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11th IEEE International Conference on Software Defined systems, SDS 2024

作者： Cardel, Victor S. Zisner, Philipp Rettore, Paulo H. L. Sevenich, Peter Santos, Bruno P. Federal University of Bahia Dept. of Computer Science Brazil Fraunhofer FKIE Dept. of Communication Systems Bonn Germany

ISBN: (纸本)9798331518325

Intelligent Transportation systems (ITSs) increasingly rely on cloud computing to gather and process vehicular data, enabling the development of adaptable transportation solutions. However, the availability of such data is often limited by privacy, security, and commercial concerns, challenging the open research in ITS. Existing datasets rarely include both intra-vehicular data (created from or inside the vehicle) and extra-vehicular data (created from surrounding sources) or provide flexible scenarios for data extraction. This paper introduces a methodology to collect and publish ITS data, including intra and extra-vehicular sensor data. A quantitative analysis was conducted as part of the data characterization to show the associated data challenges. Moreover, the dataset collected is freely accessible, allowing innovation in ITS research and application development. © 2024 IEEE.

关键词： Big-data Data Fusion Intelligent Transportation systems Vehicular Cloud Computing

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The Cubic Bipolar Neutrosophic Sets theory and Uncertainty Management in Environmental Data Analysis

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Neutrosophic Sets and systems 2024年 72卷 60-133页

作者： Salama, A.A. Khalid, Huda E. Mabrouk, Ahmed G. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dean of higher Institute of Computer Science and Information Systems Ministry of Higher Education October City Egypt Telafer University Telafer Iraq Higher Institute of Computer Science and Information Systems Ministry of Higher Education October City Egypt

Environmental data analysis often faces uncertainties in measurements. Cubic Bipolar Neutrosophic Sets (CBN Sets) provide a powerful framework to address this challenge. This paper explores the mathematical foundations of CBN Sets and highlights their practical applications through illustrative examples. We demonstrate how CBN Sets effectively capture varying degrees of certainty, possibility, and impermanence in environmental parameters. The concept of inclusion relations facilitates comparisons and information fusion. Fundamental set operations (intersection, union, complement) are explored for manipulating and analyzing uncertain environmental data. We present the application of CBN Sets in water quality assessment, highlighting their ability to analyze parameters while accounting for measurement uncertainties. The potential for air quality monitoring using CBN Sets is also discussed. Finally, distance measures and similarity coefficients are introduced to quantify relationships between air quality characteristics from different stations. By leveraging CBN Sets and their associated operations, researchers can gain a more nuanced understanding of environmental data, enabling informed decision-making for a healthier planet. © 2024, University of New Mexico. All rights reserved.

关键词： Environmental monitoring

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Anomaly Detection Model Based Visual Inspection Method for PCB Board Manufacturing Process

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Transactions of the Korean Institute of Electrical Engineers 2024年第11期73卷 2024-2029页

作者： Lee, Sang-Jeong Seo, Sung-Bal Bae, You-Suk Vision AI Business Team LG CNS Korea Republic of Dept. of Computer Engineering Tech University of Korea Korea Republic of

We developed a visual inspection method for PCB board using an anomaly detection model. To improve feature extraction performance, we developed and optimized the feature extractor by comparing three types of backbone models. Then we compared two anomaly detection models with developed feature extractor as a backbone for visual inspection. Finally, we found the optimized loss function named mean-shifted contrastive loss which showed the highest accuracy in our experiment. Copyright © The Korean Institute of Electrical Engineers.

关键词： Inspection

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Detection and Classification of Skin Cancer Using YOLOv8n 11

Detection and Classification of Skin Cancer Using YOLOv8n

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11th International Conference on Electrical engineering, computer Science and Informatics, EECSI 2024

作者： Riyadi, Munawar A Ayuningtias, Adela Isnanto, R Rizal Universitas Diponegoro Dept. of Electrical Engineering Semarang Indonesia Universitas Diponegoro Dept. of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798350355314

Skin cancer is a disease caused by the growth of abnormal cells in skin tissues. The World Health Organization (WHO) has recorded an 88% increase in deaths due to skin cancer caused by exposure to ultraviolet rays. Currently, in the medical field, the diagnosis of skin cancer involves a biopsy process, which requires considerable time and cost. Therefore, this study aims to develop a system for detecting and classifying skin cancer based on the shape of skin lesions using the You Only Look Once version 8 nano (YOLOv8n), which can detect lesions rapidly. The dataset used is ISIC 2019, comprising of 4289 images of cancerous skin lesions divided into 9 classes: Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma, Actinic Keratosis, Dermatofibroma, Nevus, Seborrheic Keratosis, Pigmented Benign Keratosis, and Vascular Lesion. Experimental results show that the designed system performs well in detecting and classifying the lesions, achieving an overall accuracy of 93.5%, with a Precision of 93.5%, Recall of 93.7%, and an F1-Score of 93.5%. © 2024 IEEE.

关键词： Lung cancer

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Holistic Design of Economical and Secure Data Centers for Developing Countries: A Free Integrative Approach 21

Holistic Design of Economical and Secure Data Centers for De...

