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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Devi, Kanniga R. Muthuraman, Poorvesh Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

Amidst a demanding world, accessible mental health support is crucial. This project proposes a portable hardware solution for anxiety reduction, integrating breathing exercises, like the physiological sigh, known for alleviating stress. The aim is to assist users in practicing these exercises during distress by leveraging wearable technology, particularly the Apple Watch. The research explores the development, design, and effectiveness of this solution, emphasizing the intersection of technology and mental health for a resilient society. This research paper explores the intricate development process, design considerations, and the overall effectiveness of this innovative solution. It emphasizes the potential of wearable technology in bridging the gap between technology and mental health, offering a personalized and adaptive approach to anxiety management. Through continuous user feedback and iterative improvements, the project aspires to contribute to a more resilient society, where individuals are empowered to manage their mental well-being through the seamless integration of technology and mindfulness practices. © 2024 IEEE.

关键词： Wearable computers

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Privacy-Preserving Employee Attrition Prediction using Deep Learning and Fully Homomorphic Encryption

Privacy-Preserving Employee Attrition Prediction using Deep ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Rajesh, R. Harshavardhan, S. Kirthana, B. Shreya, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Employee attrition presents a significant challenge for organizations, impacting both finances and operations. Mean-while, the automation of human resource processes, including recruitment and performance monitoring, heavily relies on vast amounts of personal data. Traditional predictive models for employee attrition have increasingly turned to deep learning techniques due to their superior accuracy in deciphering complex data patterns. However, these methods raise serious privacy concerns due to their reliance on sensitive employee information. This paper introduces a novel solution by integrating Fully Homomorphic Encryption (FHE) with deep learning, ensuring data privacy without compromising predictive performance. This integration not only enhances attrition prediction accuracy but also maintains employee data confidentiality, potentially improving organizational efficiency and trust in HR processes. © 2024 IEEE.

关键词： Differential privacy

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Deep Learning-Based Predictive Model of Molecular Properties 6

Deep Learning-Based Predictive Model of Molecular Properties

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Yang, Mingyu Chen, Xiangyang Lv, Weijie School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

With the rapid development of computer hardware and machine learning algorithms in recent decades, artificial intelligence has created a boom in various industries. computer technology has been widely used in all stages of drug discovery, from machine learning to deep learning, and scientists have conducted numerous studies on the physicochemical properties of drugs. With the advancement of computer technology and deep learning algorithms, it has become possible to obtain large-scale molecular characterization data, extract different types of molecular representations, and construct more subtle models. Among them, molecular graph structure data is increasingly used in deep learning to predict molecular properties. It directly extracts the two-dimensional structural information of molecules, avoiding the defects of indirect structural information extraction in traditional methods. However, the performance of different algorithms based on molecular fingerprints, descriptors, molecular pictures and molecular graph structures is currently controversial. Some studies have found that descriptor-based models outperform graph-based models, while others do the opposite. This shows the difference in the impact of different tasks and datasets on the models. In this experiment, molecular graphs and molecular fingerprints are combined, and molecular graphs are fed into graph neural networks to extract features after the attention mechanism, molecular fingerprints are fed into a fully connected layer to extract features, and the features extracted from the two molecular representations are fused together and a specific activation function is adopted to see if the accuracy and stability of the model can be effectively improved. © 2024 IEEE.

关键词： Adversarial machine learning

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An Intelligent Shared Decision-Making Model among One Patient and Multiple Doctors

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第1期8卷 132-156页

作者： Liu, Yong Lin, Kai-Biao Lu, Ping Lin, Min School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Electromechanical and Information Engineering Putian University Putian351100 China

