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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1578 条记录，以下是461-470 订阅

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Optimizing data Compression via data Reordering 34

Optimizing Data Compression via Data Reordering

引用

34th Annual international conference on Collaborative Advances in Software and computing, CASCON 2024

作者： Du, Qinxin Yu, Xiaohui An, Aijun Jania, Dariusz York University TorontoON Canada IBM Cloud and Cognitive Software Warszawa Poland

ISBN: (纸本)9798331504830

data compression is critical for efficient data operations such as migration, backup, and analytical processing. While there has been significant research into compression techniques for multimedia like videos and images, relational data has garnered less attention. Current methods for compressing relational data, such as gzip and LZ4, do not optimize record ordering within tables, potentially missing opportunities for improved compression. It has been shown that grouping similar records together can enhance compression performance, as measured by the compression ratio. As such, we propose two novel record reordering methods: (1) clustering and (2) solving a Travelling Salesman Problem (TSP) to reorder records. Additionally, we apply locality-sensitive hashing (LSH) on textual columns to convert text into vector representations, aiding in clustering and TSP reordering. For columns with a fewer number of distinct values, using run-length encoding (RLE) rather than traditional dictionary-based compression may be more effective. We therefore extend our approach to partition the set of columns in a table into two groups based on the distinct value count and then use appropriate compression algorithms for each, aiming to maximize the overall compression ratio. We conducted extensive experiments with one synthetic and three real datasets to evaluate our proposed methods. The results confirm that our proposed approaches significantly outperform existing baselines, demonstrating their efficacy in relational data compression. © 2024 ieee.

关键词： data compression ratio

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Research on Chronic Disease Auxiliary Diagnosis System Based on Multimodal Deep Learning

Research on Chronic Disease Auxiliary Diagnosis System Based...

引用

2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Chang, Hong Han, Tongshuai Wang, Zuofu State Grid Beijing Electric Power Hospital Information Center Beijing China

ISBN: (纸本)9798331511296

This research examines a system for assisting in the diagnosis of chronic diseases using multi-modal deep learning, with the goal of enhancing the precision and speed of chronic disease diagnosis by combining various types of information like images, text, and clinical data. The research developed a hierarchical system design that consisted of layers for data collection, feature extraction, data fusion, and decision-making. Every level has the responsibility of handling and combining data from various sources in multiple modes. By utilizing feature-level fusion technology, feature details from various modalities are merged to provide a thorough grasp and diagnosis of the illness. Experimental findings reveal that the diagnostic capabilities of the suggested system experience a notable enhancement following the fusion of multi-modal data. In comparison to single-modal approaches, both accuracy and resilience see significant improvement. Case studies confirm the system's efficiency on actual data sets, showcasing its promise in early detection of chronic illnesses and crafting tailored treatment strategies. The findings of this study offer a fresh approach to effectively managing chronic illnesses, with significant practical and promotional implications. This system can more effectively address the requirements of diagnosing complex diseases and offer technical assistance for the smart advancement of the medical field. © 2024 ieee.

关键词： data fusion

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Enhanced Diagnosis of Schizophrenia Using Integrated Node Embedding and Causal Discovery on Imbalanced fMRI data 10

Enhanced Diagnosis of Schizophrenia Using Integrated Node Em...

引用

10th international conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Sabzevar, Sina Masoudnia, Saeed Araabi, Babak N Nazemzadeh, MohammadReza Tavakoli, Hosna University of Tehran Department of Electrical and Computer Engineering Tehran Iran Monash University Department of Neuroscience Melbourne Australia Institute of Cognitive Science Studies Computational and Artificial Intelligence Department Tehran Iran

ISBN: (纸本)9798331532543

This study introduces a novel diagnostic method for schizophrenia using causal discovery and node embedding techniques on resting-state fMRI data. data from 148 subjects (27 schizophrenia patients, 121 healthy controls) were analyzed across 116 brain regions, applying several causal discovery techniques to assess directional connectivity and uncover schizophrenia-related patterns. By combining advanced causal discovery with node embedding techniques and a rigorous data-balancing pipeline, our method improves sensitivity to subtle network variations critical for schizophrenia diagnosis. Our approach addresses key challenges in the literature: the difficulty of extracting effective neurobiological markers from noisy and complex fMRI data, the limited interpretability of connectivity networks, and the imbalance in datasets that often skews classification performance. Experimental results demonstrate the pipeline's effectiveness in overcoming imbalanced data challenges and extracting features from noisy and complex data, with Regression Dynamic Causal Modeling (RDCM) achieving the highest classification metrics, including 90.06% accuracy and 89.09% sensitivity. This method identified 15 brain regions with strong diagnostic potential, offering improved sensitivity and specificity for schizophrenia diagnosis Compared to contemporary domain techniques. © 2024 ieee.

关键词： Diseases

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Optimizing Machine Learning Algorithms for Temperature Forecasting and Energy Management Systems

Optimizing Machine Learning Algorithms for Temperature Forec...

