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16th IEEE International Conference on Computational intelligence and Communication Networks, CICN 2024

作者： Pena-Carrillo, Cesar Gonzales-Loli, Martha Rodriguez, Diego Rodriguez, Ciro Escuela de Ing. de Software Universidad Nacional Federico Villarreal Lima Peru Universidad Peruana de Ciencias Aplicadaas Facultad de Ciencias de la Salud Lima Peru Escuela de Ingenieria Informatica Universidad Nacional Mayor de San Marcos Peru

ISBN: (纸本)9798331505264

Insecurity is a growing concern in Latin America and some parts of the world, and the city of Lima is no exception. Crimes such as assaults, robberies and other crimes are on the rise, generating great concern among the population. In this context, it is important to interact with the population by geo-referencing danger zones with crime maps. This paper aims to develop a real-time application that allows the tracking and monitoring of danger zones, using machine learning techniques and visualization of critical zones. The methodology uses a dataset of crime reports provided by the Peruvian Police from 2018 to 2023, using tools such as Power BI and libraries such as Pandas, Plotly, and Mapbox from Python to extract latitude and longitude. The process includes data analysis and prediction, application development, interactive map creation, data visualization, and application testing. The study concludes that crime mapping is a valuable tool for combating insecurity. Integrating crime data with georeferencing technologies allows detailed visualization of crime patterns, enabling better resource planning, prioritization, and data-driven decision-making to address criminal activity effectively. © 2024 IEEE.

关键词： artificial intelligence algorithms citizen security crime map geospatial data

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The Deep Neural Network Compilers for FPGAs: A Survey 3

The Deep Neural Network Compilers for FPGAs: A Survey

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Tian, Jing Shi, Tianjie Zhao, Yixuan Liu, Feiyang Xi'An Aeronautics Computing Technique Research Institute AVIC Xi'an China

ISBN: (纸本)9798350359961

In embedded systems, the low-latency, low-power consumption, and reconfigurable FPGA component undertakes the inference operations of a large number of computationally intensive tasks, such as artificial intelligence algorithms, making the deep neural network compiler for FPGAs a hot topic. This FPGA compiler can automatically optimize the model calculation diagrams and hardware structure design, and can achieve the end-To-end deployment of neural network models, efficiently and rapidly. This paper summarizes the general framework structure of the neural network compiler for FPGAs, focuses on the development of three representative compilers, compares the deployment performance of specific algorithms on the FPGA platform, summarizes the development advantages and limitations, and finally proposes its application prospects and important research directions in embedded systems, providing a theoretical basis for the rapid iteration and deployment of artificial intelligence algorithms in embedded computing systems. © 2023 IEEE.

关键词： algorithm deployment artificial intelligence algorithms compilers deep neural network FPGA

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Color classification using machine learning

Color classification using machine learning

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作者： Mahajan, Mihir Rajendra California State University – Sacramento

学位级别：硕士

Machine learning and artificial intelligence continue to be active research fields focused on real-world problems. Machine learning uses computers to make predictions based on the provided data set or previous experience. Using machine learning methods such as supervised and unsupervised learning, we can process large data and solve classification problems. In this project, we have applied supervised learning, which is the most often used task-driven classification type of machine learning. The main goal of our project is classification of different color shades using machine learning under ideal and different non-ideal conditions. In this project, we used a binary classification technique of supervised learning to classify different colors.

