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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Species Identification of Birds Via Acoustic Processing Signals Using Recurrent Network Analysis (RNN) 1

引用

6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Srujana, C. Sriya, B. Divya, S. Shaik, Subhani Kakulapati, V. Sreenidhi Institute of Science and Technology Yamnampet Ghatkesar Telangana Hyderabad501301 India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Bird watching is a popular pastime, but without the proper identification guides, it may be difficult to tell different species apart. There are more than 9000 recognized bird species. Some bird species are notoriously difficult to predict when they are first identified. Additionally, seeing beliefs when it comes to communicating with birds as a service to birdwatchers, developed a system that uses RNNS to classify bird species. RNNs are an effective machine learning algorithm suite that has shown great promise in the fields of image and audio processing. In this study, investigate potential methods for bird recognition and create a fully automated method for doing so. Automatically identifying bird calls without human intervention is an arduous task that has necessitated much research into the taxonomy and other areas of ornithology. In this study, ID is assessed from two distinct perspectives. The first thing to do was make a complete database of recorded bird calls. Following that, other techniques were used to the sound samples prior to further processing. These included pre-emphasis, framing, quiet eradication, and rebuilding. Each reconstructed audio specimen was given its spectrogram. A neural network was then constructed, trained, and applied to classify the bird species. The consequences of the proposed methodology exhibit that it has been 80% accurate in predicting the identification of bird species. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Birds

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Resolving the Quadratic Assignment Problem with the Elephant Herding Optimization 1

引用

International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Mohsine, Said Mzili, Ilyass Riffi, Mohammed Essaid LAROSERI Laboratory Department of Computer Science Faculty of Sciences Chouaïb Doukkali University El Jadida Morocco Laboratory of Research in Management and Development Department of Management Faculty of Economics Hassan 1st University Settat Morocco

ISBN: (数字)9783031542886

ISBN: (纸本)9783031542879

Combinatorial problems are often challenging and require creative thinking to find optimal solutions. These problems involve analysing and manipulating discrete objects or structures, such as permutations, combinations, graphs, or networks. In this paper, we present an application of the Elephant herding optimization (EHO) for solving the Quadratic assignment problem (QAP), which is one of the most popular combinatorial problems. The elephant herding optimization is a metaheuristic algorithm inspired by the herding behaviour of elephants in the wild. It simulates the collaboration and communication among elephants to find optimal solutions to continued optimization problems. To apply the Elephant herding optimization (EHO) to the QAP, we propose some adaptations to the EHO algorithm. The experiments are performed on a set of 25 benchmark QAPLIB instances. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Optimal systems

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Design of Multiband GaN HEMT Power Amplifier for Radar Applications 1

引用

5th International Conference on Computer and Communication Technologies, IC3T 2023

作者： Sarathkrishna, S. Mridula, S. Mohanan, P. Abhilash, S. Advanced Center for Atmospheric Radar Research Cochin University Kerala Cochin India School of Engineering Cochin University Kerala Cochin India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Gallium nitride (GaN) high-electron mobility transistors (HEMTs) exhibit remarkable capabilities catering to high power and wideband requirements. This study introduces the design of a wideband power amplifier based on GaN HEMT, operating with the frequency range of 0.5–2 GHz. From the experiment, it is found that with a gate voltage of −2.7 V and a drain voltage of 28 V, the GaN HEMT operates optimally. Notably, it achieves a power-added efficiency of nearly 50% with an RF output power of 14W. The many stages of power amplifier design are thoroughly explored in this paper, with an emphasis on the various electrical parameters. Upon the completion of layout preparations, electromagnetic (EM) co-simulation is also executed. Such comprehensive efforts result in the development of wideband power amplifiers that are particularly well-suited for applications in radar systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gallium nitride

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SGSN: Tool Support for GSN Development 6th

SGSN: Tool Support for GSN Development

引用

6th conference on Computing systems and Applications, CSA 2024

作者： Bouchekir, Radouane Ouazar, Fatiha Boukala, Mohand Cherif Ioualalen, Malika MOVEP Computer Science Faculty Université des Sciences et de la Technologie Houari Boumediene BP 32 El-Alia Algiers Algeria