Shared decision-making (SDM) is an effective decision-making method in clinical practice. However, the pressure of negotiation and decision makes it difficult to apply widely. To alleviate the pressure of artificial SDM and promote the realization of clinical SDM, this article presents a fuzzy constraint-based negotiation and decision method for the patient-doctors SDM. The proposed method includes a negotiation model and a decision-making model. The negotiation model quantifies the negotiation process between patient agent (PA) and doctor agents (DAs) in SDM. It consists of the negotiation behavior and the negotiation protocol of agents. The decision-making model quantifies the decision process of SDM. It translates the negotiation results into treatment plans and assists PA in making decisions. The main contributions are as follows: 1) the agent technology is applied to make one-to-many SDM efficient and intelligent;2) the distributed and fuzzy constraint theories are used to design an interconnected, autonomous, and distributed multi-agents negotiation system for SDM;3) the decision-making model is presented to assist doctors and patients in making decisions. The evaluation results of the negotiation and decision models demonstrate that our method is feasible and effective. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Decision making

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Advancing E-commerce Recommendations: A Comparative Study of BERT-Enhanced Collaborative Filtering and Alternative Approaches

Advancing E-commerce Recommendations: A Comparative Study of...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dazzle, A.J. Singh, Shashank Naik, Dishant Kanmani, Sujithra R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331517953

In e-commerce, recommendation systems are critical to increasing user engagement and conversion. However, traditional collaborative filtering methods require user feedback and do not take into consideration semantic understand of the items. In order to eliminate these barriers, this work suggests a novel solution of fusing collaboration technique with BERT-based embeddings for e-commerce suggestions. Besides this we include a variety of other models for comparison namely standalone BERT, Collaborative filtering approach, Roberta + Collaborative Filtering and Deberta+Collaborative Filtering. We are using collaborative filtering and item title, reviews (via BERT-based embeddings) to model user preferences. Through experiments on a real Flipkart dataset, we validate the effectiveness of our approach. Recommendation in the merged model results, is our proposed model achieved higher accuracies. These results hold valuable insights for enhancing e-commerce platform recommendation systems. This study helps improve the state-of-the-art in e-commerce recommendation systems by combining collaborative filtering and more advanced natural language processing methods. © 2024 IEEE.

关键词： Collaborative filtering

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Classification of Customer Churning based on OTT platform data. 4

Classification of Customer Churning based on OTT platform da...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Panda, Amiya Ranjan Mishra, Manoj Kumar Kalsi, Dvij Bhuyan, Kaushik Jyoti Saha, Soumyadeep Bhuyan, Kaustabh Jyoti Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

Customer Churning is a serious problem for firms since it reduces revenue and stunts long-term development. As a result, anticipating and reducing customer churn has emerged as a key area for machine learning research. We investigate several machine learning models for forecasting customer churn rates. To pinpoint the elements influencing customer outcomes, we first analyse a real-world dataset of an OTT provider. Next, for the purpose of constructing prediction models, we preprocess the data, conduct exploratory data analysis, and apply a diverse set of machine learning methodologies. These include logistic regression, decision trees, the LGBM Classifier, support vector machines, Random Forest, K-Nearest Neighbour Classifier, XGBoost Classifier, as well as various Bagging and Boosting Methods, respectively. Subsequently, we assess the prediction results using evaluation metrics. Our findings demonstrate that, with an accuracy of 89%, the XGBOOST Classifier algorithm surpasses other algorithms in forecasting customer churn rates. We identify crucial factors, including gender, age, subscription duration, weekly minutes of engagement, video consumption, maximum inactive days, etc. Our research underscores the significance of employing machine learning techniques to predict customer churn rates. This application can aid companies in identifying customers who are at a higher risk of leaving and implementing preventive measures to retain them. Businesses may utilize our insights to create strategies that effectively retain customers and enhance their overall success. © 2024 IEEE.

关键词： Decision trees

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Network Intrusion Detection System using Random Forest and Gradient Boosting Machines 4

Network Intrusion Detection System using Random Forest and G...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Bharathi, Indira Makhija, Rohan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349900

The recent technological advancements not only astonish us but also demonstrates that if they are misapplied, they can pose a serious threat. A Network intrusion Detection system (NIDS) is presented in this paper which leverages the abilities of Random Forest and Extreme gradient boosting Machine (Xgbm) classifiers and harnesses the ensemble of both classifiers which enhances the accuracy of the system and brings out the best in both the classifiers. The system is trained using the UNSWNB15 dataset. Additionally, the system also employs the statistical and Recursive Feature Elimination for feature selection which enhances the model's performance. © 2024 IEEE.