关键词： CNN artificial intelligence algorithms Color classification Convolution neural network Machine learning Machine vision

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Problém obchodního cestujícího s časovými okny

Problém obchodního cestujícího s časovými okny

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作者： Pavlovič, Dávid Brno University of Technology

Táto práca sa zaoberá riešením problému obchodného cestujúceho s časovými oknami. Problém spočíva v tom, že obchodný cestujúci musí prejsť každým d... 详细信息

Táto práca sa zaoberá riešením problému obchodného cestujúceho s časovými oknami. Problém spočíva v tom, že obchodný cestujúci musí prejsť každým definovaným miestom práve raz, a nakoniec sa vrátiť do pôvodného miesta za čo najnižšiu cenu trasy. Časové okná v tomto probléme definujú určité časové úseky, v ktorých môže byť každé konkrétne miesto navštívené, alebo sa môže stať, že v istom časovom úseku nebude existovať cesta medzi niektorými miestami. Práca sa zaoberá prehľadom tohto problému a problémov jemu podobných. Ďalej sa zaoberá popisom rôznych metód, ktorými tento problém možno riešiť. V rámci tejto práce bola tiež vytvorená aplikácia v programovacom jazyku Python, ktorá slúži na testovanie vybraných metód riešenia. Na záver sú vyhodnotené dané experimenty a porovnaná efektivita daných stratégií.

关键词： Problém obchodného cestujúceho s časovými oknami algoritmy umelej inteligencie kombinatorická optimalizácia plánovanie Travelling salesman problem with time windows artificial intelligence algorithms combinatorial optimization planning Text

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Stem Taper Estimation Using artificial Neural Networks for Nothofagus Trees in Natural Forest

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FORESTS 2022年第12期13卷 2143页

作者： Sandoval, Simon Acuna, Eduardo Univ Concepcion Fac Ciencias Forestales Dept Manejo Bosques & Medio Ambiente Concepcion 4070386 Chile Univ Concepcion Fac Ciencias Forestales Lab Anal & Modelamiento Geoinformac Concepcion 4070386 Chile

The objective of the study was to estimate the diameter at different stem heights and the tree volume of the Nothofagus obliqua (Mirb.) Oerst., Nothofagus alpine (Poepp. et Endl.) Oerst. and Nothofagus dombeyi (Mirb.) Oerst. trees using artificial neural networks (ANNs) and comparing the results with estimates obtained from six traditional taper functions. A total of 1380 trees were used. The ANN trained to estimate the stem diameter with the best performance generated RMSE values in the training phase of 7.5%, and 7.7% in the validation phase. Regarding taper functions, Kozak's model generated better RMSE indicators, but performed not as well as that generated by the ANN. The ANN estimation of the total volume was carried out in two phases. The first used the diameter estimation to determine the volume at one-centimeter intervals along the stem (one-phase ANN), and the second used the estimation of the one-phase ANN as an additional variable in an ANN that directly estimated the tree cumulative volume (two-phase ANN). The two-phase ANN method generated the best performance for estimating the cumulative volume in relation to one-phase ANN and the Kozak taper function, generating RMSE values for N. obliqua, N. alpina and N. dombeyi of 9.7%, 8.9% and 8.8%, respectively.

关键词： machine learning artificial intelligence algorithms stem profile modeling volume estimate

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Intelligent Energy Management Systems in Industry 5.0: Cybersecurity Applications in Examples

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ENERGIES 2024年第23期17卷 5871页

作者： Wyrzykowska, Barbara Szczepaniuk, Hubert Szczepaniuk, Edyta Karolina Rytko, Anna Kacprzak, Marzena Warsaw Univ Life Sci SGGW Inst Managment Nowoursynowska 166 St PL-02787 Warsaw Poland Polish Air Force Univ Dywizjonu 303 St 35 PL-08521 Deblin Poland Warsaw Univ Life Sci SGGW Inst Econ & Finance Nowoursynowska 166 St PL-02787 Warsaw Poland

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and the growing need to protect data in the face of increasing cyber threats. The challenges faced by small and medium-sized enterprises (SMEs) using solutions based on renewable energy sources, such as photovoltaic farms, are also analyzed. The article presents examples of IEMS applications and discusses methods for securing these systems, offering an overview of cyber threat protection tools in the context of modern energy management. The analysis carried out provided information that will help businesses make rational decisions and contribute to shaping the state's macroeconomic policy on cybersecurity and energy savings. The results of this research can also help develop more effective strategies for managing technology and IT infrastructure, which is crucial in the digital age of Industry 5.0.