引用

2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra Sekhar, G. Kedhari Sailaja, P. MadhusudhanaRao, G. Srinivas, N. Sai Thrinath, B.V. Akash, G. Dept. of Computer Science Engg Aeronautical Engineering Hyd Visakhapatnam Dept of Computer Science Engg Institute Visakha Inst. of Engg. & Tech Dept of Electrical and Electronics Engg Vardhaman College of Engg Hyd. Dept of Electrical and Electronics Engg Mohan Babu University Tirupati Dept of Computer Science Engg Woxsen University Hyderabad

ISBN: (纸本)9798350373783

Cloud data centers strive to provide efficient, scalable, and reliable computing resources while addressing challenges related to security, privacy, cost, and more. Their objectives include delivering value to users through flexibility, availability, innovation, and environmental responsibility. These data centers have observed tremendous growth, utilizing massive energy for computer resources and cooling systems. A combination of time series regression models, machine learning, and other optimization techniques may be used to develop more robust and adaptive resource management policies for complex data center environments. Additionally, continuous monitoring and feedback loops are essential to ensure that the models remain accurate and effective as conditions evolve. This paper studied existing machine learning algorithms and methods for temperature forecasting and adequate energy consumption for data centers. The investigations have been analyzed that the impact of energy consumption on computer resource workloads significantly improved, and Virtual Machines scheduling algorithms will optimize the forecasting of thermal effects. This paper also compares the regression model with the Machine Learning algorithms, which will provide the best methodology for forecasting thermal effects and improving efficient energy. © 2024 ieee.

关键词： Cloud computing data Centres Index: Energy Efficiency Privacy Security Thermal Forecast Virtual Machines

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Neuro-cognitive Pattern Recognition: Advancements in Memory-Related Disorders Identification 6

Neuro-Cognitive Pattern Recognition: Advancements in Memory-...

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6th ieee international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Vifert Jenuben Daniel, V. Archanaa, N. Faheem, Mohammed Kousihik, K. Suwin Kumar, J.D.T. Muthulakshmi, M. Amrita School of Engineering Amrita Vishwa Vidhyapeetham Department of Artificial Intelligence Chennai India Amrita School of Engineering Amrita Vishwa Vidhyapeetham Department of ECE Chennai India

ISBN: (纸本)9798350306613

The application of machine learning in medical imaging and diagnostics opens up new pathways for disease identification and classification. Our research investigates the use of various machine learning models for Alzheimer's disease detection and classification using MRI image data, including Support Vector Machines (SVM) with different kernels, Linear Discriminant Analysis (LDA), and the advanced convolutional neural network, EfficientNetB0. The comparative study is an important component of this research, concentrating not only on the models' performance but also on the theoretical assumptions that underpin their efficacy. This investigation sheds light on why certain models outperform others, notably in the context of Alzheimer's disease classification. The research emphasizes the robust performance of linear models such as SVM and LDA, as well as the complexities of convolutional neural networks. Notably, our findings show a tremendous success with a 98.8 % accuracy rate, highlighting the potential of machine learning in improving diagnostic processes. This study contributes to the expanding field of machine learning in medical diagnostics by providing in-depth analysis and theoretical understanding that guide model selection and optimization in high-stakes healthcare applications. © 2024 ieee.

关键词： Alzheimer disease CNN EfficientNetB0 LDA MRI PCA SVM

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Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

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2024 ieee-EMBS international conference on Biomedical and Health Informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 ieee.

关键词： Prediction models

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MT-CNN: A Lightweight Spatial-Temporal Convolutional Neural Network for Deep Learning of complex Trajectory Distributions based on Area Partitioning

MT-CNN: A Lightweight Spatial-Temporal Convolutional Neural ...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Ye, Rongkun Lv, Zhiqiang Xu, Zhihao Li, Jianbo Qingdao Univ Coll Comp Sci & Technol Inst Ubiquitous Networks & Urban Comp Qingdao Peoples R China

ISBN: (纸本)9798350359329;9798350359312

With the rapid development of 5G technology and deep learning, intelligent transportation systems (ITS) have been constantly improved and perfected. In the ITS, trajectory prediction, as a key part, requires the processing of a large amount of urban data, which places a high demand on computing resources. To reduce computational complexity and demand, this work proposes a lightweight spatial-temporal model based on convolutional neural networks which has fewer model parameters, faster prediction speed and higher prediction accuracy. This work transforms the complex trajectory into regular areas, reducing model computation while also more intuitively representing the change in trajectory. This work extracts multi-scale spatial features through spatial convolutions of different sizes to comprehensively understand the spatial variation of trajectory. Then, the temporal correlation of the trajectory is extracted through multi-layer time convolution. In addition, this paper introduces the trajectory relationship matrix(TRM) to enhance the global spatial relationship of the pedestrian trajectory and capture different spatial correlations for different areas. It analyzes the relationship between the areas where the trajectory is located by learning the historical trajectory. This work introduces extra factors such as pedestrian motion direction and speed to assist prediction, improving the model's prediction ability and robustness. Experimental results show that the proposed MT-CNN model improves the accuracy by 22.7% compared to other models, with better performance and generalization ability, while also having excellent computational time and energy consumption.

