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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Ghezaiel Hammouda, Nadia Toujani, Radhia College of Computer Science and Software Engineering University of Hail Hail Saudi Arabia ESTMOD Department Higher Institute of management University of Tunis Tunisia

Machine learning techniques, particularly fuzzy Support Vector Machines (fuzzy SVM), have demonstrated effectiveness in sentiment classification within social networks. However, the evolving complexity of social networks content and the necessity to handle diverse opinions prompt ongoing advancements in sentiment analysis methodologies. In this study, we propose an innovative approach by integrating a Convolutional Neural Network (CNN) model with fuzzy SVM. Our key contribution lies in the fusion of these two methodologies, capitalizing on the strengths of fuzzy SVM for opinion mining and CNN for extracting intricate features. Additionally, we extend this method to enable multi-class opinion classification, allowing for a finer understanding of nuances in expressed sentiments. Through evaluation on benchmark datasets, we highlight a notable improvement in performance compared to baseline methods. The results underscore the enhanced generalization capabilities and increased accuracy in opinion classification offered by our hybrid model, rendering it a promising asset for decision-making applications based on multi-opinion analysis. © 2024 The Authors.

关键词： Convolutional neural networks

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Unveiling Interpretability: Analyzing Transfer Learning in Deep Learning Models for Traffic Sign Recognition

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SN computer science 2024年第6期5卷 682页

作者： Waziry, Sadaf Rasheed, Jawad Ghabban, Fahad Mahmoud Alsubai, Shtwai Elkiran, Harun Alqahtani, Abdullah Department of Software Engineering Istanbul Aydin University Isanbul 34295 Turkey Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul 34303 Turkey Faculty of Computer Science and Engineering Taibah University Medina Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al Kharj 11942 Saudi Arabia

Since the advent of automobiles and driver assistance technologies, traffic sign recognition has been of the utmost importance for Industry 4.0. In the driving system, good data pre-processing is critical. For such objectives, sophisticated transformations or fundamentally computational image processing approaches are out of the question. Convolutional Neural Networks (CNN) have been used to perform more object identification challenges, thus, improving most computer vision applications, both existing and new, because of their excellent recognition rate and rapid execution. This study introduces a method for recognizing traffic signs by utilizing a CNN-based model and the transfer learning concept. The TensorFlow library is used for training the underlying neural network model. The offered approach makes use of the German Traffic Sign Recognition Benchmark (GTSRB) and images from the Traffic Sign Images from Turkey (TSIT) databases. These datasets are dependable and vibrant, and they have been used to train many algorithms. Furthermore, after training the model, the proposed scheme acquired a testing accuracy is 99.44%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CNN German traffic sign Image processing Transfer learning

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IEARACDA: Predicting circRNA-disease associations based on information enhancement and arctangent rank approximation

IEARACDA: Predicting circRNA-disease associations based on i...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Song, Zheqi Liu, Tiyao Wu, Wenhao Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Circular RNAs (circRNAs) are non-coding RNA molecules that play a significant role in cell regulation and disease occurrence. In recent years, the use of computational methods to predict circRNAs associated with diseases has become a research focus. However, these methods are often constrained by the noise interference and an insufficient exploration of the underlying information in known associations. Therefore, this paper proposes an information enhancement and arctangent rank approximation based method to predict potential circRNA-disease associations (IEARACDA). Firstly, information enhancement methods are used to make circRNA and disease similarity information more complete and to reduce the sparsity of known association. Subsequently, the arctangent rank approximation method is employed to mitigate the potential bias issues that may arise from the nuclear norm, and optimization is carried out using the inexact augmented Lagrange multiplier (IALM) method. Finally, potential associations are predicted through heterogeneous graph inference method. The experimental results demonstrate that IEARACDA exhibits superior performance compared to existing methods. Furthermore, case studies for hepatocellular carcinoma and lung cancer provide additional confirmation of the accuracy and practical significance of the model predictions. © 2024 IEEE.

关键词： Lung cancer

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Time Series Data Cleaning under Expressive Constraints on Both Rows and Columns 40

Time Series Data Cleaning under Expressive Constraints on Bo...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Genglong Wang, Hongzhi Wang, Chen Song, Yichen School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Laboratory for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy. © 2024 IEEE.

