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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Patil, Snehal Sameer Moorthy, Vaishnavi SRM Institute of Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9798350336009

Artificial Intelligence in the healthcare sector is becoming increasingly essential to extract huge texts for decision-making. Extraction of clinical data is a fundamental task in Medical Natural language processing. This process is still challenging through deep learning due to critical medical data, lack of interpretability, and limited availability. Text extraction from Electronic Healthcare records is crucial for improving patient care and understanding clinical decision-making. It also supports analysing the patients' feedback and physician notes to identify areas for improvement in patients' satisfaction and care quality. This helps in drug discovery and development through clinical data patterns. The proposed research focuses on implementing Natural language processing methods for data processing like classification and prediction, Word Sense Disambiguation, Segmentation, and word Embedding. These methods can process vast amounts of medical text data for decision support, research, and drug discovery. It can increase the possibility of identifying the patients who may at risk for certain conditions and diseases related to cancer and comparing it with their medical history. The chief aim is to provide improvised data analyses that could further improve their treatment. © 2023 IEEE.

关键词： Extraction

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ML assisted enhanced recommendation system for reliable volunteering environment 2

ML assisted enhanced recommendation system for reliable volu...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Prabakaran, N. Meenakshi, K. Kumarnath, J. Rajasekaran, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Mathematics Presidency University Bengaluru560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision trees

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Video Compression through Advanced Video Saliency Aware Spatial-Temporal Integration and Attention Mechanisms

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SN Computer Science 2024年第7期5卷 1-15页

作者： Ravishankar, H. AnithaKumari, R.D. Sarvamangala, D.R. Rashmi, C. Deepa, K.R. School of Computing and Information Technology REVA University Karnataka Bangalore India School of Electronics and Communication Engineering REVA University Karnataka Bangalore India School of Electrical and Electronics Engineering REVA University Karnataka Bangalore India

In the field of computer vision, video saliency detection is a critical task that involves identifying and highlighting areas within video frames that are most likely to attract human attention. Despite its importance, this task poses significant challenges due to the dynamic nature of video content, which encompasses varying spatial and temporal features. Addressing these challenges, the Dynamic Attentive Integration for Spatial-Temporal Saliency (DAISTS), a novel methodology that significantly enhances the precision of video saliency detection is designed. DAISTS introduces a dual-path spatial-temporal feature hierarchy, effectively merging deep and shallow learning attributes to improve saliency map accuracy. Additionally, spatial and channel attention aided integration is designed, which adaptively refine feature fusion based on scene context and training data. Moreover, DAISTS incorporates a frame-based attention model, a breakthrough in temporal analysis that selectively prioritizes frames for improved saliency prediction. The methodology is further supported by a comprehensive loss function that integrates Relative Entropy, linear correlation coefficient, and normalized scan-way saliency metrics, ensuring a balanced and effective approach to training. This encapsulates the approach to advancing video saliency detection, highlighting DAISTS as a robust, efficient, and adaptable solution, to make a significant impact in the realm of computer vision. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision DAISTS Relative entropy Temporal analysis

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in Computer Science and Communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham Science Publishers. All rights reserved.

关键词： Electronic health record

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis 18

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Data-Driven StrategicWorkforce Planning in UK SMEs: A Comparative Analysis

Data-Driven StrategicWorkforce Planning in UK SMEs: A Compar...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Jaleh, Fatemeh Saadati, Parisa Husamaldin, Laden Aladesuru, Favour School of Business and Management London School of Science and Technology London United Kingdom School of Computing and Engineering University of West London London United Kingdom

