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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sérgio, B. Mastelini, Saulo M. Paula, Marcos B. S. Santos, Moisés R. Guarnier, Ewerton Filho, Donato S. Santos, Maria E. C. M. Fernandes, Thaina R. Rosa, Victor C. V. Volt Robotics Brazil Institute of Mathematics and Computer Science University of São Paulo Brazil University of Porto Faculty of Engineering Portugal Norte Energia S.A. Brazil

ISBN: (纸本)9798350374889

With its vast territory and diverse population distribution, Brazil is undergoing significant developments in its energy sector as it transitions towards a liberalized market. By 2030, the country aims to open the market to all consumers, enhancing competition, and liquidity in energy contracts, and fostering a dynamic trading environment. Effective market price monitoring is essential for understanding its dynamics and guiding decision-making processes in engineering applications. This paper aims to predict medium and long-term price movements in the free energy market. Our analysis of market price curves for different time horizons offers insights into energy market dynamics, enabling energy companies to optimize portfolio strategies and mitigate risks effectively. We consider forecast horizons of one quarter and one semester ahead, providing valuable insights for stakeholders navigating Brazil's evolving energy landscape. We utilize a combination of Machine Learning (ML)-based forecasters enriched by a broad set of exogenous data sources to provide forecasts for energy contracts whose effective date is Y2, Y3, and Y4 years ahead. In this study, we explore a simple yet effective and explainable alternative to using all the exogenous time series directly as input for our models. Namely, multiple related time series are aggregated into a series of histograms. Our proposed ensemble outperforms traditional statistical-based forecasting models, other ML solutions, and strong baselines in terms of resulting financial returns, particularly in the one-quarter prediction horizon, while also achieving competitive results for one semester ahead. © 2024 IEEE.

关键词： Energy Market Ensemble Forecasting Long-term Prices Machine Learning

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Recognition of the emotional state of energy facilities operators based on artificial immune systems 4

Recognition of the emotional state of energy facilities oper...

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4th International Conference on Applied Physics, Information Technologies and Engineering 2022, APITECH 2022

作者： Nuriakhmetov, A.I. Bogdanova, D.R. Yusupova, N.I. Faculty of Computer Science and Robotics Ufa State Aviation Technical University Ufa450077 Russia

This research paper presents results on the construction of a system for recognizing emotional state of energy facilities operators by the expression of their faces in the images. The model uses one of the algorithms of artificial immune systems as a classifier. An artificial immune system is a concept that contains methods and algorithms that try to apply the basic mechanisms of biological immune system to solve various practical problems. In accordance with the main aim of the research, the article deals with existing concepts of emotion classification as well as current methods for recognizing emotions from facial expressions in images. Also, a brief review of the literature is given in accordance with the questions posed. The proposed model uses a feature extraction approach based on marking the human face with 68 points, followed by extraction of their coordinates. For the obtained coordinates, a new special technique for their processing for subsequent classification is proposed. The built model is able to classify the 7 basic Ekman emotions both on images and in the video stream, with an accuracy of about 80%. © Published under licence by IOP Publishing Ltd.

关键词： Extraction

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Analog, Programmable Switched Capacitor FIR Filter Based on Rotator Architecture Implemented in CMOS Technology 26

Analog, Programmable Switched Capacitor FIR Filter Based on ...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Pawlowski, Pawel Dlugosz, Rafal Radkowski, Marek Watly, Mateusz Dabrowski, Adam Poznań University of Technology Division of Signal Processing and Electronic Systems Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznań60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350304985

The paper presents project and its verification of a prototype integrated circuit containing an analog, programmable finite impulse response (FIR) filter, implemented in CMOS 350 nm technology. The structure of the filter is based on the switched capacitor technique. In circuits of this type, one of main challenges is an efficient implementation of filter coefficients, which result from several factors described in this work. When implementing such filters as programmable circuits, the values of their coefficients have to be limited to a selected range, i.e. a given resolution in bits. In the implemented prototype filter, the filter coefficients are represented by 6 bits in sign-magnitude notation, so they can take 63 different values only. In such filters, it is not possible to directly implement any frequency response of the filter. Each time, it is necessary to properly round the theoretical values of the coefficients so that they fit into the available range of discrete values resulting from the implementation. The authors of the work designed an algorithm that allows such matching. The paper also presents results of measurements of the prototype chip. © 2023 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： FIR filters

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Optimization of the Storage Location Assignment Problem Using Nested Annealing 12th

Optimization of the Storage Location Assignment Problem Us...

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11th and 12th International Conferences on Operations Research and Enterprise Systems, ICORES 2022 and 2023

作者： Oxenstierna, Johan van Rensburg, Louis Janse Stuckey, Peter J. Krueger, Volker Department of Computer Science Lund University Lund Sweden Faculty of Information Technology Monash University Melbourne Australia Flux Robotics Caltowie Australia Kairos Logic AB Lund Sweden

ISBN: (纸本)9783031496615

The Storage Location Assignment Problem (SLAP) has a significant impact on the efficiency of warehouse operations. We propose a multi-phase optimizer for the SLAP, where the quality of an assignment is based on distance estimates of future-forecasted order-picking. Candidate assignments are first sampled using a Markov Chain accept/reject method. Order-picking Traveling Salesman Problems (TSPs) are then modified according to the assignments and solved. The model is graph-based and generalizes to any obstacle layout in two dimensions. We investigate whether optimization speed-ups are possible using methods such as cost approximation, rejection of samples with low approximate cost and restarts from local minima. Results demonstrate that these methods improve performance, with total travel-cost reductions of up to 30% within 8 h of CPU-time. We share a public repository with SLAP instances and corresponding benchmark results on the generalizable TSPLIB format. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hamming distance

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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A Comparative Study of Lossless Compression Technique in Quantum Secure Direct Communication 1

A Comparative Study of Lossless Compression Technique in Qua...