关键词： Deep learning Trajectory prediction Lightweight spatial-temporal model Trajectory relationship matrix

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Enhanced Machine Learning Algorithms for Precise Heart Disease Diagnosis 3

Enhanced Machine Learning Algorithms for Precise Heart Disea...

引用

international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Sarika, Shinde G. Kharade, K. G. Kamat, R. K. Shivaji Univ Dept Comp Sci Kolhapur Maharashtra India Shivaji Univ Dept Elect Kolhapur Maharashtra India Dr Homi Bhabha State Univ Mumbai Maharashtra India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The development of bettered machine learning algorithms is the primary focus of this exploration, with the ultimate thing of enhancing the delicacy of heart complaint opinion. presently available individual styles constantly warrant the necessary delicacy, which can affect treatments that are moreover delayed or incorrect. The purpose of this exploration is to develop models that can relate and prognosticate heart complaints from complex medical data with lesser perfection. These models will be created by utilizing advanced ways in machine learning, similar to deep learning and ensemble styles. An expansive quantum of data preprocessing, point selection, and model training are all part of the exploration process. These conditioning are carried out on large datasets attained from healthcare institutions. The primary findings suggest that there has been a substantial improvement in individual delicacy in comparison to the conventional styles, along with an increase in both perceptivity and particularity. Not only does this approach demonstrate the eventuality of machine learning to revolutionize healthcare diagnostics, but it also holds the pledge of perfecting patient issues by allowing for earlier and more accurate opinions. Eventually, the findings of this study could contribute to the development of individual tools that are more reliable, effective, and scalable, which would eventually lead to bettered operation and treatment of heart complaints.

关键词： Machine learning heart disease diagnosis accuracy predictive models medical data analysis deep learning cardiovascular health

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Using Big data Mining Algorithm to Improve the Accuracy of Risk Assessment of Key Operating Vehicles 3

Using Big Data Mining Algorithm to Improve the Accuracy of R...

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3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Huang, Xin Wu, Yunpeng Zhao, Xin Zhang, Jiuchuan Li, Jie China Transport Telecommunications & Information Center Beijing100102 China China Transport InfoJet Technologies Co. Ltd. Beijing100102 China Transportation Bureau of Xinjiang Production and Construction Corps Xinjiang Wulumuqi83002 China

ISBN: (纸本)9798331519032

Traditional risk assessment methods face challenges when dealing with massive data and complex operating environment. Therefore, this paper proposes a hybrid mining algorithm-Integrated Risk Decision Tree (IRDT) which integrates decision tree and random forest. IRDT algorithm combines the intuitive interpretation of decision tree and the integrated learning advantages of random forest, and improves the accuracy and stability of risk assessment through the training and integration of multiple decision tree sub-models. The experimental results show that the accuracy of IRDT algorithm on the test set reaches 90.40%, which is significantly higher than that of logistic regression model (76.80%) and support vector machine (81.20%). In addition, IRDT algorithm shows a fast response speed when dealing with real-time data, which verifies its effectiveness in complex and multi-dimensional risk assessment tasks. The risk assessment model based on IRDT algorithm constructed in this paper not only improves the accuracy of risk assessment of key operating vehicles, but also provides detailed risk classification and quantitative assessment methods, which provides strong support for the safety management of transportation industry. The research results show that big data mining algorithm has important application value in risk assessment of key operating vehicles. © 2024 ieee.

关键词： big data decision tree operating vehicles random forest risk assessment

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cognitive Wellness Assessment: A Comprehensive Review of Deep Learning and Machine Learning Approaches for Mental Stress Detection in the Digital Age 14

Cognitive Wellness Assessment: A Comprehensive Review of Dee...

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14th international conference on Cloud computing, data Science and Engineering, Confluence 2024

作者： Panda, Madhusmita Sharma, Arun Jaiswal, Garima Indira Gandhi Delhi Technical University for Women Computer Science Engineering New Delhi India Bennett University Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350344837

Mental stress is a big problem nowadays, particularly in young people. The age group that was formerly seen as the most carefree is presently experiencing a great deal of stress. Increased stress in today's world causes numerous issues like depression, suicide, heart attacks, and *** identification is important because it can help to prevent chronic stress and its negative health consequences, improve productivity and performance, and reduce the risk of accidents and injuries. It can also increase self-awareness and improve communication and relationships. Effective stress management is essential for preserving general wellbeing and guarding against the harmful consequences of long-term stress, which can have an adverse influence on relationships, mental and physical health, and physical *** incorporating stress management techniques into daily life, individuals can effectively reduce stress levels, enhance their quality of life, and promote *** in deep learning and machine learning methods, this paper offers an extensive overview of the research on stress identification. Their development over a number of years is covered. Furthermore, the study makes recommendations for future work, with a focus on increasing accuracy and utilizing new technologies. For scholars and practitioners in the field, this paper provides insightful information about the prospective applications of deep learning and machine learning in stress identification. © 2024 ieee.

关键词： Health risks

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