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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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A Review on the applications of Machine learning and Deep learning Techniques for Skin Cancer Detection 4

A Review on the Applications of Machine Learning and Deep Le...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine learning (ML) and Deep learning (DL) models. this review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. this article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

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An Ocean Wave Power Plant Buoy System as learning Media 4

An Ocean Wave Power Plant Buoy System as Learning Media

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Febry, Dea Muthia Haryani, Fairusy Fitria Rahmawati, Karisma Ardiyana, Sasna Fauzan, Ahmad Zahroh, Shofi Hikmatuz Sebelas Maret Univ Phys Educ Surakarta Indonesia Sebelas Maret Univ Occupat Hlth & Safety Surakarta Indonesia

ISBN: (纸本)9798350354140;9798350354133

this study aims to develop a prototype of an ocean wave power plant buoy system as an educational tool to introduce the concept of renewable energy to students. the authors use a v-model, which includes design stages, implementation, calibration test, and validation. the calibration focuses on observing the influence of wave amplitude on electrical current output, while the validation tests by experts use instrument item validity tests. Results from the calibration test indicated that larger wave amplitudes produced higher electrical currents, with significant precision and standard deviation calculations confirming the reliability of the measurements. the validation test results categorised the learning media as very good based on presentation, visual, and design aspects, with an overall score indicating high suitability for educational use. Educating future generations about renewable energy sources like ocean waves is crucial with the increasing global demand for sustainable energy solutions. Existing educational tools often lack hands-on experience, limiting students' understanding of complex concepts. this prototype aims to fill that gap by providing a tangible learning experience demonstrating ocean wave energy conversion into electricity. By integrating STEM principles into education through practical applications, because it involves a combination of science, technology, engineering, and mathematics, students can better appreciate renewable energy technologies and their potential impact on global energy sustainability.

关键词： learning media ocean wave power plant renewable energy STEM

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Minor Surface Cracks Detection using SCD11 Convolutional Neural Network 4

Minor Surface Cracks Detection using SCD11 Convolutional Neu...

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Hussain, Adil Qureshi, Kashif Naseer Zaman, Faizan Aslam, Ayesha Tariq Changan Univ Sch Elect & Control Engn Xian Peoples R China Univ Limerick Dept Elect & Comp Engn Limerick Ireland

ISBN: (纸本)9798350354140;9798350354133

the manual detection of road cracks is a timeconsuming process. On the other hand, solutions that are based on deep learning are both speedy and accurate. Recently, several different Convolutional Neural Networks (CNN) based on deep learning have been proposed. However, the performance of the CNN models has varied. the major challenge is the computational resources required to train a pretrained CNN model;however, a lightweight CNN is more suitable for better training efficiency. In this study work, the SCD11 CNN model is implemented and compared with the pretrained CNN models, including Inception V2, VGG19, and Xception CNN. the models are trained and tested using the public dataset i.e., the Surface Cracks Dataset. the dataset is divided into training, validation and test sets. the SCD11 CNN along with the pre-trained CNN models are trained and validated and then tested using the splitting of the public dataset. Furthermore, the model's performance evaluation is performed by using a private dataset. the results show that the SCD11 CNN performs better than the pre-trained CNN models for both the public and private datasets.

关键词： Road Cracks SCD11 CNN Deep learning Cracks Detection Minor Cracks.

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Comparative analysis of traffic flow prediction based on XGBoost before and after optimization 4

Comparative analysis of traffic flow prediction based on XGB...

引用

4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Zheng, Yuqi Ye, Wei Yin, Ronghui Yin, Yexiang Dai, Xiaomin Engineering Technology and Transportation Industry in Arid Desert Areas Xinjiang Urumqi830001 China Xinjiang University School of Traffic and Transportation Engineering Xinjiang Urumqi830017 China Xinjiang Jiaotou Construction Management Co. Ltd. Xinjiang Urumqi830000 China Xinjiang Key Laboratory of Green Construction and Maintenance of Transportation Infrastructure and Intelligent Traffic Control Urumqi830017 China

