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Enhancing Smart Healthcare Services Through Deep Learning-Based Medical data Analysis 3

Enhancing Smart Healthcare Services Through Deep Learning-Ba...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Ahmed, Sheik Jamil Atham, Saira Banu Kadhim, Hajar Gupta, Deepak Muraay Majed, Aaied Eqab Mohamed Faizal, M.K. Madanapalle Institute of Technology & Science Department of CSE Andhra Pradesh India Presidency University Presidency School of Computer Science and Engineering Karnataka Bangalore India Uruk University Department of Medical Laboratory Techniques Baghdad Iraq Graphic Era Hill University Department of Mechanical Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Mechanical Engineering Uttarakhand Dehradun India Al Kitab University Department of Medical Engineering Kirkuk Iraq K.Ramakrishnan College of Engineering Department of AI & DS Trichy India

ISBN: (纸本)9798331523893

In order to enhance patient care and medical decision-making, smart healthcare services are progressively utilizing cutting-edge technologies. Deep learning (DL), one of these technologies, has become a potent instrument for evaluating enormous volumes of medical data, allowing for improved clinical procedures and more precise diagnosis. This study focuses on enhancing smart healthcare services through deep learning-based medical data analysis, with a particular emphasis on Named Entity Recognition (NER) and Relation Extraction (RE) tasks from clinical records. The study is implemented using Python software on the MIMIC-III dataset, utilizing an optimized Particle Swarm Optimization (PSO)-BioBERT model. The model achieved remarkable performance in both NER and RE tasks, with accuracy rates of 99.2% for NER and 99.5% for RE, and F1-scores of 98.1% and 98.8%, respectively. Compared to traditional models like Bi-LSTM and BERT, the PSO-BioBERT model significantly outperformed them, with the proposed model exceeding Bi-LSTM by 6.2% in accuracy and BERT by 12.12%. The findings highlight that the PSO-BioBERT model provides substantial improvements in extracting and classifying medical entities and their relationships from clinical data. These developments revolutionize smart healthcare services by making it possible to analyze unstructured medical information more accurately and consistently. The study emphasizes the effectiveness of deep learning models, particularly PSO-BioBERT, in improving data-driven decision-making in healthcare, which could ultimately enhance patient care outcomes and healthcare system efficiency. Future work could focus on integrating these models with real-time clinical systems for broader applications, including predictive analytics and personalized treatment plans. This approach holds promise for revolutionizing how healthcare data is utilized to improve overall patient care. © 2025 ieee.

关键词： BioBERT Deep Learning Medical data Analysis NER Smart Healthcare

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Leveraging AI to Personalize HR Marketing Campaigns: A data-Driven Approach 3

Leveraging AI to Personalize HR Marketing Campaigns: A Data-...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Alshar, Moath Mahmoud Sao, Ameet Sharma, Manish Kadyan, Sunil Rao, Vuda Sreenivasa Anitha Vijayalakshmi, B. Jadara University Faculty of Business Business Administration Department Jordan National Institute of Construction Management and Research Haryana Bahadurgarh India Maharaja Agrasen Institute of Management Studies Department of Business Administration Delhi India Manav Rachna University School of Management and Commerce Faridabad India Koneru Lakshmaiah Education Foundation Department of CSE A.P. Vaddeswaram India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of ECE Chennai India

