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7th International Conference on Instrumentation, Control, and Automation, ICA 2021

作者： Bandong, Steven Nazaruddin, Yul Yunazwin Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung Indonesia National Center for Sustainable Transportation Technology CRCS Instrumentation and Control Research Group Department of Engineering Physics Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision NLP and Big Data Analytics Institut Teknologi Bandung Instrumentation and Control Research Group Department of Engineering Physics Bandung Indonesia

ISBN: (纸本)9781665432955

The increase in free trade will also amplify the exchange of goods between countries and islands, especially in the seaports. The manual operation of the gantry-crane at the seaports has a risk due to human negligence. For that reason, automation is strongly needed in the loading and unloading of the container. Camera-based object detection is implemented to achieve this goal. In the recent years, CNN gives better accuracy in term of image classification, recognition and detection compared to the traditional method that use handcrafted features. In this paper, several advanced detection methods using CNN-based object detection, namely MobileNet, ResNet, and Faster RCNN are compared to detect and track the movement of containers. The results of the experiment show that the SSD ResNet and Faster RCNN methods are superior in terms of detection accuracy based on the Precision, Recall, Average Precision, and IoU values. MobileNet v3 excels in speed detection compared to the other methods. © 2021 IEEE.

关键词： Containers

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Revolutionizing Oil Spill Detection: A Machine Learning Approach for Satellite Image Classification

Revolutionizing Oil Spill Detection: A Machine Learning Appr...

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Telecommunications Conference (ITC-Egypt), International

作者： Khaled Sherif Faris H. Rizk Ahmed Mohamed Zaki Marwa M. Eid Nima Khodadadi Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-Kenawy Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Department of Civil and Architectural Engineering University of Miami Coral Gables FL USA Faculty of Engineering Design and Information & Communications Technology (EDICT) School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Jadara Research Center Jadara University Irbid Jordan

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Identifying and labeling oil spills in satellite imagery is an essential activity of both environmental monitoring and disaster response actions. This work is dedicated to applying an artificial Neural Network (ANN) model for gathering oil spill data by using a dataset that is specially curated for this reason. Our dataset was developed from satellite pictures of the ocean, some of which depict oil spills and some that do not. The features were extracted from each picture using computer vision algorithms. Our ANN model is trained to distinguish between two classes: The metrics that are looked at consist of accuracy, sensitivity, specificity, PPV, NPV, and statistical significance, and they illustrate how the model performs. As a result, the ANN model gets an accuracy of 96.88% and a sensitivity of 92.86% at the same time, while the specificity is 99.88%. The sensitivity of this diagnostic test is 96.30%, and the specificity is 94.74%. A p-value of 0.985997 means that the reported finding reaches a statistical significance, which is enough to support our hypothesis. This can be concluded from the results of ANN, showing the potential of this model to successfully classify the image patches into two sets, namely the ones covered by oil spills and the oil spill-free ones. The research work is a great contribution to the development of the area of environmental monitoring through the machine learning methods used for quick and appropriate detection of environmental hazards.

关键词： Sensitivity Oils Computational modeling Disasters artificial neural networks Machine learning Predictive models

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6G and AI: Viewpoints, Processes, and Technologies

6G and AI: Viewpoints, Processes, and Technologies

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S.A. Shoyusupov B. Lanitha Shanky Goyal Vishal Goyal Saif O. Husain Praveen Sharma Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Artificial Intelligence and Data Science Karpagam Academy of Higher Education Coimbatore Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Electronics and Communication Engineering GLA University Mathura India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The rapid improvement of remote systems is being driven by the growing use of fake intelligence. The shift from "interdependent items" to "associated insights" is expected to be fundamentally altered by sixth-generation (6G) distant innovation. However, because of their vast analytics foundations and deep neural networks, state-of-the-art AI frameworks entirely rely on computer and interactions. As a result, excessive inactivity, excessive energy use, organizational obstruction, and protection leakage occur throughout the planning and deduction phases. Edge AI might be a game-changer for 6G, improving the practicality, efficacy, security, and validity of 6G systems in general, with its steady integration of sensing, communication, processing, and intelligence. It does this by coordinating modelling with duction and teaching skills at the organise edge. In this research, we will outline our methodology for robust and flexible uncontrolled neural network models and coordinated Bluetooth connections in edge AI contexts. A comprehensive end-to-end frame plan for supporting edge AI, service-driven use efficiency tactics, and contemporary concepts in remote organize building will all be safeguarded. In addition, uniformity, phases for software and hardware components, state-of-the-art AI applications, and real-world examples are presented to support industrialization and subsequently deployment.

关键词： 6G mobile communication Technological innovation Edge AI Software Telecommunication computing Sensors Resource management Reliability artificial intelligence Protection

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Feature Selection Based on dataset Variance Optimization Using Hybrid Sine Cosine – Firehawk Algorithm (Hscfha)

SSRN

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SSRN 2023年

作者： Moosavi, Syed Kumayl Raza Saadat, Ahsan Abaid, Zainab Ni, Wei Li, Kai Guizani, Mohsen School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Islamabad Pakistan Data61 Commonwealth Scientific and Industrial Research Organisation Sydney Australia CISTER Research Centre Portugal Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Feature selection is a crucial step in the preprocessing of data for machine learning (ML) models, as it involves selecting a subset of relevant features to improve the model's accuracy and reduce the risk of overfitting. Metaheuristic-based wrapper methods are a type of feature selection technique that utilizes the predictive performance of a learning algorithm to create a reduced feature set. The traditional methodology involves using K-Nearest Neighbour (KNN) based accuracy maximization as the cost function, which is seen to produce sub-optimal solutions in large sample spaces and is relatively resource intensive. In this work, we propose a novel metaheuristic algorithm, termed the Hybrid Sine Cosine Firehawk Algorithm. The feature selection technique uses this hybrid algorithm to eliminate insignificant and redundant features by including the minimization of dataset variance in the cost function. Furthermore, the proposed hybrid algorithm uses the best features of the combined algorithms to improve the exploration ability. The proposed technique is tested on 22 University of California Irvine datasets containing low, medium and high dimensional datasets and compared to the traditional KNN-based approach. Moreover, the technique is also compared with other state-of-the-art metaheuristic techniques, namely Particle Swarm Optimizer, Grey Wolf Optimizer, Whale Optimization Algorithm, Hybrid Ant Colony Optimizer and Improved Binary Bat Algorithm. The results show significant improvements over previous techniques in terms of minimal loss in essential data while reducing the size of the raw data in considerably less time as well as a well-balanced confusion matrix. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications

