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Analysis of Isotonic Calibration on Gaussian Naïve Bayes Performance for Guitar Chords Classification

Analysis of Isotonic Calibration on Gaussian Naïve Bayes Pe...

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automation and Computation (AUTOCOM), international conference on

作者： Nipun Sharma Swati Sharma C Bhanuprakash Reddy Manisha A Bahuguna P Ankita T ECE Deptt Presidency University Bangalore India PSCS Presidency University Bangalore India SOE ECE Department Presidency University

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Music recognition and recommendation is an area of machine learning which is gaining popularity by the day amongst interdisciplinary researchers. Guitar chord classification problem has been making rounds in machine learning enthusiasts for music recognition and recommendation models. Fundamentals of guitar playing and learning often includes the knowledge about guitar strings and chords etc. Going one step deeper, the domains like music recognition and music recommendation systems are evolving at a very fast pace. In this paper, we have worked on Guitar chord classification problem which has been explored and experimented in more than one dimension for music recognition and recommendation models. The preprocessing of guitar chords audio into numerical data is computationally extensive. During the preprocessing stage the harmonics are generated in large numbers and the effect of higher order harmonics is explored in some previous works. In this paper we have evaluated the Brier score losses of the Gaussian Naïve bayes algorithm for guitar chord classification problem for two sets of harmonics count. Further, the isotonic calibration is done on the classification problem and the experimental results are obtained and presented in the results and discussion section. Isotonic calibration of Gaussian Naïve bayes shows great promise in the performance where it gives improved values of Brier score for both harmonics inclusion cases. The results are improved by up 5% even when the harmonics are included upto 9 th position.

关键词： automation Computational modeling Machine learning Harmonic analysis Calibration Bayes methods Numerical models Classification algorithms Recommender systems

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Major Depressive Disorder Detection using Dung Beetle Optimization based QGNN Classification

Major Depressive Disorder Detection using Dung Beetle Optimi...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： R.K Shalini V. Sujitha P. Parameswari Department of Information Technology Kumaraguru College of Technology Coimbatore TamilNadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Major Depressive Disorder (MDD) is prevalent and incapacitating mental health disorder which mandates correct and early diagnosis for appropriate intervention. Conventional diagnostic methods are based on subjective assessments, often resulting in discrepancies and delayed treatment. Into our research, we offer novel framework based upon Quantum Graph Neural Networks (QGNN) for automatic detection of MDD, which focuses on leveraging deep learning and optimization techniques to improve accuracy in the diagnostic process. The DLC integrates QGNN with Dynamic Bayesian Optimization (DBO) for hyperparameter fine-tuning, enhancing model's predictive accuracy. Further, enhanced data preprocessing methods are applied, like Hanning Window function, for filtering out noise and enhancing input features. Experimental evaluations on the MDD patient dataset demonstrate that proposed approach achieves an accuracy of 86.62%, higher than existing graph-based models. Quantum-Inspired Schemes for Graph Learning and Optimization Techniques for NMPDD Classification and Recognition Expanding datasets, optimizing deep learning architectures, and incorporating clinical data are potential avenues for future research for further improving model generalization and diagnostic reliability. These conclusions specify a QGNN-DBO has probability to be powerful, scalable, and efficient solution for automatic MDD detection, potentially enhancing the landscape of mental health diagnostics and treatment planning

关键词： Deep learning Accuracy Data preprocessing Mental health Predictive models Depression Graph neural networks Data models Telecommunication computing Optimization

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Stacking-Based Transfer Learning for Optimized Employee Management and Retention in HR Systems

Stacking-Based Transfer Learning for Optimized Employee Mana...

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automation and Computation (AUTOCOM), international conference on

作者： K. Krishna Rao R. Shakthivel B Mohan Krishna Bommireddy Poojitha Parimi Jayanth Sai Chitti Sai Lakshman Koneru Lakshmaiah Education Foundation KL Business School Vaddeswaram A.P India Department of Mathematics Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Background: For human resources departments trying to ensure organisational stability and lower turnover costs, employee retention is a major challenge. In terms of employee management, traditional approaches often lack the capacity to correctly mix insights from several sources, so limiting the predictive power of these approaches. Methodology: This work proposes a Stacking-Based Transfer Learning (TL) model to support bettering of employee retention policies. This model aggregates domain-specific data and pre-trained models to offer the most exact projections. The approach extracts and combines textual and numerical insights from HR data by means of a hierarchical stacking ensemble of deep learning models, which consists of BERT and ResNet. Tests carried out on a dataset of employee retention easily accessible to the general public reveal the efficiency of the model. Findings and Result: With 92.5%, the suggested method's accuracy exceeded those of other now in use methods including Random Forest (88.3%) and Support Vector Machine (85.7%). Moreover, by 17% over baseline models, the Stacking-based TL model lowers the prediction error linked with employee turnover rates. These findings indicate that HR analytics has great potential to include innovative learning strategies into order to increase employee satisfaction and retention rates respectively.

