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

作者： Kumar, Praveen Khera, Richa Grover, Ashish Sharma, Kamlesh Department of Computer Engineering Astana IT University Astana Kazakhstan Electricals and Electronics Engineering School of Engineering and Technology MRIIRS India

ISBN: (纸本)9798350374865

Speech emotion recognition is a pivotal aspect of human-computer interaction and affective computing. This research delves into the application of the Multi-Layer Perceptron (MLP) Classifier for emotion recognition within speech, focusing on the widely used CREMA-D dataset. Our investigation covers meticulous pre-processing methods, feature extraction techniques, and model configuration. Raw audio data undergoes preprocessing, with acoustic features like Mel-frequency cepstral coefficients (MFCCs) and pitch-related parameters extracted. The MLP Classifier, a feedforward neural network, is employed to categorize emotions in speech samples. Our results reveal competitive performance in recognizing a spectrum of emotions, including happiness, sadness, anger, and neutrality within the CREMA-D dataset. Our study examines the influence of di-verse hyperparameters, training strategies, and model architectures on emotion recognition accuracy. Cross-validation ensures the reliability of our findings, emphasizing the relevance of dataset selection and the potential for advancing speech emotion recognition research. This research aids in enhancing human-computer interaction systems, sentiment analysis, and emotional well-being monitoring applications, while also contributing to the discourse on optimizing this technology. © 2024 IEEE.

关键词： Emotion Recognition

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Online Convex Optimization with Switching Cost and Delayed Gradients

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Performance Evaluation Review 2024年第4期51卷 22-23页

作者： Senapati, Spandan Vaze, Rahul Department of Computer Science & Engineering Indian Institute of Technology Kanpur India School of Technology and Computer Science Tata Institute of Fundamental Research India

We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions fτ, τ 16(Lµ+5) for the quadratic switching cost, and also show the bound to be order-wise tight in terms of L, µ. In addition, we show that the competitive ratio of any online algorithm is at least max{Ω(L), Ω(pLµ )} when the switching cost is quadratic. For the linear switching cost, the competitive ratio of the OMGD algorithm is shown to depend on both the path length and the squared path length of the problem instance, in addition to L, µ, and is shown to be order-wise, the best competitive ratio any online algorithm can achieve. Copyright is held by author/owner(s).

关键词： Convex optimization

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A Novel Fuzzy Logic Framework for Model Reliability Evaluation in Permeability Prediction Using GPR 16

A Novel Fuzzy Logic Framework for Model Reliability Evaluati...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Lawal, Ahmad Yang, Yingjie Baisa, Nathanael L. He, Hongmei School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Computer Science and Informatics University of Nairobi Leicester United Kingdom School of Science Engineering and Environment University of Salford Manchester United Kingdom

ISBN: (纸本)9798331505264

Permeability is a critical parameter in reservoir engineering and hydrocarbon extraction, yet its prediction remains challenging due to inherent uncertainties in subsurface data. While Gaussian Process Regression (GPR) has proven effective in predicting permeability with associated uncertainties, it generates multiple metrics that are difficult to interpret, particularly in high-stakes environments. This study proposes a novel approach using fuzzy logic to compute a single, comprehensive metric that accounts for model reliability. Our method incorporates human input and reasoning into the modelling process, enhancing the model's interpretability and its ability to handle uncertainty. Additionally, we introduce a new visualization technique to simplify the understanding of fuzzy logic outputs for non-technical stakeholders. The proposed methodology demonstrates that GPR achieves a higher reliability level (0.89) compared to traditional machine learning counterparts, which are typically neutral to uncertainties. By providing a comprehensive, transparent, and easily interpretable measure of model reliability, this approach significantly aids in making more informed and responsible decisions in reservoir management. Our framework represents a crucial step towards improving the practical application of advanced machine learning techniques in the oil and gas industry, potentially extending to other fields where uncertainty quantification is vital. © 2024 IEEE.