ISBN: (纸本)9798331523893

The research proposes an AI-driven personalization framework for enhancing the efficiency and engagement of HR marketing campaigns. The system integrates real-time data processing, collaborative filtering, and feedback loops to dynamically recommend job opportunities tailored to individual candidate preferences. Using an HR-related dataset from Kaggle, the framework preprocesses data by handling missing values, scaling numerical features via Min-Max Scaling, and reducing dimensionality using Principal Component Analysis (PCA). Collaborative Filtering is employed to predict candidate preferences through matrix factorization, dynamically updating recommendations based on real-time user interactions. The experimental evaluation highlights the framework's superior performance compared to traditional methods. Click-Through Rate (CTR) improved significantly, with AI-driven personalization achieving 30% compared to 18% from rule-based methods and 12% from the baseline. Accuracy analysis revealed that Collaborative Filtering outperformed random and content-based methods, achieving the highest precision (85%), recall (88%), and F1-score (86%). These results underscore the effectiveness of AI in aligning candidate preferences with recruitment goals while ensuring dynamic adaptation through feedback. The proposed system also seamlessly integrates personalized insights into HR marketing channels such as job portals, email campaigns, and social media advertisements, optimizing recruitment processes and enhancing user engagement. Future research aims to extend the dataset to include diverse candidate profiles, incorporate multi-modal data sources, and leverage deep learning for improved recommendation accuracy. Additionally, explainability techniques will be explored to ensure transparency and build trust among users. This framework demonstrates the transformative potential of AI-driven personalization in reshaping HR marketing strategies. © 2025 ieee.

关键词： Convolutional Neural Network Cyber Security Multi-Layer Perceptron Network Anomaly detection Tuna Swarm Optimization

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The Application of Digital Empowerment Combined with intelligent Algorithms of Computer Technology in Decision Support systems

The Application of Digital Empowerment Combined with Intelli...

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Integrated Circuits and Communication systems (ICICACS), ieee International Conference on

作者： Wan Lu Li Chen Fei Gao Chang Wang Wuhan Windoor Information &Technology Co. Ltd Wuhan Hubei China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), this paper constructs a new IDSS framework aimed at improving decision-making efficiency and accuracy. In terms of research methods, large-scale distributed computing is performed in the cloud based on parallel computing technology; the logical reasoning of expert systems and the self-learning ability of graph neural networks are combined to achieve dynamic updates and efficient processing of data by unstructured knowledge bases. Compared with traditional DSS, the accuracy of IDSS based on the fusion of GNNs and ES in financial forecasting and risk assessment scenarios is improved to more than 90%, and resource utilization is significantly optimized. This study shows that the integrated application of intelligent algorithms has broad prospects in improving the dynamic adaptability of decision support systems and coping with complex decision-making environments.

关键词： Decision support systems Accuracy Heuristic algorithms systems architecture Graph neural networks Real-time systems Risk management Resource management Expert systems Optimization

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Formula One: Racing Intelligence in the Age of AI

Formula One: Racing Intelligence in the Age of AI

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), International Conference on

作者： R.K. Kavitha S. Kanishk Department of Computer Applications Kumaraguru College of Technology Coimbatore Department of Data Science Kumaraguru College of Liberals Arts and Science Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This study proposes an all-inclusive, data-driven system to optimize the performance of Formula One racing by incorporating state-of-the-art technology such as blockchain, edge computing, artificial intelligence, and real-time telemetry. The current systems of Formula 1 depend heavily on manual analysis and traditional data collection, thereby limiting the ability to make decisions in real time. The proposed system, however, incorporates high-resolution telemetry sensors with Nvidia Jetson edge computing devices, AI-driven prediction models, and secure blockchain protocols so that performance improvement during a race can be achieved even faster and more accurately. The key components are in the form of telemetry sensors for real-time data acquisition, edge computing for real-time processing, blockchain technology for secure data exchange and artificial intelligence models for predictive analytics. This method improves vehicle performance, adjusts to the race condition changes, and enhances general decision-making with an efficient, transparent, and scalable solution. The system architecture also accommodates future technological development as well as cooperation within the motorsport industry. The implications of the findings spread wider to driverless car industries and other data-dependent sectors, showing how blockchain and AI can change the game plan of racing and corporate formations.

关键词： Computational modeling Predictive models Real-time systems Sensor systems data models Blockchains Telemetry Artificial intelligence intelligent sensors Edge computing

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Cutting Force Prediction in Drilling Operations Using Temporal Convolutional Networks (TCN) and Spindle Speed data 3

Cutting Force Prediction in Drilling Operations Using Tempor...