关键词： Support vector machines Accuracy Transfer learning Stacking Stability criteria Predictive models Data models Numerical models Computational efficiency Random forests

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Campus Placement Analysis: Predictive Insights with Machine Learning models

Campus Placement Analysis: Predictive Insights with Machine ...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Sathyendra Kumar V Ramya Mahitha Palle Prathyusha Budarapu Reddy Sudhan Reddy Pullaganti Siva Karthik Reddy Moyilla Department of CSE Annamacharya University Rajampet India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The system is designed using machine learning to predict the placement of students which is being trained on the historical features such as academic achievements, demographics and extracurricular activities. The model is designed to help educational institutions and students gauge placement likelihood by utilizing attributes like academic performance, field of study, work experience, and specialization. As a result, the system implements multiple data preprocessing methods, such as missing data imputation, and label encoding for the purpose of converting categorical variables to numerical format. Utilizing performance assessment metrics such as precision and recall, a decision tree classifier is trained to predict whether or not a student will be placed in a company. In addition, the system contains visualization tools which help to illustrate the relationship among factors such as specialization and salary and gives a holistic view of how placement is affected by the different attributes. Streamlit is used to construct a nice interface and users are able to upload new data for prediction and analysis in a real time setting. Aiming to fuse both data-driven approach and an easy user interface, this paper is prepared to assist in placement forecasts thus aiding career guidance counsellors and students to use the available job opportunities more effectively. The system could, however, be developed further by including parameters relevant to the industry so that it can be applicable to a number of fields and professions.

关键词： Analytical models Accuracy Engineering profession Computational modeling Data preprocessing Predictive models Data models Telecommunication computing Decision trees Remuneration

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Evolution-based Deployment with Efficient Routing and Crow Search Optimization in Internet of Things Application

Evolution-based Deployment with Efficient Routing and Crow S...

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international conference on ent Computing and Control Systems (ICICCS)

作者： C. Kalaivanan Ayman Amer Maria Rubiston. M M. Vennila Jayant Giri M. Dinesh Department of EEE Sona College of Technology Salem Department of Energy Engineer Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of ECE S.A. Engineering College Chennai Department of of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

IoT technologies to bolster residential security, safety, and sustainability. By leveraging facial recognition for door access, enhancing fire safety monitoring, and integrating energy harvesting, it aims to create more resilient and eco-friendly residential environments. These innovations offer comprehensive solutions to address evolving challenges and optimize residential living. An ED Model coupled with Crow Search Optimization (CSO) for enhancing the efficiency and reliability of IoT applications. The ED model integrates static routing mechanisms with efficient protocols like AOMDV, ensuring robust connectivity and energy-efficient operation in diverse environments. Leveraging CSO, inspired by collective crow intelligence, enhances network performance by dynamically adapting to changing conditions. Through simulations and comprehensive analysis, the proposed approach demonstrates superior performance compared to existing models, achieving high data delivery ratio, network throughput, and energy efficiency. This integrated framework offers a sustainable solution for resilient IoT deployments, addressing evolving challenges and optimizing residential living environments.

关键词： Adaptation models Analytical models Routing Throughput Energy efficiency Data models Internet of Things Reliability Sustainable development Optimization

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Trust Assessment Model for Visual Image-Based Human-Robot Interaction Under Known and Unknown Threats 16th

Trust Assessment Model for Visual Image-Based Human-Robot I...

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16th international conference on Social robotics, ICSR + BioMed 2024

作者： Li, Heqing Li, Xinde Liu, Erfeng Ge, Shuzhi Sam School of Cyber Science and Engineering Southeast University Nanjing China School of Automation Southeast University Nanjing China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9789819789627

Visual imagery is a critical perceptual source in human-robot interaction (HRI), extensively applied in various domains such as robotic navigation, target detection, and task planning. Aerial robots, also known as UAVs, are the typical representatives of teleoperation robots. The substantial distance between humans and remotely operated robots, coupled with the dynamic and open environments these robots navigate, exposes image-based HRI to significant security risks from both known and unknown threats. The trustworthiness of data sources is a fundamental prerequisite for ensuring the security of HRI systems. However, the multidimensionality of trust factors and the uncertainty of trust evidence have constrained its practical application. To advance this field, we have innovatively proposed a trust evaluation scheme based on multidimensional evidence fusion within the Belief Functions (BFs) framework. This scheme constructs an adaptive trust evaluation model capable of addressing both known and unknown threats. For known threats, we developed coarse-grained multidimensional trust elements and employed multiple lightweight SVM submodels to construct Basic Belief Assignments (BBAs), thereby achieving direct trust modeling and enabling rapid trust evaluation of images. Conversely, for unknown threats, we utilized pretrained models to establish fine-grained multidimensional trust elements, implementing indirect trust modeling through trust recommendation, thus facilitating indirect trust evaluation of images. We further combined direct and indirect trust to derive a overall trust assessment of the images, achieving adaptive dynamic trust evaluation in image-based HRI. Additionally, to more accurately represent image trustworthiness, we introduced a BBAs weighted fusion method, which aids in more rational trust aggregation. To validate the efficacy of our proposed method, we conducted experiments on a real aerial image dataset. The results demonstrate that our approach ef