ISBN: (纸本)9783031718472

Graphical argument structures such as Goal Structuring Notation (GSN) are increasingly being used in practice as a means to provide safety assurance to stakeholders, such as regulatory authorities, regarding the dependability and safety of a system. These argument-based assurance cases are utilized in practice to demonstrate that a system is suitable for its intended use. In this paper, we present Structured GSN (SGSN), an open-source tool to create and edit assurance cases. In addition to basic functionalities related to the creation of GSN, we added two types of analysis, syntactic analysis which checks if the developed GSN satisfies structured criteria, and semantic analysis which checks compartmental properties. For the semantic analysis, we mapped the GSN to a formal model i.e., Markov Decision Process (MDP), and checked properties expressed in PCTL using a probabilistic model-checker. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Markov processes

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Crop Yield Prediction in India Using Machine Learning Model 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Kumar, Anuj Sharma, Tarun K. Verma, Om Prakash Shobhit University Gangoh India University Meerut India Dr B R Ambedkar National Institute of Technology Jalandhar India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

The advancement of the global economy is greatly influenced by the agricultural sector. Agricultural production comprises one of India’s major industrial sectors, and economic growth heavily depends on it to maintain sustainability in remote counties. Undoubtedly one of the most significant issues in agricultural production is yield prediction, which will serve the government in formulating suitable planning strategies to improve productivity while also ensuring a reasonable price for impoverished farmers. Farming is dependent on the surroundings as well as the climate. Agriculture is influenced by many variables, including topsoil, weather, tides, nutrients, humidity, rainfall, harvests, pests, and botanicals. One of the key technologies for forecasting grain yields is machine learning. In this paper, we have employed four supervised machine learning approaches which are K-nearest neighbor (KNN), logistic regression, decision tree, and random forest. The factors taken into account in this model include fertilizer consumption, namely nitrogen (N), phosphorus (P), and potassium (K), as well as temperature, humidity, pH, and rainfall. With an astounding 99.77% accuracy, the random forest model was determined to be the most fit. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Modeling the Alienability of an Electronic Document 1

引用

7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Solovyev, Alexander V. Federal Research Center "Computer Science and Control" of the Russian Academy of Scienes 44/2 Vavilova Street Moscow119333 Russia

ISBN: (数字)9783031535529

ISBN: (纸本)9783031535512

The article considers aspects of mathematical modeling of the alienability of an electronic document from the software and hardware environment for its storage. Alienability refers to the possibility of extracting an electronic document from the current hardware and software storage environment and placing it in a new one, where it must also be stored, independent of the storage environment and interpreted. A distinctive feature of the electronic document storage environment is mobility and variability, as well as susceptibility to parametric disturbances. It follows from this that during long-term storage, an electronic document, as a control object, should be maximally stabilized in terms of its main characteristic-safety. To do this, in particular, it should be as independent as possible from the storage environment. As a measure of independence, a mathematical model for assessing the risk of inalienability of a document from the storage environment is proposed. The practical use of the proposed modeling aspects in a number of electronic archive projects, as well as the performed preliminary calculations, give reason to believe that the presented model is adequate for the conditions of the long-term storage problem. #COMESYSO1120. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Risk assessment

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Data Cube Technology for Accessing of Large Database 1

引用

5th International Conference on Computer and Communication Technologies, IC3T 2023

作者： Saxena, Vipin Verma, Vishal Verma, Versha Singh, Karm Veer Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow226025 India Department of Computer Applications and Science School of Management and Science Lucknow226027 India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

During the past years and due to coronavirus pandemics, the online business of the companies has been increased, and people are working from the home through high-speed Internet connectivity. Daily, large amount of database is getting space over the cloud servers, but when one is accessing the desired information from the various search engines, customers are not getting accurate information within minimum time frame. Therefore, present work is an attempt to use of the data cube technology for representation of large amount of the database and faster accessing of desired database as per the customer’s requirements. A sample of database of the Life Insurance of India (LIC), which is a largest leading company working in the insurance sector, is selected for validation of the proposed system model which is designed through the unified modeling language (UML), and the computed results are depicted in the form of the tables and graphs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geometry