关键词： Time series

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Predictive Classification Model of Crop Yield Data Using Artificial Neural Network 5

Predictive Classification Model of Crop Yield Data Using Art...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kanagaraj, R. Elakiya, E. Kumaresan, A. Selvakumar, J. Suganya, J. Shruthi, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Mailam Engineering College Mailam India Bosch Global Software Technologies Coimbatore India

ISBN: (纸本)9798350321425

Agriculture is under tremendous pressure to produce more productivity with fewer resources as a result of the expanding global population. Decision-making along the entire supply chain needs to be improved considering competing economic, environmental, and societal demands. Data from satellites, drones, sensors, and other sources must be processed in order to make informed judgments. However, sophisticated machine learning and data analytics methods must be used due to the complexity and volume of the data. This initiative covers crucial duties in yield prediction and precision agriculture. Management zones are areas of a field that have high inter-regional variability and low intra-regional variability, dividing the field into uniform zones for which agronomic practices should be modified independently. For fertilizer optimization and post-harvest logistics, yield prediction is crucial. In order to make up for the nutrients that have been taken out of the soil, fertilizer type and quantity must be adjusted accordingly. The yield data is also essential for enhancing harvesting, shipping, storage, and sales. So, utilizing web scraping and convolution neural networks, the proposed model has been forecast yields from input satellite photos. The result of investigations is illustrated that the artificial neural network algorithm is used to predicting the agricultural data rules and powerfully decrease the error rate from crop yield data. © 2023 IEEE.

关键词： Neural networks

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Collaborative Filtering-Based Recommender System for Ethiopian Tourism Sites 11th

Collaborative Filtering-Based Recommender System for Ethiopi...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Assefa, Yibeltal Meshesha, Million Nibret, Shegaw Gedif, Birku Software Engineering Wollo University Kombolcha Ethiopia Computer Science Addis Ababa University Addis Ababa Ethiopia Information Science NGO Bahir Dar Ethiopia Information Science University of Gondar Gondar Ethiopia

ISBN: (纸本)9783031641503

Tourism is a significant source of income for countries and regions, and with the advancement of technology, everything is now interconnected, generating massive amounts of data. Recommender systems are one way to utilize this generated big data. However, currently, Ethiopian Tourism Institutions do not have a system to manage tourist sites, analyze customer preferences and behavior, or filter based on their interests. Therefore, the purpose of this research is to develop a collaborative-based recommender system for Ethiopian tourism sites. This study applies both user-based collaborative filtering using a cosine similarity algorithm and model-based collaborative filtering using singular value decomposition (SVD), nonnegative matrix factorization(NMF), and K-nearest neighbors (KNNBasic) to analyze the data collected from the Amhara tourism office, which describes the Ethiopian tourism site. After the analysis, the results show that cosine similarity has the lowest root mean square error (RMSE) score. The methodology followed in this research is the design science approach, and the artifact recommender system is developed using the flask framework. After demonstrating the recommender system, domain experts evaluate it, achieving a promising result of 84.2%. The scarcity of data is a significant challenge, and thus, limited attributes for clustering are selected. Using more attributes to make the similarity of tourists will improve the development of the tourist site recommender system, which is left for future researchers to consider. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Collaborative filtering

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A computer Vision-Based Vehicle Detection System Leveraging Deep Learning 1

A Computer Vision-Based Vehicle Detection System Leveraging ...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Khan, Saadullah Vohra, Sanayna Siddique, Sultan Abdullah Abro, Abdul Ahad Ebrahim, Mansoor Iqra University Main Campus Department of Software Engineering Karachi Pakistan Iqra University Main Campus Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

This study uses advanced computer vision (CV) and deep learning (DL) techniques to present a combined approach for extensive vehicle detection and number plate recognition. Improving the effectiveness and precision of traffic surveillance and administration systems is the main goal. The two primary parts of the suggested system are identification of number plates recognition and vehicle detection. The vehicle detection module uses rigid images or video streams to recognize along with monitor vehicles in real-time using innovative DL algorithms. Such limitations of system reliability are overcome through the application of CNN and object detection models in an integrated computer vision system that allows for reliable performance under a variety of environmental conditions including unfavorable lighting and weather situations. The goal of an identification plate recognition feature is to read and extract alphanumeric characters from license plates that have been detected. To precisely identify and interpret license plate data, this stage combines DL models with the use of optical character recognition (OCR) techniques. The system can adapt to different geographical locations by supporting changes in plate layouts, fonts, and positions. By combining these two modules, a complete intelligent transportation system solution is produced, offering useful information for applications such as congestion examination, safety, and assurance. Because of its modular architecture, the system is easily scalable and customizable, enabling it to be adjusted to various hardware configurations and surveillance scenarios. Using real-world scenarios and benchmark datasets, extensive experiments are conducted to assess the effectiveness of the proposed system. The outcomes show excellent efficiency and accuracy in tasks involving the detection of vehicles and number plate recognition. By promoting enhanced security, integrity, and general traffic management, the suggested integrated syste