关键词： Human robot interaction

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Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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international conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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Real-Time Cyberbullying Detection Across Multimodal Platforms Using XLNET and Deep CNN

Real-Time Cyberbullying Detection Across Multimodal Platform...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Suresh Kumar K Abi C Asmitha V Dharshine S Department of Information Technology Sri Krishna College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Cyberbullying has emerged as a primary societal concern with the widespread use of social media and online communication systems. The developing complexity of virtual interactions, together with text messages, social media comments, and images with embedded textual content, makes computerized detection way more challenging and complex. Conventional methods frequently fail to adequately convey the complex and dynamic character of cyberbullying practices. Here, we recommend a robust solution which makes use of Deep Convolutional Neural Networks (Deep CNNs) for potent classification of messages and XLNet for feature extraction. This can process a variety of input types, including textual and visual information, to enhance the identification of bullying patterns more efficient by integrating these contemporary system learning techniques. This framework's ability to detect various forms of online movement makes it more effective across multiple modalities thus contributing to the development of a safer and more inclusive online environment through corrective action for all users.

关键词： Training Sentiment analysis Adaptation models Visualization Text analysis Cyberbullying Feature extraction Real-time systems Data models Convolutional neural networks

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Evaluating Feature Importance in the Context of Simulation-Based Inference for Cortical Circuit Parameter Estimation 17th

Evaluating Feature Importance in the Context of Simulatio...

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17th international conference on Brain Informatics, BI 2024

作者： Sandron, Alessandro Valero, Alejandro Orozco García, Juan Miguel Mancini, Gabriele Pelayo, Francisco Morillas, Christian Panzeri, Stefano Martínez-Cañada, Pablo University of Padua Padua Italy University of Granada Granada Spain Istituto Italiano di Tecnologia Genoa Italy Department of Pharmacy and Biotechnology University of Bologna Bologna Italy Hamburg Germany Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

ISBN: (纸本)9789819632930

Extracellular electrophysiology recordings capturing the activity of neuronal populations, e.g., Local Field Potentials (LFPs), have offered important insights into cortical dynamics. Yet there is still a lack of clarity about how features and characteristics of these extracellular potentials relate to the properties and function of the underlying neural populations. Mechanistic models combined with simulation-based inference (SBI) algorithms have emerged as an effective strategy for developing predictive tools that fit well with available empirical data and can be used to predict key parameters that describe neural activity. Numerous SBI techniques rely on summary statistics or interpretable features to approximate the likelihood or posterior. However, at present, a significant challenge is assessing how each feature impacts the SBI model’s predictions. Here, we developed an approach to determine feature importance in the context of cortical circuit parameter inference. We first created a dataset that includes a million distinct simulations from a spiking cortical microcircuit model of recurrently connected excitatory and inhibitory populations. Biophysics-based causal filters were coupled with spikes to generate realistic LFP data. We then extracted a set of meaningful features from simulated LFP data that were used to train an SBI algorithm. To evaluate feature importance, we employed SHAP values, a prominent tool in machine learning for interpreting the contribution of each feature to the prediction outcomes. Our findings demonstrate the effectiveness of our approach in pinpointing the most critical features, such as dfa, rs_range, low_freq_power or transition_variance, for inferring parameters of a recurrent cortical circuit model based on electrophysiological data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrophysiology

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Collaborative Optimization Algorithm of Deep Learning and Compliant Control in Online Trajectory Planning

Collaborative Optimization Algorithm of Deep Learning and Co...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Junting Liao Chunjian Zhao Hui Xie School of Computer and Artificial Intelligence Xiangnan University Chenzhou China China Telecom Hunan Chenzhou Branch Chenzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. This study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. Then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. The trained CNN model is then used to generate real-time trajectory predictions. The robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. The average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. The collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments.

关键词： Deep learning Trajectory planning Heuristic algorithms Collaboration Predictive models Prediction algorithms Real-time systems Trajectory Convolutional neural networks Optimization

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