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A Review of Performance and Complexity on Sorting Algorithms

A Review of Performance and Complexity on Sorting Algorithms

2022 International Conference on Computing, Networking, Telecommunications and engineering Sciences Applications, CoNTESA 2022

作者： Salihu, Armend Hoti, Mergim Hoti, Arber South East European University Faculty of Contemporary Sciences and Technologies Macedonia University of Prishtina "hasan PRISHTINA" Faculty of Electrical and Computer Engineering Serbia

ISBN: (纸本)9798350398014

computer systems have increased the volume of digital data in nearly every sector. However, many digital data have also created a problem in their management, one of the main problems being the sorting of data. For this issue, several different sorting algorithms have been developed. This paper thus analyzed the main classification algorithms concerning the complexity of time, space, and their performance and used functions developed in the MATLAB environment for comparison of execution time. Some of the advantages and disadvantages of various sorting algorithms are also identified in this article which is explained how they are executed. © 2022 IEEE.

关键词： Sorting

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Micro Frontend Architecture

Micro Frontend Architecture

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Vladimír Kunštnár Patrik Podhorský Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350349153

ISBN: (纸本)9798350349160

Our paper focuses on software architectures in web application environments, specifically their evolution in client interface development. We will focus on implementing client interfaces with Micro Frontends, which are based on the principle of microservices. We will explain how to implement this architecture and integrate multiple Micro Frontends into a single web application. Additionally, we will evaluate the efficiency and performance of Micro Frontends compared to monolithic client interfaces. Finally, we will compare the performances of the two architectures and propose an improvement to implementing Micro Frontends. Our improvement proposal will explain how Micro Frontends can be implemented to create more modular and reusable user interfaces using well-known methodologies and design patterns.

关键词： Technological innovation Software architecture Design methodology Microservice architectures computer architecture User interfaces Proposals

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy 12

Algorithmic Trading Model for Stock Price Forecasting Integr...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Govindan, Niranjana Baskaran, Bharathi Azizan, Azizul Yakub, Fitri Dhamanti, Inge Dhaya, C. SRMIST Faculty of Engineering & Technology Department of Computing Technologies Kattankulathur Chennai India Faculty of Intelligence Kuala Lumpur Malaysia Malaysia-Japan International Institute of Technology UTM Electronic System Engineering Department Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Adhiparasakthi Engineering College Department of Computer Science and Technology Melmaruvathur India

ISBN: (纸本)9798350340204

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model. © 2024 IEEE.

关键词： Adversarial machine learning

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Research and development of an information system for speech recognition based on neural network algorithms metrics 4

Research and development of an information system for speech...

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4th IEEE International Conference on Smart Information Systems and technologies, SIST 2024

作者： Kanashev, Maksat Mukasheva, Assel Yedilkhan, Didar Rakhmetulaeva, Sabina Gnatyuk, Sergiy Nazym, Mirambayeva Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The Department of Computer Engineering Astana IT University Astana Kazakhstan The Department of Information Systems International University of Information Technologies Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350374865

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields. © 2024 IEEE.

关键词： Audio recordings

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Performance Evaluation of Diverse ML Techniques in Disasters Twitter classification 2

Performance Evaluation of Diverse ML Techniques in Disasters...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Nath, Basudev Sahoo, Deepak Biswal, Bhramara Bar Patra, Sudhansu Shekhar Ray, Rakesh Kumar Sri Sri University Faculty of Emerging Technologies Cuttack India Giet University Dept. of Computer Science & Engineering Gunpur India Kiit Deemed to be University School of Computer Applications Bhubaneswar India Centurian University of Technology and Management Mca Department Khordha India

ISBN: (纸本)9798350372892

Social media has grown to be an essential and significant aspect of our lives in the modern day. Due to the widespread usage of laptops, mobile phones, and other devices, individuals find it simpler to communicate with one another and to post their opinions, concerns, and other information on social media sites like Facebook, Instagram, and tweeter. Several government and commercial organizations use these social media platforms to gather text and assess people's sentiments during times of crisis. Tracking the tweets relating to the accident is too tough for them to do. To categories tweets as 'disaster' or 'non-disaster,' the paper implemented three models, the Naïve-Bayes, KNN and support vector machine classification models. The Naïve-Bayes algorithm shows the highest accuracy of 86.591. © 2024 IEEE.