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3rd ieee International Conference on Automation and Computation, AUTOCOM 2025

作者： Ganesh V, Dilli Ikram, Mohsin Husain, Saif O. Ballabh, Jayanti Mohanraj, T. Aanandha Saravanan, K. Department of Mechanical Engineering Chennai India School of Agriculture Uttaranchal University Dehradun India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

The prediction of forces in drilling is vital for enhancement of machining process, reduction of tool wear rates, and quality improvement of the final product. The present work introduces a new method for force estimation based on Temporal Convolution Networks (TCNs) and trained with spindle speed data. TCNs take advantage of the ability to process sequential data that perform well in capturing short-term and long-term dependencies, and hence are used in time-series analysis to analyze machining environments that are ever changing. The process is real-time acquisition of spindle speed and cutting force signals, filtering of signals to remove noise, and feature extraction for training of the models. To make correct predictions while making efficient computations, a TCN architecture that has been specifically customized with causal and dilated convolutions is used. The proposed model has MAE = 2.15 N and 98.7% inclusive squared coefficient of determination, which is significantly better than traditional machine learning models like ANN, SVM, and LSTM and faster on inference time. Indeed, all of the present model's validation has been done across different spindle speeds and for different types of materials. Also, the TCN model implementation into real-time monitoring allows optimizing the modelling parameters in proximity to changing conditions, which is a feature of industry 4.0. It also strongly supports the practice of sustainable manufacturing, which in turn improves the efficiency of machining. High value is credited to the potential of TCNs as a transformative tool in intelligent machining systems. © 2025 ieee.

关键词： Cutting

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advanced Modeling of Sodium-Ion Batteries for Electric Vehicles: Enabling Wireless Communication and intelligent Transportation systems

Advanced Modeling of Sodium-Ion Batteries for Electric Vehic...

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Wireless Africa Conference (WAC)

作者： Khanyisile Masemola Cuthbert Nyamupangedengu David G. Dorrell School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Department of Mechanical & Material Engineering University of Turku Turku Finland

ISBN: (数字)9798331517588

ISBN: (纸本)9798331517595

Sodium-ion batteries (SIBs) have emerged as a viable substitute for lithium-ion batteries (LIBs) in electric vehicles (EVs) due to the growing need for sustainable energy solutions. A comprehensive modeling framework for SIBs designed especially for electric vehicle applications is presented in this paper, with a focus on how these batteries integrate with wireless communication and intelligent transportation systems (ITS). The thermal and electrochemical models of SIBs, their possible use in vehicle-to-everything (V2X) systems, and the wireless state-of-charge (SoC) monitoring via real-time communication protocols are investigated. Simulation results demonstrated the efficiency of SIBs in vehicular applications, their compatibility with wireless data transfer protocols, and their contribution to energy optimization in ITS. The paper concludes with insights into the future of SIBs in EVs and wireless transportation ecosystems.

关键词： Wireless sensor networks Protocols Simulation Transportation Real-time systems Batteries Dedicated short range communication Vehicle-to-everything Optimization Testing

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Design and Simulation of an intelligent Traffic Management System Using Vehicular Networks

Design and Simulation of an Intelligent Traffic Management S...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： P. Tapan Datta D.Lalith Kumar S. Tanoj Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In this study , the reasearch is focused on the simulation of vehicular communication networks, especially intelligent traffic systems, to enhance dependability, safety, and optimality in communication. The simulation is performed using the OMNeT++ simulator and is integrated with the Veins framework that uses the ieee 802.11p protocol for V2V and V2I communications. Importing real-world traffic scenarios into the framework is obtained via the use of SUMO through TraCIScenarioManager. Other features of the framework include the definition of mobility models, dynamic obstacles, and detailed radio propagation modeling. TraCIDemo11p is used for mobility and communication by both RSUs and vehicles with applications in beaconing and channel switching. The whole framework analyzes smart traffic management solutions, providing as much insight into their performance based upon the communication reliability and overall efficiency in vehicular networks.