关键词： intelligent energy management systems Industry 5.0 cybersecurity attack vectors artificial intelligence algorithms

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Feature Selection algorithms in Intrusion Detection System: A Survey

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第10期12卷 5079-5099页

作者： Maza, Sofiane Touahria, Mohamed Univ Ferhat Abbas Setif 1 Dept Comp Sci Setif 19000 Algeria

Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.

关键词： Intrusion Detection System Feauture Selection artificial intelligence algorithms Deterministic algorithms Swarm intelligence

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Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

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MATERIALS 2023年第8期16卷 3220-3220页

作者： Saldana, Manuel Galvez, Edelmira Navarra, Alessandro Toro, Norman Cisternas, Luis A. Univ Arturo Prat Fac Engn & Architecture Iquique 1110939 Chile Univ Antofagasta Dept Ingn Quim & Proc Minerales Antofagasta 1270300 Chile Univ Catolica Norte Dept Met & Min Engn Ave Angamos 0610 Antofagasta 1270709 Chile McGill Univ Dept Min & Mat Engn 3610 Univ St Montreal PQ H3A 0C5 Canada

Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process's productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.

关键词： SAG mill comminution processes artificial intelligence algorithms modeling optimization mineral processing

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A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

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ENERGIES 2024年第7期17卷 1627页

作者： Famoso, Fabio Oliveri, Ludovica Maria Brusca, Sebastian Chiacchio, Ferdinando Univ Messina Dept Engn I-98166 Messina Italy Univ Catania Dept Elect Elect & Comp Engn I-95125 Catania Italy

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.

关键词： cluster analysis artificial intelligence algorithms Reliability Block Diagrams wind energy wind farm production estimation artificial neural network

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Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms

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AGRICULTURAL WATER MANAGEMENT 2023年 280卷

作者： Di Nunno, Fabio Granata, Francesco Univ Cassino & Southern Lazio Dept Civil & Mech Engn DICEM Via Biasio 43 I-03043 Cassino Frosinone Italy

In years of increasing impact of climate change effects, a reliable characterization of the spatiotemporal evolutionary dynamics of evapotranspiration can enable a significant improvement in water resource management, especially as regards irrigation activities. Sicily, an insular region of Southern Italy, has exceptionally valuable agricultural production and high irrigation needs. In this study, the ETo reference evapotranspiration in Sicily was first evaluated on the basis of historical and future climate parameters, referring for future values to two climate scenarios characterized by different Representative Concentration Pathways: RCP 4.5 and RCP 8.5. Then, the Hierarchical algorithm was used to divide Sicily into three homogeneous regions, each characterized by specific ETo features. In addition, some Machine Learning (ML) algorithms were used to develop forecasting models based on only historical data. Support Vector Regression (SVR) was used to predict the future values of Tmin and Tmax, while an ensemble model based on Multilayer Perceptron (MLP) and M5P Regression Tree was developed for the ETo forecasting. Predictions made with the ensemble MLP-M5P model were compared with the ETo computed for the RCP 4.5 and RCP 8.5 future climate scenarios. During the forecast period, from 2001 to 2091, evapotranspiration increases were observed for all three clusters. For cluster C1, along the coast, percentage increases of 7.52%, 14.64% and 10.78%, were computed for RCP 4.5, RCP 8.5, and MLP-M5P, respectively, while, for cluster C3, in the inland, percentage increases were higher and equal to 8.12%, 16.71%, and 14.98%, respectively. The ensemble MLP-M5P model led to intermediate trends between RCP 4.5 and RCP 8.5, showing a high correlation with the latter (R2 between 0.93 and 0.98). The developed approach, based on both clustering and forecasting algorithms, provided a comprehensive analysis of the reference evapotranspiration, with the detection of the different ho

关键词： Climate change artificial intelligence algorithms Clustering Ensemble models Mediterranean climate Irrigation planning

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