ISBN: (纸本)9798331528973

Due to the imbalance between the supply side and the demand side, the continuous urbanization process has challenged road traffic transportation, and improving the efficiency of road traffic transportation has become one of the challenges facing traffic engineering in the new era, in which the improvement of algorithmic prediction ability in machine learning has become one of the core of the solution. this study analyzes the accuracy and time benefit of XGBoost algorithm SSA optimization before and after two traditional deep learning in deep learning class models and simulates the fitting of time series feature data in the same space. From the simulation results, the models built in this study before and after optimization and the two traditional deep learning are within the error tolerance, and we believe that the prediction results using the optimized SSA-XGBoost are better for our dataset. © 2024 IEEE.

关键词： Prediction models

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optimization Design of MOSFET Controlled Switching Power Supply 4

Optimization Design of MOSFET Controlled Switching Power Sup...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Hua, Shen Xiangyang, Zhu State Grid Shanghai Electric Power Company Marketing Service Center Shanghai China State Grid Qinghai Electric Power Company Haidong Power Supply Company Haidong China

ISBN: (纸本)9798350359589

this paper optimizes the design of MOSFET controlled switching power supply and improves the instantaneous drop phenomenon of the front stage voltage circuit. MOSFET tubes are often used in power control circuits with high current and delayed switching on. However, at the moment of MOSFET switching on, there may be a momentary drop in the front voltage circuit. Shunt capacitors between the G and S poles of MOSFET tubes, analyze the impact of circuit improvement on the characteristics of MOSFET controlled switching power supply, and optimizes the power control circuit of MOSFET tubes, providing improvement solutions for the application of MOSFET tubes in power control circuits. © 2024 IEEE.

关键词： capacitance MOSFET transistor switching power supply voltage

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ON thE CONVERGENCE OF SINGLE-TIMESCALE MULTI-SEQUENCE STOCHASTIC APPROXIMATION WIthOUT FIXED POINT SMOOthNESS 49

ON THE CONVERGENCE OF SINGLE-TIMESCALE MULTI-SEQUENCE STOCHA...

引用

49th IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang, Yue Wu, Zhaoxian Ling, Qing Sun Yat Sen Univ Guangzhou Peoples R China Rensselaer Polytech Inst Troy NY USA

ISBN: (纸本)9798350344868;9798350344851

Stochastic approximation (SA) that involves multiple coupled sequences has diverse applications, including but not limited to bilevel optimization, meta learning and reinforcement learning. Unfortunately, the existing multi-timescale analysis of multiple-sequence SA (MSSA) implies a slow convergence rate, whereas the single-timescale analysis relies on assuming smoothness of fixed points. In this paper, we present tighter single-timescale analysis for MSSA, without assuming smoothness of fixed points. Our theoretical results demonstrate that, when all involved operators are strongly monotone, MSSA converges at a rate of (O) over tilde (K-1), where K is the total number of iterations. Under a weaker assumption that all involved operators are strongly monotone except for the main one, MSSA converges at a rate of O(K - (1) (2)). these theoretical results align with those established in single-sequence SA (SSSA). Applying these theoretical results to bilevel optimization offers relaxed assumptions and/or simpler algorithms with performance guarantees, as validated by numerical experiments.

关键词： Stochastic approximation (SA) convergence analysis bilevel optimization

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Sentiment Analysis of Translated BBC Hindi News Articles Using Machine learning: A Comparative Study of Classification algorithms 4

Sentiment Analysis of Translated BBC Hindi News Articles Usi...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Menon, Vishnu Achutha Sateesh Kumar, T.K. thomas, Juby thomas, Lijo P. Institute for Educational and Developmental Studies Department of Education Noida India Kristu Jayanti College Department of Commerce Bengaluru India Kristu Jayanti College Department of Media Studies Bengaluru India Kristu Jayanti College Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529635