关键词： Protocols Vehicle-to-infrastructure Simulation Vehicular ad hoc networks Switches Radio propagation Safety Security Reliability Vehicle dynamics

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Research on the Application of Cloud computing-based Dual-Machine Hot Standby System in Railway Signal Safety Management

Research on the Application of Cloud Computing-based Dual-Ma...

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Integrated Circuits and Communication systems (ICICACS), ieee International Conference on

作者： Zhe Ji Ding Wang Bing Liu Yiming Liu China Academy of Railway Sciences (Shenzhen) Research and Design Institute Co. Ltd Shenzhen China China Academy of Railway Sciences Co. Ltd Beijing China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper investigates the application of cloud computing-based dual-computer hot standby computer security platform in railway signalling security management, aiming to enhance the reliability and security of railway signalling system. With the increasing importance of railway transport to modern society, the safety of railway signal system has become a key factor for public safety and social stability. This paper first analyses the security challenges faced by railway signalling systems and explores the potential of dual-machine hot-standby systems in improving system availability and security. Then, this paper details the design and implementation of a cloud-based dual-machine hot standby architecture, including system architecture, signal monitoring module, data encryption module, security audit module, and event response module. Through experimental validation, this paper compares the performance of the cloud-based dual-machine hot-standby architecture with that of the traditional single-machine system in terms of failure recovery time, resource utilisation and system response time, and the results show that the dual-machine hot-standby architecture has significant advantages in these key performance indicators. Finally, the paper discusses the limitations of the research and proposes directions for future research, including cost-benefit analysis, resource optimisation, technology adaptation research and intelligent development, with a view to providing a more efficient and intelligent solution for railway signal safety management.

关键词： Cloud computing Safety management systems architecture Computer architecture Rail transportation Stability analysis Security Reliability Resource management Time factors

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data Security Protection System in Human-Machine-Object Fusion intelligent Demand Engineering System

Data Security Protection System in Human-Machine-Object Fusi...

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Integrated Circuits and Communication systems (ICICACS), ieee International Conference on

作者： Yongjun Qi Chenggao Li Hailin Tang Junxuan Shen Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China School of Information and Communication Technology Mongolian University of Science and Technology Ulaanbaatar Mongolia

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In the engineering system of human-machine physical intelligence requirements, the multi-source and high complexity of data pose significant security risks. Therefore, this article designs a comprehensive data security protection system. Firstly, this article chooses a dynamic encryption strategy based on role-based access control. Then, this article combines differential privacy algorithm to construct a privacy protection model to ensure the privacy of data during sharing and processing. Subsequently, this article proposes a trusted data transmission protocol. Finally, this article introduces federated learning techniques to further improve the security and robustness of data in distributed scenarios. The experimental results show that the system performs well in terms of data security, privacy protection, and processing efficiency. Especially under high-intensity attacks, the system successfully detected and prevented approximately 98% of tampering. In the above data conclusions, the research method has significant advantages in encryption performance, achieving a better balance between privacy protection and data accuracy, demonstrating strong application prospects.

关键词： Differential privacy Protocols Federated learning data security Heuristic algorithms Robustness Encryption data communication Requirements engineering Protection

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Enhancing IoT-Driven Smart Home Security and Automation with a GCN Model

Enhancing IoT-Driven Smart Home Security and Automation with...

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Integrated Circuits and Communication systems (ICICACS), ieee International Conference on

作者： Sreelakshmi Ganti Ravi Kumar Vankayalapati P. Krishnamoorthy Pallavi S Thakare Ullal Akshatha Nayak P Vignesh Department of Electronics and Communication Engineering Geethanjali College of Engineering and Technology Medchal India Equinix Texas USA Department of Computer Science and Engineering Sasi Institute of Technology & Engineering West Godavari India Department of Information Technology Dr D. