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2024 international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings

2024 International Conference on Emerging Trends in Networks...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

ISBN: (纸本)9798350353266

the proceedings contain 126 papers. the topics discussed include: towards predictive modelling of recidivism: a case of Namibia correctional services;research and application of commodity recognition algorithm based on machine learning;multi-hop data offloading in heterogeneous ad-hoc UAV networks with deep reinforcement learning;a comprehensive approach to cryptography and security testing in network defense;emotion analysis in smart journals: bridging ML and NLP for holistic mental health monitoring;multi-agent deep reinforcement learning for hybrid motion planning in dynamic environments;advance facial expression detection mechanism using single shot detection;an intelligent technique for the effective prediction of Parkinson disease;beyond traditional methods: exploring deep learning techniques for accurate traffic projection;and an efficient ai based approach for multimedia traffic management in wireless network.

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Cognitive Deep-learning approach to enhance Problem-Solving Skills based on Knowledge Organization 4

Cognitive Deep-Learning approach to enhance Problem-Solving ...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Akhil, Karnam Harshadha, Bollineni Kota, Nikhil Kalakata, Harika Kondabrolu, Jaswanth Abhinay, Medipally Vnr Vignana Jyothi Institute Of Engineering And Technology Department of Cse&csbs Hyderabad India Vnr Vignana Jyothi Institute Of Engineering And Technology Department of Csbs Hyderabad India Vnr Vignana Jyothi Institute Of Engineering And Technology Hyderabad India

ISBN: (纸本)9798331518981

Knowledge is a fundamental component of human cognition that exists in procedural, declarative, and non-declarative forms and plays a crucial role in influencing problem-solving capabilities. In the current environment of education and professional development, the capacity to solve complex problems effectively is of great importance. Graduates are often examined for problem-solving skills as they transition from education to the professional world. Despite the vast amount of knowledge available, graduates frequently face challenges applying their cognitive skills to real-world situations. this requires a deeper insight into how different types of knowledge and their organization affect problem-solving abilities. Recent studies have demonstrated how data mining techniques with access to surveys can potentially solve problems associated with Attention Deficit Disorder (ADD) and Absence Epilepsy. Research on data pattern analysis regarding depression, anxiety, and cognitive impairments has actually enabled researchers to design interventions in the form of prototypes in video games that may improve cognitive and motor functions. the aim of this study is essentially applying a similar approach to analyze the intricate relationship between knowledge organization, cognitive skills, and problem-solving efficacy among graduates. the data is collected through surveys from the graduates. Concepts of data mining are used to extract the meaningful patterns between knowledge and cognitive skills and their impact on the problem-solving ability to analyze how effectively the knowledge is being used and affected for and by solving different problems. the study mainly focuses on deep learning approaches that are capable of recognizing complex patterns, inferring relationships, and providing tailored recommendations. In conclusion, this research will contribute to a deeper understanding of knowledge acquisition, organization, and utilization, with significant implications for educat

关键词： Knowledge organization system (KOS)

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

4th International Conference on Frontiers in Computing and S...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

ISBN: (纸本)9789819726103

the proceedings contain 48 papers. the special focus in this conference is on Frontiers in Computing and Systems. the topics include: machine learning-Based Early Epilepsy Diagnosis with Secure EEG data Sharing Using Blockchain;application of Different Decision Tree Classifier for Diabetes Prediction: A machine learning Approach;exploring Electric Vehicle Adoption Research through Bibliometric Analysis and Visualization;optimizing Solar Power Distribution in Microgrids to Reduce Energy Waste;GPA: Uni-directional GRU-Based Traffic Prediction Model for Minimizing Air Pollution;A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm;Unveiling the Art of Music Generation with LSTM;few-Shot learning with Fine-Tuned Language Model for Suicidal Text Detection;a Comparative Analysis of Feature Selection Approaches for Sensor-Based Human Activity recognition;GuideBP: Guided Backpropagation in Multi-output Neural Networks by Channeling Gradients through Weaker Logits;a Study on Users Sentiment from Twitter data and Stock Market During Russia-Ukraine War;illegitimate Comment Filtration Method for Social Media Applications Using Logistic Regression;Unsupervised MTS Anomaly Detection with Variational Autoencoders;SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems;predicting Suicidal Behavior Among Indian Adults Using Childhood Trauma, Mental Health Questionnaires and machine learning Cascade Ensembles;analyzing Students’ Emotion and Activities in the Classroom: A Rural Education Perspective;MARS: Manual and Automatic Robotic Sanitization on Social Milieu;cervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification;enhancing Graph-Based Representation learning with Adversarial Policy Gradient: A Hyperparameter Analysis;Exploring Label-Specific Feature Weights for Multi-label Feature Selection Using FWMABAC-MFS;CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID-

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A study on stock market forecasting based on time series with integrated multi-feature LSTM modeling 4

A study on stock market forecasting based on time series wit...