关键词： Object detection

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System for Table Run Strategy in Peruvian Billiards with Real-Time Oject Detection using YOLOv8n 10

System for Table Run Strategy in Peruvian Billiards with Rea...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Silva, Italo Chavez, Raquel Castillo-Sequera, José Luis Wong, Lenis Peruvian University of Applied Sciences Department of Software Engineering Lima Peru University of Alcalá Department of Computer Science Alcalá de Henares Spain

ISBN: (纸本)9798350387353

The main challenge for billiard players, regardless of their skill level, is to develop a strategy that allows them to consecutively pocket balls in a single turn, commonly known as 'closing the table.' This strategy requires high precision and consistency, especially for less experienced players. This study integrates a YOLOv8n model, a shot suggestion algorithm, and a shot prediction algorithm to build a system that aids players in developing this 'table closing' strategy through visual guides indicating the shots to take and where to position the cue ball for the next shot. The proposed system follows these phases: (1) Selection of the video camera, (2) Selection of the object detection technique, (3) Development of the shot suggestion algorithm, (4) Development of the shot prediction algorithm, and (5) System construction. Phases (1) and (2) address the implementation of the YOLOv8n model, while phases (3) and (4) involve the development of algorithms for the 'table closing' strategy. To validate the system's effectiveness, an experiment with 24 players of different skill levels was conducted, showing that the system reduced the average number of shots needed to close a table to 22, compared to the 47-shot average of professional players. Beginners accepted 100% of the system's recommendations, while intermediate and professional players showed acceptance rates of 95.1% and 95.6%, respectively. A survey indicated high overall satisfaction, with a median response of 4.5 on a 5-point scale. The system also demonstrates significant competitive advantages over existing solutions by maintaining high detection and prediction accuracy while effectively functioning in real-world scenarios, leveraging the latest advances in computer vision to surpass older techniques in both effectiveness and efficiency. © 2024 IEEE.

关键词： computer vision

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Gliding over the Pareto Front with Uniform Designs 38

Gliding over the Pareto Front with Uniform Designs

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Xiaoyuan Li, Genghui Lin, Xi Zhang, Yichi Chen, Yifan Zhang, Qingfu Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University China Department of Statistics Indiana University Bloomingtom United States Department of Mathematics and Computer Science Hong Kong Baptist University Hong Kong

Multiobjective optimization (MOO) plays a critical role in various real-world domains. A major challenge therein is generating K uniform Pareto-optimal solutions to approximate the entire Pareto front. To address this issue, this paper firstly introduces fill distance to evaluate the K design points, which provides a quantitative metric for the representativeness of the design. However, directly specifying the optimal design that minimizes the fill distance is nearly intractable due to the involved nested min − max − min problem structure. To address this, we propose a surrogate "max-packing" design for the fill distance design, which is easier to optimize and leads to a rate-optimal design with a fill distance at most 4× the minimum value. Extensive experiments on synthetic and real-world benchmarks demonstrate that our proposed paradigm efficiently produces high-quality, representative solutions and outperforms baseline MOO methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Predictive Modeling of Students' Stress Levels Using Machine Learning Algorithm 4

Predictive Modeling of Students' Stress Levels Using Machine...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Ali, Hassan Amin, M. Hamza Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350353266

The increasing prevalence of stress among university students has raised concern about its impact on academic performance and overall well-being. This conference paper explores the applications of machine learning algorithms to predict and analyze the student's stress level. A comprehensive dataset encompassing various stress-related factors, including academic workload, social interactions, and lifestyle, was collected from Kaggle which consists of 1100 instances and 19 attributes. Our methodology places a premium on human connection, integration of qualitative data to refine the accuracy of stress predictions. By seamlessly merging the capability of machine learning with an empathetic understanding of the human experience, our approach strives to pave the way for a more holistic and personalized educational ecosystem. The insights derived from this study aspire to guide educators, administrators, and policy makers in crafting nurturing environments that empower students to excel both academically and emotionally. Embarking on a journey to unravel the myriad factors influencing students' stress, we consider academic pressures, social dynamics, and personal experiences. Harnessing machine learning algorithms such as decision trees, neural networks, we navigate expansive student populations. Acknowledging the nuanced nature of stress, our study integrates a compassionate approach with cutting-edge technology. The study employs state-of-the-art machine learning techniques using various classification algorithms, to build predictive models for assessing stress levels out of which random forest gives 97.45% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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