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1st International Conference on Cyber Security and Computing, CyberComp 2024

作者： Ishak, Izlin Zuriani Wooi, Liew Seow Harun, Nur Ziadah Azahari, Nur Shahirah Ramli, Sofia Najwa Ghazali, Osman Politeknik Seberang Perai Department of Information Technology and Communication Pulau Pinang Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Selangor Malaysia Raffles University Faculty of AI and Robotics Johor Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Department of Information Security and Web Technology Johor Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9798350387728

This is a comparative study to explore the performance of three lossless compression techniques, namely Run Length Encoding (RLE), Shannon-Fano Coding (SFC), and Huffman Coding (HC) in the context of a Hybrid M-Ary in Braided Single Stage (HMBSS) protocol communication scheme. The previous study on HMBSS using HC found that HC's compression technique has drawbacks, i.e., it is sensitive to errors and noise in the transmission channel and limited to certain data types. These drawbacks did affect the value of the compression rate limit. In this study, we evaluate the effectiveness of RLE, SFC, and HC in terms of data size reduction, coding efficiency, and data transmission time by comparing their results arising from the simulation experiments using Python coding in Jupiter Notebook and developed based on the HMBSS protocol algorithm. This simulation experiment starts with an initial six randomly generated datasets. Each dataset is tested using three compression techniques, which resulted in 18 different datasets. The results from the experiments reveal that SFC and HC consistently achieved a higher compression ratio and coding efficiency than RLE. These two compression techniques' algorithms effectively reduce the data size while preserving the original information. In our study, we also find that the data transmission time is increased with larger text sizes for all three compression techniques. This indicates that the larger the data size, the more transmission time is required with regard to what the compression technique's algorithm is. © 2024 IEEE.

关键词： Quantum communication

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Predicting Threat Degree for Onset of Type 2 Diabetes Mellitus Based on Machine Learning Methods 6th

Predicting Threat Degree for Onset of Type 2 Diabetes Mellit...

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6th Computational Methods in Systems and Software, CoMeSySo 2022

作者： Shakhmametova, Gyuzel Vakkazov, Nikita Klimets, Sofya Computer Science and Robotics Department Ufa State Aviation Technical University Ufa Russia Faculty of General Medicine Bashkir State Medical University Ufa Russia

ISBN: (纸本)9783031214349

This article discusses the use of machine learning methods to predict the degree of threat for onset of type 2 diabetes mellitus in patients aged 25 years. Type 2 diabetes mellitus is a disease that complicates the course of other concomitant diseases, and predicting the threat of its occurrence is an important element in forming the trajectory of diagnosis and treatment of the patient. A binary classification model based on the logistic regression method has been proposed and developed. The developed approach identifies the threat of diabetes mellitus onset using indicators such as age, sex, body mass index (BMI) and glycated hemoglobin (HbA1c). The paper describes how to build a model, and how to generate and prepare data for binary logistic regression. To implement the described approaches, the Python programming language, the Jupyter Lab development environment and scikit-learn, scipy, pandas, and numpy packages were used. Performance analysis showed accuracy of the proposed model as 0.98 on test data. The developed software can be used as a separate application and be built as a module into the clinical decision support system #COMESYSO1120. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Logistic regression Machine learning Predicting Type 2 diabetes mellitus

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Deep Learning-Based Classification of Areca Nut Yellow Leaf Disease with ResNet-50 CNN

Deep Learning-Based Classification of Areca Nut Yellow Leaf ...

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2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Veeresha, R.K. Lathish Kumar, N.D. Shetty, Samarth S. Prasad, Shrajan G. Poojary, Swaroop S. Karegoudra, Shilpa Koten, Hasan Department of Robotics and Artificial Intelligence Udupi574110 India Department of Computer Science and Engineering Udupi574110 India Istanbul Medeniyet University Faculty of Engineering and Pure Sciences Department of Mechanical Engineering Istanbul Turkey

ISBN: (纸本)9798350388602

Detecting healthy arecanut leaves, yellow leaf disease in arecanut, and differentiating these from other types of leaves using deep learning involves designing an advanced neural network model for precise image classification. The model is trained on a dataset comprising images of healthy arecanut leaves, arecanut leaves affected by yellow leaf disease, and various other leaves from different plant species. Convolution Neural Networks (CNNs) are leveraged to extract and analyze intricate patterns in the images, enabling the model to effectively classify each leaf type. Transfer learning techniques might be utilized to improve the model's performance and adaptability. The model's effectiveness is evaluated using accuracy, precision, recall, and F1 score on a dedicated test dataset. This paper explores the methodology and results of employing image detection for the identification of healthy arecanut leaves, yellow leaf disease, and other leaf types, with potential applications in agricultural diagnostics, disease management, and crop monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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