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4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Fu, Pengwei Hainan International College Communication University of China Beijing China

ISBN: (纸本)9798331528676

this paper constructs an effective stock prediction model, which can help investors better understand the market trend and accurately grasp the fluctuation pattern of stock prices, thus guiding them to formulate reasonable investment strategies. Taking Hengrui Medicine as the research object, we collected the stock data, established the feature engineering, set up the index evaluation system for the stock features, and constructed the six features of turnover, average price, relative increase, turnover change volume, increase/decrease amplitude, and amplitude from the aspects of enterprise value, enterprise development potential, and capital stability, respectively. In addition, principal component analysis is used to extract effective information and remove redundant information from the constructed features and original features. the decomposition model, exponential smoothing model, ARIMA and SARIMA are applied to the stock price prediction task of Hengrui Medicine. For the decomposition model, the STL and SEATS decomposition methods are used to decompose the closing rate into seasonal terms, trend-period terms, and residual terms. After that, we tried to integrate machine learning models, such as XGBoost, Adaboost and other Boosting models, and Random Forest and other Bagging models, and formed the meta-model into a Stacking model to evaluate the effect of different methods on stock feature extraction. Finally, we use LSTM model in deep learning field and apply its single-feature and multi-feature input dimensions respectively, and combine the previous models to form an integrated machine learning-multi-feature LSTM stock prediction model, and horizontally compare the traditional and modern methods, and get the conclusion that it has a good fitting effect. ©2024 IEEE.

关键词： Costs

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Proceedings - 2023 international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

Proceedings - 2023 International Conference on Advances in E...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

ISBN: (纸本)9798350308082

the proceedings contain 159 papers. the topics discussed include: cloud edge interaction protocol detection system for distribution IoT based on metadata definition;a data mining method based on natural language processing and machine learning;simulation and intelligent fault diagnosis of bidirectional LLC resonant converter;an autonomous exploration method integrating local multi-point TOF data and edge following;regional integrated energy system control strategy considering orderly charging and discharging of electric vehicles;photogrammetric method for calculating spatial coordinates of transmission line galloping;analysis of the mechanism of soft fault in cables based on transmission line theory;short-term photovoltaic power prediction based on similar daily clustering and ISSA optimized neural network;and leveraging profiling for facial recognition optimization.

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Predictive Modelling of Osteoporosis using machine learning Algorithms 4

Predictive Modelling of Osteoporosis using Machine Learning ...

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

作者： Dhivya, R. Sagili, Sreenivas Reddy Praveen, R.V.S. Vamsilala, Purushothapatnapu Naga Venkata Sangeetha, A. Suchithra, B. M.Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Rochester Institute of Technology Department of Computer Science RochesterNY14623 United States M/s. Divija Commercial Properties LTIMindtree Telangana Hyderabad500081 India Vignan's Lara Institute of Technology and Science Vadlamudi Andhra Pradesh Guntur district522213 India Thottiam Trichy India Sri Eshwar College of Engineering Department of Cse Coimbatore India

ISBN: (纸本)9798331529635

Osteoporosis implies the deterioration of bones or their denser tissue and the subsequent elevated risk of skeletal fractures constitutes a major health problem today. therefore, early recognition and initiation of treatment should be enacted to enhance the status of patients. this paper aims at presenting an understanding and analysis of the use of these particular methods, and their specificity in the context of osteoporosis modeling using Support Vector machines. With the help of the large number of features, which are clinical and demographic factors, we are to create a stable model that will allow defining the probability of osteoporosis. the SVM algorithm for dealing with high-dimensional data and classification is used for the investigation of the connection between input characteristics and the advance of the condition. It also makes it easier to determine the most relevant factors that could be used in the creation of a predictive instrument that might help in the early detection of the disease and the subsequent improvement of the available treatment plans. It is with this goal in mind that this research aims at contributing to development of further implementation of machine learning approaches in osteological field, thus laying basis for enhancing diagnostic approaches and patients' management. © 2024 IEEE.