News consumption in India has shifted significantly with the rise of Web 2.0 technologies, leading to the prominence of platforms like BBC Hindi News, which blends international credibility with regional relevance. the dataset comprises over 5000 articles from BBC Hindi (Kaggle), with sentiment distributed as 97% neutral, 2.1% positive, and 0.7% negative. Text preprocessing steps include cleaning, tokenization, stopword removal, and lemmatization. Features were extracted using TF-IDF vectorization, and two machine learning models-Logistic Regression and Random Forest-were employed for classification. Performance metrics such as accuracy, precision, recall, F1-score, and confusion matrices were used for evaluation. Logistic Regression achieved an accuracy of 97.38%, whereas Random Forest marginally outperformed with 97.99%. Random Forest also demonstrated better handling of minority classes, reflected in higher macro-average metrics compared to Logistic Regression. Despite the improvements, both models exhibited a strong bias toward the dominant neutral class, with difficulties in accurately classifying positive and negative sentiments. the study highlights the importance of addressing class imbalance through techniques like oversampling minority classes or adjusting class weights. Random Forest's performance suggests ensemble techniques are better suited for imbalanced datasets, though further optimization through hyperparameter tuning and feature refinement is recommended. © 2024 IEEE.

关键词： Contrastive learning

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Deep learning Based Localization Detection Technique for False Data Injection Attacks 4

Deep Learning Based Localization Detection Technique for Fal...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Chen, Haoran Wang, Jun Zhu, Yonghai Si, Yifei Henan University of Science and Technology Luoyang China Guangdong Nanhai Power Design & Engineering Co. Ltd Foshan China

ISBN: (纸本)9798350359589

False Data Injection Attacks (FDIA) significantly threaten the state estimation of cyber-physical systems (CPS) in the power grid, jeopardizing the secure operation of the electricity system. Currently, most FDIA detection methods are designed for direct current systems, and traditional DC FDIA detection methods cannot effectively detect alternating current FDIA with satisfactory performance. the existing AC FDIA detection methods typically employ time-series measurement data for direct detection, leading to suboptimal detection accuracy. Moreover, these methods focus on detecting the presence of attacks, failing to locate the positions affected by the attacks accurately. To address these issues, this study introduces a deep learning detection model based on Short-Time Fourier Transform (STFT) for the localization of stealthy FDIA in AC systems. Initially, low-quality data affected by FDIA is removed using a BDD detector. then, the filtered measurement data is transformed from the time domain to the time-frequency domain using STFT. Finally, the results of the STFT are input into a dual-channel convolutional neural network (DCNN) to extract time-frequency domain features for training and learning, aiming to locate the position of the FDIA attack. Experiments conducted in the IEEE 14 and IEEE 118 bus systems demonstrate that the detection method proposed in this article achieves an average AUC value that is respectively 0.0465, 0.0812 and 0.0255 higher than those of the STFT+BPNN, STFT+LSTM, and STFT+Bi-LSTM algorithms. Furthermore, under various noise intensities and attack strengths, it maintains accuracy, recall, precision, F1 score, and RACC at levels above 0.9036, 0.9425, 0.9588, 0.9506, and 0.899, respectively. © 2024 IEEE.

关键词： AC systems DCNN FDIA Locate STFT

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Research on Hierarchical Sparse Bayesian Channel Estimation Algorithm for OTFS Systems 4

Research on Hierarchical Sparse Bayesian Channel Estimation ...

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4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Tang, Xijuan Liu, Jia Jiang, Zhanjun School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9798350337181

In order to improve the accuracy of sparse Bayesian learning (SBL) channel estimation in the OTFS system, a hierarchical SBL channel estimation algorithm with adaptive adjusted hyperparameters is adopted. Firstly the algorithm transforms the channel estimation problem into a sparse signal recovery problem based on the sparsity of the delay-Doppler domain channel using the principle of compressed sensing. Secondly the sparse signal is modeled as a three-layer a priori SBL(T-SBL) model. Finally, a particle swarm optimization (PSO) algorithm with high global seeking capability is proposed to optimize the sensing matrix for the effect of the nonorthogonality of the sensing matrix on the algorithm. the simulation results show that the T -SBL algorithm improves the normalized mean square error (NMSE) performance of OTFS system in low SNR environment, and the BER performance of the system can be effectively enhanced when the T -SBL algorithm is used to estimate the channel after optimizing the sensing matrix. © 2023 IEEE.

关键词： learning algorithms

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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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