关键词： Network intrusion

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UE-GS: High-Quality 3D Gaussian Splatting Real-Time Rendering for Unreal Engine 7

UE-GS: High-Quality 3D Gaussian Splatting Real-Time Renderin...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Jin, Xiaodan Han, Xuefei Nan, Hai Xue, Zhijian School Of Computer Science And Engineering Chongqing University Of Technology Chongqing China

ISBN: (纸本)9798350361445

Recently, 3D Gaussian Splatting (3D-GS) has gained popularity in novel-view scene synthesis, achieving high fidelity and efficiency. However, the rendering quality of 3D-GS is still not up to the quality of the best methods in NeRF, and serious artifacts will occur when changing the sampling rate and changing the focal length or camera distance. Another significant problem is that interactive real-time rendering of complex scenes also has great limitations. To address this challenge, we have come up with a new rendering system, UE-GS, that blends 3D-GS with traditional rendering techniques in Unreal Engine 5 (UE5) to achieve higher quality visuals. Our approach draws inspiration from UE5's Level of Detail (LOD) technology. When changing the focal length or camera distance, we dynamically render LOD for different viewing levels, and replace the dilatation filter in 3D-GS with 2D EWA filtering, effectively alleviating the problems of aliasing and dilatation artifacts. Use octree to optimize spatial point data in complex scenes (40 million Gaussian +) and customize rendering components so that they are not limited by particle counts in UE5. We validated our approach on a variety of data sets and scenes that retain visual quality, achieving better rendering quality and faster training and reasoning speed than 3D-GS, while retaining the benefits of real-time performance. In addition, rendering in UE5 helps with scene editing in subsequent stages. © 2024 IEEE.

关键词： Engines

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Photonic Crystal Microring Resonator on a Hybrid Silicon nitride-on-lithium niobate Platform

Photonic Crystal Microring Resonator on a Hybrid Silicon nit...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Peng, Zhongdi Krishna, Rakesh Wu, Xi Hosseinnia, Amir H. Fan, Tianren Adibi, Ali School of Electrical and Computer Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781957171395

We demonstrate a photonic crystal resonator on hybrid silicon nitride-on-lithium niobate (SiN-on-LN) platform, designed for microwave-assisted optical-frequency conversion applications. We measure a large intrinsic quality factor (Qint) of 1.47×105 with mode-splitting bandwidth of 14.6 GHz. © Optica Publishing Group 2024 © 2024 TheAuthor (s)

关键词： Silicon nitride

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A Novel RoPE-Enhanced Informer Approach for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries

A Novel RoPE-Enhanced Informer Approach for Accurate Remaini...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Nair, Aayush Bhatnagar, Shashwat Srinivasan, Kathiravan Manoov, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331505776

Lithium-ion batteries are integral to various systems, including electric vehicles and warehouses, where estimating their operational lifespan is critical. This lifespan, known as Remaining Useful Life (RUL), helps in optimizing performance and maintenance. Accurate RUL prediction helps avoid unexpected breakdowns and lowers maintenance costs, making these systems more reliable. This paper introduces a novel approach using a Rotary Positional Embedding (RoPE) Enhanced Informer model to address challenges in long-term time series forecasting for RUL prediction. By integrating RoPE into the Informer, the model efficiently preserves positional information and leverages sparse attention mechanisms to reduce computational complexity, enabling it to handle longer sequences with minimal memory usage. The proposed model was tested on benchmark datasets, where it outperformed peer level approaches in terms of Mean Absolute Error and Root Mean Squared Error. The results show a significant reduction in validation errors, indicating the model's superior generalization ability and its robustness against overfitting. This approach offers a scalable solution suitable for real-time predictive maintenance, particularly in resource-constrained environments. The combination of advanced positional encoding with the Informer architecture demonstrates the potential for this method to enhance battery management and improve the accuracy of RUL prediction across various industrial applications. © 2024 IEEE.

关键词： Lithium-ion batteries

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