关键词： Support vector machines

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Vision Assist: Real-Time Guidance System 4

Vision Assist: Real-Time Guidance System

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

作者： Mohanraj, E. Devadarshini, D. Manonavaneethan, M. Aravind, S. K.S.Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798331529635

In order to help visually impaired people, this study proposes a machine learning-based system that provides audio feedback, distance calculation, and real-time object detection. the system uses Faster Region-based Convolutional Neural Network (Faster R-CNN), optimized with Compute Unified Device Architecture (CUDA) and CUDA Deep Neural Network library (cuDNN) for GPU-accelerated performance with low latency. Images captured by the rear camera of a smartphone are processed to identify objects and compute their proximity. the system offers audio feedback using the Pyttsx3 text-to-speech engine, alerting users to nearby objects and potential hazards. the system achieved over 99% accuracy in recognizing everyday objects. Parallelized GPU operations ensure efficient real-time object recognition and distance estimation, improving users' safety and mobility. Experimental results demonstrate the system's stability and reliability in assisting visually impaired individuals during navigation. © 2024 IEEE.

关键词： Convolutional neural networks

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Lithium-Ion Battery Prognostic and Health Prediction Using machine learning Models 4

Lithium-Ion Battery Prognostic and Health Prediction Using M...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Nsaka, Ozioma John Malki, Heidar A. University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Engineering Technology HoustonTX United States

ISBN: (纸本)9798350395914

Lithium-Ion battery prognostic and health prediction is an essential part of our modern world today. Reliable predictions of the current state and remaining useful life are critical to ensure safe operation of various industrial applications. there exists established and extensively researched battery state prediction models including the equivalent circuit and physics-based models. the emergence and advancements of machine learning algorithms, such as support vector machine (SVM), have led to a new era of data-driven models. In this research, we initially establish a baseline prediction using traditional machine learning algorithms and subsequently employ deep learning algorithms. Our analysis focuses on the battery's degradation pattern, specifically the predication of the State of Health (SOH) prediction and the Remaining Useful Life (RUL), aiming to predict the degradation from a specific threshold cycle to the EOL of the battery. In this study, our aim is to establish a comparable baseline for several machine learning-based prognostic predictions. To overcome this problem, we conduct several experiments on the same dataset using traditional algorithms and as well as neural network models. the classification accuracy aligns with several benchmarks established in the research literature. this research further underscores the viability of using Long Short-Term Memory (LSTM) models for RUL prediction, thereby advancing the role of data-driven models in prognostic and health management for critical engineering applications. © 2024 IEEE.

关键词： Electronic health record

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

4th International Conference on Frontiers in Computing and S...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

ISBN: (纸本)9789819726134

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One-Shot learning for Archaeological Site data Using Deep Neural Network on Embedded Systems 4th

One-Shot Learning for Archaeological Site Data Using Deep Ne...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Patil, Anoop Jain, Palak Roy, Shastransh Savanur, Shri Nagahari Kulkarni, Uday Chikkamath, Satish K.L.E. Technological University Vidyanagar Karnataka Hubballi580031 India

ISBN: (纸本)9789819994410

Training machine learning models with effective features can be challenging and computationally expensive, especially when dealing with limited data. Conventional gradient-based approaches for target recognition require a large amount of data and extensive training. However, this study investigates a method for training Siamese Neural Networks using One-Shot learning, which reduces training time. By leveraging a unique structure, these networks can rank similarities between inputs and generalize to entirely new classes from unknown distributions. the Indian Archaeological dataset is used to train and test the model, and after fine-tuning the network, its powerful discriminative features can be utilized to extend the predictive capabilities of the model beyond new data. In contrast, the Siamese Neural Network approach allows for effective feature extraction and recognition with limited data and reduces the risk of misconfiguring feature structures. Using this approach, we are able to achieve better accuracy of 86% and 66%, respectively, for One-Shot classification tasks for MNIST and Indian archaeological dataset by usage of triplet loss function. Using quantization, the model size is reduced to make it more compatible for working on embedded devices for classification tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Embedded systems

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