关键词： Gas industry

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Enhanced Lightweight Facial Emotion Recognition Systems for Visually Impaired People 12

Enhanced Lightweight Facial Emotion Recognition Systems for ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Shehada, Dina Turky, Ayad Rabie, Tamer Hussain, Abir University of Dubai College of Engineering and IT Dubai United Arab Emirates University of Sharjah College of Engineering Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah College of Computing & Informatics Department of Computer Science Sharjah United Arab Emirates University of Sharjah College of Computing & Informatics Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350391398

Facial expressions are a vital component of human communication, conveying emotional information that enhances the social experience. However, for individuals with visual impairments, perceiving and interpreting facial expressions can be challenging, hindering their ability to engage fully in social interactions. This paper presents an enhanced facial emotion recognition system specifically designed to aid visually impaired individuals. Two enhanced approaches for facial emotion recognition systems are proposed. The first approach consolidates negative emotions (anger, fear, sadness, disgust) into a single class, while the second approach incorporates diverse training datasets from real-world environments. The negative emotion consolidation approach achieved 90.9% and 94.7% accuracy on the FER2013 and CK+ datasets, respectively. While, the diverse dataset integration approach achieved 84.9% and 70.6% accuracy on FER2013 and CK+ datasets, respectively. This is a significant improvement when benchmarked with other approaches available in the literature. The feasibility of the enhanced proposed systems as assistive technology for the visually impaired indicated its ability to be applied in real-world applications. © 2024 IEEE.

关键词： Assistive technology

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AD-OCT: An Early Alzheimer Disease Detection Method via OCT Images using Advanced Feature Extraction and Clustering Techniques 2

AD-OCT: An Early Alzheimer Disease Detection Method via OCT ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mahendran, Rakesh Kumar Kalaiyarasi, B. Krithik Karan, K. Thirunavukkarasu, Usharani Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of BioMedical Engineering Chennai India Saveetha University Saveetha School of Engineering SIMATS School of Engineering Department of BioMedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331534950

for efficient management and treatment whereas the researchers focus on the various approaches including biomarkers, imaging and techniques like Magnetic resonance imaging (MRI), Optical Coherence Tomography (OCT) and advanced Deep learning (DL) algorithms to identify the early signs of the disease before significant cognitive decline occurs. DL holds noteworthy promise in proceeding Alzheimer's research and patient care. Conventional models have traditionally faced challenges in capturing the intricate details and subtle features within OCT images that are crucial for early detection of AD, leading to superior performance. Imbalanced datasets, where positive cases of Alzheimer's are far fewer than negatives, often bias models toward the majority class, which hampers accurate detection. Moreover, current models often struggle to accurately pinpoint pathological structures within OCT images, which are essential for dependable diagnosis, presenting a significant hurdle and furthermore, limiting the generalization of models to unseen data and affecting their clinical utility. By overcoming these issues, Authors have approached an Advanced Feature Extraction and Integration Techniques by this method Authors can perceive the AD earlier. Initially Authors provide the OCT images in the Integrated Residual Enhanced Attention Module (IREAM) which combines the strengths of residual learning and attention mechanisms and the extracted features are now refined and clustered using the Cluster Feature refinement (CFR) and Clustered Feature Fusion (C2F) which improves the quality and the extracted features are merged to get a unified set. Finally, Author's model is compared with the existing model and Authors calculate the balance accuracy, Recall, AUC, Dice coefficient and F1-Score of the proposed model. © 2024 IEEE.