关键词： Tweets

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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images 19

Assessing Parkinson's Disease Severity Using Channel Attenti...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Fadaie, Fatemeh Aghapanah, Hamed Sedighifard, Alireza Shirvan, Hadi Niknam AI and Robotics University of Isfahan Department of Computer Engineering Isfahan Iran School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Department of Bioelectrics and Biomedical Engineering Isfahan Iran Iran University of Science and Technology Department of Artificial Intelligence Isfahan Iran University of Tehran Faculty of New Sciences and Technologies Isfahan Iran

ISBN: (纸本)9798331507565

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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AHHO: Arithmetic Harris Hawks Optimization algorithm for demand side management in smart grids

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Discover Internet of Things 2023年第1期3卷 3页

作者： Manzoor, Awais Judge, Malik Ali Islam, Saif ul Neggaz, Nabil Abulaigh, Laith Ahmad, Ishfaq Department of Computer Science COMSATS University Islamabad Islamabad44550 Pakistan Department of Computer Sciences Institute of Space Technology Islamabad Pakistan Faculty of Mathematics and Computers University of Sciences and Technologies-USTO-MB Oran Algeria Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Department of Computer Science and Engineering The University of Texas at Arlington Arlington United States

Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively. © The Author(s) 2023.

关键词： Optimization

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Object Detection Using a YOLOv4 Model in Video Surveillance 4

Object Detection Using a YOLOv4 Model in Video Surveillance

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Balamurugan, G. Arulalan, V. Premanand, V. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350300888

One of the most significant issues in surveillance videos is the detection of vehicles and objects at real time. Adverse weather conditions and lighting (illumination) variations are a few of the challenges faced while detecting objects through surveillance videos. In this work, a You Only Look Once v4 model, combined with a Spatial Pyramid Pooling and a Path Aggregation Network block and a contrast enhancement framework for effective detection of objects with higher accuracy is proposed. The video is converted to frames and is completely enhanced throughout using the Exposure Fusion Network. The brightness of the frames is equalized such that every point in the frame is clearly visible to the naked eye. Finally, the objects in the frames are detected using a YOLOv4 model, which is coupled with a SPP layer and a PAN network. The model generates an accuracy of 92.6%. © 2023 IEEE.

关键词： Object detection

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VocabGCN-BERT: A Hybrid Model to Classify Disaster Related Tweets 20

VocabGCN-BERT: A Hybrid Model to Classify Disaster Related T...

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20th OITS International Conference on Information Technology, OCIT 2022

作者： Ranjan Paul, Nayan Sahoo, Deepak Chandra Balabantaray, Rakesh Computer Science and Engineering Iiit Bhubaneswar Bhubaneswar India Sri Sri University Faculty of Emerging Technologies Cuttack India

ISBN: (纸本)9781665493482

When it comes to classifying tweets about disasters, Deep Neural Network-based models have shown great potential over conventional machine learning models. In particular, Bidirectional Encoder Representations from Transformers(BERT) are effectively used to capture the contextual information present in a tweet. But it is not effectively capturing the global structural information of tweets. A Graph Convolutional Network's(GCN) power lies in its ability to capture global information. In this study, we present a novel hybrid model called VocabGCN-BERT by combining the GCN made from a vocabulary graph of tweets and the pre-trained BERT model. A powerful representation for classifying tweets is created by combining local contextual information acquired from BERT with global structural information acquired from VocabGCN. The results of the experiments demonstrate that the proposed VocabGCN-BERT performs better than the currently available state-of-art models based on GCN on seven publicly available datasets by a margin of +1.66% to +4.79% in weighted average F1 score and +1.45% to +4.34% in accuracy. © 2022 IEEE.

关键词： Disasters

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Contemporary Approach of Additive Manufacturing for Sustainable Products in the Era of Circular Economy 8th

Contemporary Approach of Additive Manufacturing for Sustaina...

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8th EAI International Conference on Management of Manufacturing Systems, MMS 2023

作者： Knapčíková, Lucia Behúnová, Annamária Tauberová, Rebeka Kaščak, Jakub Husár, Jozef The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Industrial Engineering and Informatics Prešov Slovakia Technical University of Kosice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Kosice Slovakia The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Computer Aided Manufacturing Technology Prešov Slovakia

ISBN: (纸本)9783031531606

Considering the need for mass customization in Industry 4.0, it is necessary to develop non-traditional production methods. Therefore, additive manufacturing can become a key technology for the production of customized products thanks to its ability to create irregular objects or shapes. In addition to enabling new design possibilities, the additive manufacturing method holds promise for increasing social, economic, and environmental sustainability. It also has the potential to reduce energy consumption and lower polymer material consumption. Additive manufacturing contrasts with conventional processes, as it consists in applying materials layer by layer. Conventional manufacturing operations produce polymer waste that is difficult to reintroduce into manufacturing processes. The aim of the contribution is the possibility of using additive manufacturing in the design and processing of new effective use of the product with regard to the environmental aspect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 3D printing

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