arXiv

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arXiv 2023年

作者： Lin, Jiatai Han, Guoqiang Xu, Xuemiao Liang, Changhong Wong, Tien-Tsin Chen, C.L. Philip Liu, Zaiyi Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Class activation mapping (CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation (WSSS) and object localization (WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores (forward information), gradients (backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data. © 2023, CC BY.

关键词： Learning systems

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House price prediction and regression evaluation metrics score using Machine Learning and Deep Learning model

引用

AIP Conference Proceedings 2024年第1期3131卷

作者： Gagan Gowda Mandya Venkatesh Bharath Rajashekar Ashwini Kodipalli Trupthi Rao Kumaraswamy Siddagangaiah Department of Computer Science and Engineering Global Academy of Technology Bangalore Karnataka India Department of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India

Housing price prediction and increasing research-based studies using Machine Learning and Deep Learning are popular. Some researchers have not considered all-inclusive information that affects the price prediction. Motive of this paper is to show variance score, measure of errors and predicting the feature price of the house performed by two different models. Here we are using Machine learning (ML) and Deep Learning models to calculate the metric. By using the Keras-ANN model performs both variance score and predicting new price of the house. It inputs a single data of a house in Boston city and predicts the new price based on the features of house. Model Evaluation Measures is used to compare the performance of both models. The result describes the measure of variability in evaluation scores.

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Research Artifacts in Software engineering Publications: Status and Trends

arXiv

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arXiv 2024年

作者： Liu, Mugeng Huang, Xiaolong He, Wei Xie, Yibing Zhang, Jie M. Jing, Xiang Chen, Zhenpeng Ma, Yun Ministry of Education School of Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China Department of Informatics King’s College London London United Kingdom National Key Laboratory of Data Space Technology and System Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Artificial Intelligence Peking University Beijing China

The Software engineering (SE) community has been embracing the open science policy and encouraging researchers to disclose artifacts in their publications. However, the status and trends of artifact practice and quality remain unclear, lacking insights on further improvement. In this paper, we present an empirical study to characterize the research artifacts in SE publications. Specifically, we manually collect 1,487 artifacts from all 2,196 papers published in top-tier SE conferences (ASE, FSE, ICSE, and ISSTA) from 2017 to 2022. We investigate the common practices (e.g., URL location and format, storage websites), maintenance activities (e.g., last update time and URL validity), popularity (e.g., the number of stars on GitHub and characteristics), and quality (e.g., documentation and code smell) of these artifacts. Based on our analysis, we reveal a rise in publications providing artifacts. The usage of Zenodo for sharing artifacts has significantly increased. However, artifacts stored in GitHub tend to receive few stars, indicating a limited influence on real-world SE applications. We summarize the results and provide suggestions to different stakeholders in conjunction with current guidelines. © 2024, CC BY.

关键词： Software engineering

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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing Control

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： P. Sathyanathan K. Balachander Pramod Pandey V. Prasanna Srinivasan T. R. GaneshBabu M. Muthulekshmi Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes.

关键词： Pediatrics Machine learning algorithms Surveillance Medical services Transforms Nearest neighbor methods Real-time systems Internet of Things Wearable sensors Asthma

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Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations

arXiv

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arXiv 2025年

作者： Aghaei, Raha Kiaei, Ali A. Boush, Mahnaz Vahidi, Javad Zavvar, Mohammad Barzegar, Zeynab Rofoosheh, Mahan School of Mathematics and Computer Science Iran University of Science & Technology Tehran Iran Department of Artificial Intelligence in Medicine Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran Cellular and Molecular Biology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Computer engineering group Alborz Vocational Technical University Alborz Iran

Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation in marketing management. In this paper, we explore the transformative potential of LLMs along with the current applications, future directions, and strategic recommendations for marketers. In particular, we focus on LLMs major business drivers such as personalization, real-time-interactive customer insights, and content automation, and how they enable customers and business outcomes. For instance, the ethical aspects of AI with respect to data privacy, transparency, and mitigation of bias are also covered, with the goal of promoting responsible use of the technology. Through best practices and the use of new technologies businesses can tap into the LLM potential, which help growth and stay one step ahead in the turmoil of digital marketing. This article is designed to give marketers the necessary guidance by using best industry practices to integrate these powerful LLMs into their marketing strategy and innovation roadmap without compromising on the ethos of their brand. Copyright © 2025, The Authors. All rights reserved.

关键词： Sales

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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Telecommunications Conference (ITC-Egypt), International

作者： Abdelhameed Ibrahim Christos Gatzoulis Marwa M. Eid Faris H. Rizk Laith Abualigah El-Sayed M. El-kenawy Faculty of Engineering Design and Information & Communications Technology (EDICT) School of ICT Bahrain Polytechnic Isa Town Bahrain Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform; the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance.

关键词： Machine learning algorithms Transforms Predictive models Nearest neighbor methods Prediction algorithms Boosting data models

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