关键词： Optical coherence tomography

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Fruit Quality Monitoring System using CNN 1

Fruit Quality Monitoring System using CNN

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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A proposed brain tumor detection algorithm using Multi wavelet Transform (MWT)

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Materials Today: Proceedings 2022年 65卷 2731-2737页

作者： Mahmoud Al-Jawher, Waleed A. Awad, Sarah H. Uruk University Department of computer engineering Iraq Iraqi Commission for computers and informatics Iraq

Magnetic resonance imaging)MRI) is a technological development in the medical field. It is used to give images of the human body with high accuracy and good quality, facilitating the process of identifying and classifying diseases in the human body. One of the diseases that are diagnosed using MRI is a brain tumor, MRI which helps in the early diagnosis of the tumor, and helps the doctor to diagnose and identify the tumor and thus make the fastest medical decision. This paper used to identification and extraction of brain tumors from magnetic resonance images based on Multi Wavelet Transform (MWT) and image processing techniques. This project Multi Wavelet Transform (MWT) used in preprocessing stage to enhance the input image and denoising. Thresholding based is used for segmentation methods, classification of brain tumor is done using Statistical Classification Methods to classify the input MRI of brain into normal or abnormal. Firstly the location of tumor was presented & next its area computed. © 2022

关键词： Magnetic resonance imaging

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Comparative Analysis of Machine Learning Algorithm;for Conversion Predictors of Clinically Isolated Syndrome (CIS) to Multiple Sclerosis (MS) 4

Comparative Analysis of Machine Learning Algorithm;for Conve...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Mendanha, Serena Gudibandi, Keerthi Reddy Hajarathaiah, Koduru Gudibandi, Bharat Reddy Enduri, Murali Krishna School of Computer Science and Engineering VIT-AP University Amaravati India School of Electronics Engineering VIT-AP University Amaravati India Srm University Department of Computer Science and Engineering AP India

ISBN: (纸本)9798331523923

The central nervous system is impacted by multiple sclerosis (MS), a chronic neurological condition that causes significant cognitive and physical deficits. Better disease management and prompt intervention depend on early and precise detection. Effective diagnosis of MS is hampered by a number of factors, such as small datasets, significant clinical presentation variability, difficult feature selection, model generalization problems, and integration of multimodal data such as magnetic resonance imaging and genetic markers. Several machine learning (ML) models are assessed in this study in order to predict the development of clinically isolated syndrome (CIS) to multiple sclerosis. Using clinical and magnetic resonance data from patients with CIS at risk of developing MS, we evaluate the effectiveness of support vector machines (SVM), K nearest neighbor (KNN), decision trees (DT), random forests (RF), logistic regression (LR), Gaussian naive Bayes (Gaussian NB) and XGBoost (XG). Evaluation is performed using performance criteria including F1 score, recall, precision, and precision. According to our research, Random Forest has the best prediction accuracy, which makes it a potentially useful tool for helping doctors diagnose and treat MS patients early. Notwithstanding these developments, issues like model interpretability and data scarcity still exist. In order to improve diagnosis precision, future research will concentrate on enhancing these models by integrating deep learning methods, genetic markers,, and more advanced imaging modalities. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

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Artificial Intelligence in Agriculture 2024年第4期14卷 99-114页

作者： Mohammad Amin Razavi A.Pouyan Nejadhashemi Babak Majidi Hoda S.Razavi Josue Kpodo Rasu Eeswaran Ignacio Ciampitti P.V.Vara Prasad School of Electrical and Computer Engineering University of TehranTehranIran Department of Biosystems and Agricuttural Engineering Michigan State UniversityMIUSA Department of Computer Engineering Khatam UniversityTehranIran Department of Computer Science and Engineering Michigan State UniversityEast LansingMIUSA Department of Agronomy Faculty of AgricultureUniversity of JafinaKilinochchSri Lanka Department of Agronomy Kansas State UniversityManhattanKSUA Feed the Future Sustainable Intensification Innovation Lab Kansas State UniversityManhattanKSUA

In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these features influence crop yields by utilizing remotely sensed *** methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in *** optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model *** top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive *** results demonstrated that XGBoost and CatBoost perform better than the other *** introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural *** achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in *** approach distinguishes itself by creating a flexible model applicable to various crops *** integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance *** findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.

关键词： Crop yield prediction Variational auto encoder Pattern recognition on spatiotemporal and physiographical variables Synthetic tabular data generation Ensemble learning

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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