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2024 IEEE/ACS 21st International Conference on computer systems and Applications, AICCSA 2024

作者： Bouri, Mohamed Ali Kammoun, Habib M. Aouicha, Mohamed Ben Dept. Computer Sciences and Communications Data Engineering and Semantics Sfax Tunisia University of Sfax Dept. Computer Sciences and Communications Research Groups in Intelligent Machines Sfax Tunisia University of Sfax Dept. Computer Sciences and Communications Data Engineering and Semantics Sfax Tunisia

ISBN: (纸本)9798331518240

This proposal aims to suggest improvements for the article titled 'Low-Cost Data Centers in Developing Countries' with the aim of enhancing its content, relevance, and practicality. Our expertise in data infrastructure, we believe these improve-ments will provide valuable insights and guidance to readers interested in establishing efficient data centers in developing countries. © 2024 IEEE.

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A Dual-Action Approach to Mosquito Control: Harnessing Mosquitoes Affinity for Light with Metofluthrin's Lethality 18

A Dual-Action Approach to Mosquito Control: Harnessing Mosqu...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Mittal, Manisha Singh, Mahender Dewan, Prachi Singh, Prabhmeet Dept. of Electronics and Communication Engineering New Delhi India Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9789380544519

Amidst rapid urbanization and fluctuating climatic patterns, the world witnessed a surge in mosquito-borne diseases, escalating global health concerns. Mosquitoes, often dismissed as mere irritants, are carriers for a spectrum of conditions, including malaria, dengue, and the Zika virus, with WHO reporting large numbers of dengue cases annually. This review delves deep into the observation of mosquito behavior and the challenges in eradicating these pests, emphasizing the inefficacy of singular sensory targeting repellents. This paper introduces a pioneering device, combining the mosquito's affinity towards light with the suffocating effects of metofluthrin, offering a holistic solution. By the combination of advanced engineering with a comprehensive understanding of mosquito behavioral dynamics, this device promises not only to repel but to substantially reduce these vectors, fostering safer indoor spaces. Through a detailed exploration of current methodologies and the introduction of this integrated strategy, this paper provides insights into creating a healthier, mosquito-free environment. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： fluorescent light metofluthrin mosquito control mosquito-borne diseases multifaceted sensory system

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Performance Analysis of Machine Learning and Deep Learning Techniques for Answer Type extraction of Marathi Questions 5

Performance Analysis of Machine Learning and Deep Learning T...

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5th Biennial International Conference on Nascent Technologies in engineering, ICNTE 2023

作者： Amin, Dhiraj Govilkar, Sharvari Kulkarni, Sagar Pillai College of Engineering Dept. of Computer Engineering India

ISBN: (纸本)9781665465045

Question answering systems involve extraction of correct answers for natural language questions provided as input. Question classification is an important part of the question processing phase where natural language questions are categorized into predefined classes which specifies expected type of answer for the question. Extraction of answer type can be performed using machine learning and deep learning classification techniques which helps to reduce the list of possible correct answers for a question. In this paper we have compared various classification techniques which can be used for building a Marathi question classification system. Additionally, we have created a Marathi question classification dataset by translating existing TREC dataset available in English language. We observed that fine tuning the RoBERTa based monolingual language model for question classification was the best classification technique with accuracy of 91% in the coarse grained category of question classification and accuracy of 85% in the fine grained category of question classification. © 2023 IEEE.

关键词： Extraction

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Rainfall Forecasting Using Hybrid Learning Method

Rainfall Forecasting Using Hybrid Learning Method

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Hawari, Ihtifazhuddin Isnanto, R. Rizal Dwi Nurhayati, Oky Diponegoro University Dept. of Electrical Engineering Semarang Indonesia Diponegoro University Dept. of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798350366648

Rainfall is one of the most important meteorological variables. Rainfall has a multiplicity of effects on agriculture, ecosystems and urbanization. The amount of rainfall cannot currently be determined due to climate change. For this reason, in order to mitigate the effects that arise from heavy rainfall, it is crucial to forecast the amount of rainfall. Currently, statistical techniques and machine learning are two of the methods has been used to forecast rainfall. These methods have been successfully to forecast rainfall. However, it still has low accuracy and need more time to forecast because the rainfall influence parameters are time-series and non-linear. This study will employ a hybrid learning technique to resolve the issue. The method used is a combination of two algorithms, namely long short-term memory (LSTM) and particle swarm optimization (PSO), and the prediction system will be evaluated using RMSE and MAE. Time-series and non-linear rainfall data will be overcome by the LSTM algorithm, and uncertain hyperparameter settings in the LSTM method will be overcome by the PSO algorithm. The ideal LSTM algorithm employing PSO optimization yields the following hyperparameter value results: 143 neurons, learning rate of 0.0000840, and 122 epochs in 50 iterations. With the lowest RMSE and MAE values of 18.557095 and 8.972361, respectively, the prediction model's optimal hyperparameter values can forecast the next five days based on the data used. © 2024 IEEE.

关键词： forecast hybrid learning LSTM PSO rainfall

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Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Pandya, Kushagra Hathi, Heli Ravikumar, R.N. Nadarajan, Sivakumar Praveen, Manoj Pal, Vinod Kumar Tss Consultancy Pvt. Ltd. Rajkot India Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Velalar College of Engineering and Technology Dept. of Ai & Ds Erode India Marwadi University Dept. of Computer Science Rajkot India

ISBN: (纸本)9798350379716

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance. © 2024 IEEE.

关键词： Contrastive Learning

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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