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Decision Support systems Based on Artificial Intelligence for Supply Chain Management: A Literature Review 1

引用

International Conference on Intelligent systems and Smart Technologies, I2ST 2023

作者： Hamou, Khalid Ait Ben Jarir, Zahi Quafafou, Mohamed Elfirdoussi, Selwa LISI Laboratory Faculty of Sciences Semlalia Cadi Ayyad University Marrakech Morocco LSIS—CNRS UMR 6168 Université Aix-Marseille Marseille France Emines—University Mohammed VI Polytechnic Benguerir Morocco

ISBN: (数字)9783031476723

ISBN: (纸本)9783031476716

The aim of this paper is to provide an overview of the integration of artificial intelligence (IA) in decision support systems (DSS) to solve problems related to the Supply Chain Management (SCM). Non-intelligent DSS approaches based on traditional methods are unable to handle unstructured data, and they face great difficulties in describing non-linear relationships. Looking for intelligent solutions that are able to overcome these difficulties is becoming increasingly important and has led to a new trend of adding intelligence capability to DSS. Many efforts have been devoted to propose intelligent decision support systems for SCM. To shed light on the degree of AI integration in DSS in this context, we present a literature review highlighting the following results: (a) AI integration in DSS for SCM was still in the development stage, (b) multi-agent systems and machine learning are the most used AI techniques and methods in DSS, and (c) risk management and performance improvement are the most covered activities in intelligent DSS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multi agent systems

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Systematic Mapping Study on the Use of Deep Learning, Image Processing, and IoT in Precision Agriculture 1

引用

International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Alahiane, Abdelaziz El Asnaoui, Khalid Chadli, Sara Saber, Mohammed Mohamed First University ENSAO SmartICT Lab Oujda Morocco

ISBN: (数字)9783031543180

ISBN: (纸本)9783031543173

Precision agriculture is a rapidly growing field focused on optimizing crop production while minimizing environmental impact. One promising approach involves the use of Deep Learning (DL), Image Processing (IP), and the Internet of Things (IoT) to detect crop diseases, pests, and nutrient deficiencies, allowing for targeted and precise application of treatments. Our study involved an analysis of 238 papers published between 2009 and March *** findings reveal that IP and DL are the most frequently employed techniques in precision agriculture, primarily for detection purposes. The results have demonstrated a significant increase in crop yields and improved profitability for farmers with the adoption of these technologies. The overall integration of DL, IP, and IoT in precision agriculture holds immense potential to transform the agricultural industry and make it more sustainable and efficient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Capacity Prediction for Lithium-Ion Batteries Using Different Neural networks Methods 1

引用

International Conference on Intelligent systems and Smart Technologies, I2ST 2023

作者： Loukili, Salah Eddine Ezzati, Abdellah Ben Alla, Said Zraibi, Brahim Faculty of Science and Technology Hassan First University of Settat Settat Morocco

ISBN: (数字)9783031476723

ISBN: (纸本)9783031476716

For the purpose of assuring reliability and safety, the lifespan of a lithium-ion battery must be accurately predicted. Additionally, it serves as a mechanism of early warning to prevent the battery’s failure. New data-driven estimation techniques are made possible by recent advances in machine learning (ML). In this study, we propose two models that make use of the Long Short-Term Memory LSTM (LSTM) and Gated recurrent units (GRU) algorithms, respectively, to estimate the battery’s capacity and increase prediction accuracy. To demonstrate the superiority of the suggested GRU-based approach, a comparison against several ML estimation algorithms is made. Two statistical indicators, the MAE and RMSE, are used in order to carefully evaluate the accuracy of the prediction. Utilizing datasets of various lithium-ion batteries from NASA, experimental validation is carried out. The GRU technique performs better than the others, according to the results, and the suggested method is also effective at decreasing prediction error and enhancing forecasting performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Long short-term memory

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