ISBN: (纸本)9798350369106

Amidst escalating digital competition, small and medium-sized enterprises (SMEs) face a vital need to strategically manage human resource management (HRM) for sustainable growth. The adoption of data-driven strategic workforce planning is crucial for SMEs, facilitating informed decisions that enhance operational efficiency. Drawing from secondary data obtained through a governmental survey involving over 2000 SMEs across various sectors in the UK, this study endeavors to deepen our comprehension of the determinants and challenges associated with the adoption of data-driven workforce planning (DDWP) in SMEs and its consequential impact on organizational performance. The study endeavors to construct a framework anchored in the technology-organization-environment (TOE) model, subsequently validating this framework across three prominent industries within the UK SME landscape to substantiate hypotheses derived from the extant literature. The discussion encompasses an exploration of the challenges inherent in implementing DDWP within SMEs, followed by an assessment of how these challenges influence the adoption of DDWP. Subsequently, the study delves into an evaluation of the impact of DDWP on performance, concluding with an examination of the framework's applicability across industries. The results illuminate that drivers influencing the level of adoption of DDWP in SMEs similarly impact selected industries. Moreover, the findings underscore that the level of adoption of DDWP exerts a palpable impact on organizational performance, enhancing overall business efficiency. The outcomes of this study furnish valuable insights and practical recommendations for HR professionals, policymakers, and researchers, delineating strategies to elevate business performance and efficiency through the transformation of workforce planning into data-driven workforce planning. © 2024 IEEE.

关键词： Efficiency

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Exploring the Impact of Deep Learning Models on Lane Detection Through Semantic Segmentation

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SN Computer Science 2024年第1期5卷 1-15页

作者： Kumar, Sunil Pandey, Ankur Varshney, Sudeep School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering Manipal University Jaipur Jaipur India Department of Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

Due to advancements in the deep learning technology, object detection has become significantly important for lane detection and vehicle detection. In recent times, lane detection has become more popular as it plays a significant role in traffic surveillance compared to other object detection technology. However, these strategies have several intrinsic flaws which need to be addressed. Traditional-based techniques still suffer from the challenges of the effectiveness and accuracy, whereas a complex convolutional layer is a challenge for deep learning-based strategies. A parameter selection issue affects the majority of the available lane detection algorithms, which further contributes to their unsatisfactory detection performance. In this study, we provide an effective lane detection method based on semantic segmentation to identify lane lines in a high-dimensional dataset by adding vertical spatial properties and contextual driving information. This paper employs two created frames—feature merging block and information exchange block—to identify unclear and obstructed lane lines more effectively. The simulations have been carried out for the proposed model on TUSimple and CULane datasets which resulted with 94.42% accuracy, 93.47% precision, 92.85% recall, and 93.17% F1-score. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Lane detection Occluded lane lines Semantic segmentation

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CPIE: A Spatiotemporal Visual Analytic Tool to Explore the Impact of Coal Pollution

CPIE: A Spatiotemporal Visual Analytic Tool to Explore the I...

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2024 IEEE Workshop on Energy Data Visualization, EnergyVis 2024

作者： Jin, Sichen Henneman, Lucas Roberts, Jessica Georgia Institute of Technology School of Interactive Computing United States George Mason University College of Engineering and Computing Sid and Reba Dewberry Department of Civil Environmental and Infrastructure Engineering United States

ISBN: (纸本)9798350379235

This paper introduces CPIE (Coal Pollution Impact Explorer), a spatiotemporal visual analytic tool developed for interactive visualization of coal pollution impacts. CPIE visualizes electricitygenerating units (EGUs) and their contributions to statewide Medicare deaths related to coal P M2.5 emissions. The tool is designed to make scientific findings on the impacts of coal pollution more accessible to the general public and to raise awareness of the associated health risks. We present three use cases for CPIE: 1) the overall spatial distribution of all 480 facilities in the United States, their statewide impact on excess deaths, and the overall decreasing trend in deaths associated with coal pollution from 1999 to 2020;2) the influence of pollution transport, where most deaths associated with the facilities located within the same state and neighboring states but some deaths occur far away;and 3) the effectiveness of intervention regulations, such as installing emissions control devices and shutting down coal facilities, in significantly reducing the number of deaths associated with coal pollution. © 2024 IEEE.

关键词： Coal transportation

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