Works Casablanca Morocco

ISBN: (纸本)9783031686337;9783031686344

The electrification of transport is necessary in order to reduce greenhouse gases, as well as to guarantee sustainability, as conventional vehicles, use of limited natural resources such as oil. Despite the considerable progress in terms of autonomy and energy efficiency of these vehicles, it is still necessary to take advantage of technological progress and that of battery efficiency, to ensure a wider deployment of electric vehicles (EV). In this paper we present a systematic review of the impact of the different technological aspects on electric vehicle efficiency, we investigate the energy efficiency of EVs, as well as the issues and constraints considered in electric vehicle routing problems (EVRP). The strengths of the electrification of transport and the use of EVs in distribution logistics are clearly highlighted, and gaps in EVRP models are identified.

关键词： Energy consumption Electric vehicle EVRP EV Battery

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AI-Driven EoL Aircraft Treatment: A Research Perspective

AI-Driven EoL Aircraft Treatment: A Research Perspective

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Intelligent systems Conference

作者： Amirnia, Ashkan Keivanpour, Samira 2500 Chem Polytech Dept Math & Ind Engn Montreal PQ H3T 1J4 Canada

ISBN: (纸本)9783031664274;9783031664281

End-of-life (EoL) aircraft treatment is a crucial step in the circular manufacturing of the aircraft industry, delivering considerable economic and environmental benefits. Despite these advantages, complex and sensitive operations are a serious challenge in this field due to the high complexity of an aircraft structure and its constituent materials and composites. Poor operations can significantly reduce output quality and increase operational costs. Consequently, the value of the recovered materials may not exceed the operational costs. Artificial intelligence (AI) is a valuable tool for enhancing the effectiveness and sustainability of EoL aircraft management by automating and optimizing the identification, separation, processing, and transformation of materials. AI can also help to reduce waste and emissions, increase material recovery and reuse, and create new markets and jobs. This paper comprehensively reviews the AI applications in EoL aircraft treatment. It discusses the current state-of-the-art AI models across three domains: recycling, maintenance, and dismantling/disassembly. This article then carefully highlights the existing gaps based on the analyses. It also describes the possible directions of future research.

关键词： Artificial intelligence EoL aircraft Sustainable aircraft management Circular manufacturing

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Control Techniques for Vision-Based Autonomous Vehicles for Agricultural Applications: A Meta-analytic Review 1

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Thakur, Abhishek Kumar, Ankit Mishra, Sudhansu Kumar BIT Mesra Ranchi 835215 Jharkhand India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

The agriculture industry continues to expand, showing significant potential in meeting the escalating food demand. Renowned for its plentiful resources and substantial agricultural yield, India is progressively leveraging advancements in technology for mass production of superior quality goods. Notably, the integration of robotics has revolutionized farming practices, encompassing activities such as fruit picking, sheep herding, pruning, weeding, spraying, and other farming tasks. The move toward automation not only mitigates the reliance on human labor but also enhances the overall productivity of the agriculture sector. This paper conducts a detailed examination of the progress in the realm of vision-based autonomous vehicles (AV) tailored for agricultural applications. Previous research has employed a range of machine learning algorithms, from linear and logistic regression, decision trees, support vector machines (SVM), naive Bayes, K-nearest neighbor (KNN) algorithms, to K-means. Nowadays, hybrid algorithms are increasingly employed in AV designs, addressing the constraints of single algorithms while elevating their efficiency. Furthermore, ensemble techniques are being used for efficient handling and accurate prediction over vast data quantities. These techniques amalgamate the predictive prowess of numerous base estimators, boosting robustness, and are predominantly employed today utilizing Python and R codes.

关键词： Autonomous vehicles Artificial intelligence Vision-based control Machine learning algorithms Control techniques

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Study on Grade Discrimination Method of Traffic Accident Risk Factors Considering Road Type

Study on Grade Discrimination Method of Traffic Accident Ris...

引用

Intelligent systems Conference (IntelliSys)

作者： Wang Jie Zhan Zhenfei Qian Liuzhu Chongqing Jiaotong Univ Chongqing Peoples R China

ISBN: (纸本)9783031477140;9783031477157

In order to further explore the occurrence mechanism of road traffic accidents;this paper takes into account the differences in road types. Based on the open source traffic accident data set of the British Ministry of Transport, the traffic accident risk factors and their importance of the four road types with the highest traffic accident rates are studied. Firstly, based on data visualization, the distribution of data is preliminarily explored from the spatial and temporal dimensions. Secondly, aiming at the shortcomings of the integrated algorithm with more hyperparameters, which lacks accurate and efficient parameter adjustment methods, a Bayesian optimization based random forest algorithm (BO-RF) implemented by the Optuna framework is proposed to construct the risk factor importance level screening model of four road types. In addition, a control experiment with the random forest algorithm based on random search optimization (RS-RF) was carried out. The results show that the BO-RF algorithm model based on the Optuna hyperparameter optimization framework has shorter tuning time and higher accuracy. Finally, based on the visualization results of road traffic accident risk factor level obtained by the model constructed by BO-RF algorithm, a scheme suitable for reducing accidents under this road condition is proposed, which provides a reference for preventing road traffic accidents and optimizing relevant safety management regulations.

关键词： Data mining Causation analysis Random forest Bayesian optimization Optuna framework Road risk differences

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Toward a Human-in-the-Loop Approach to Create Training Datasets for RDF Lexicalisation 1

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Intelligent systems Conference (IntelliSys)

作者： Barbato, Jessica Amianto Cremaschi, Marco Rula, Anisa Maurino, Andrea Univ Milano Bicocca Milan Italy Univ Brescia Brescia Italy

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

Datasets that include alignments between natural language and Knowledge Graphs are fundamental to a wide variety of Natural Language Processing and Generation tasks. Current state-of-the-art aligned datasets, though, are significantly impacted by reduced size and scarcity of covered domains, and their quality is difficult to evaluate. To compensate for these issues, we introduce SEALIon, a tool for extracting RDF triples from natural language textual corpora based on a humanin-the-loop approach. We present our first results of SEALIon's approach, paving the way for further researches in the field of human-in-the-loop triple extraction.

关键词： Natural language processing Natural language generation Human-in-the-loop Relation extraction

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Attending to Entity Class Attributes for Named Entity Recognition with Few-Shot Learning

Attending to Entity Class Attributes for Named Entity Recogn...

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Intelligent systems Conference (IntelliSys)

作者： Patel, Raj Nath Dutta, Sourav Assem, Haytham Huawei Res Dublin Ireland Amazon Alexa AI Cambridge England

ISBN: (纸本)9783031477140;9783031477157

Named Entity Recognition (NER) serves as the foundation for several natural language applications like question answering, chat-bots and intent classification. Identification of entity boundaries and its categorization into entity types poses a significant challenge in domain-dependent and low-resource settings, with limited training data availability. To this end, we propose AtEnA, a novel NER framework utilizing entity class attributes from external knowledge source for few-shot learning. We use a two-stage fine-tuning process, wherein a language model is initially trained to "attend" to the different entity class attributes along with the textual context, and is then fine-tuned for the downstream application data with few annotated training examples. Experiments on benchmark NER datasets depict AtEnA to perform around 10 F1 score points better than the existing NER methodologies, specifically for fewshot limited training scenarios.

关键词： Named entity recognition Knowledge graph Few-shot learning Transformers Attention mask

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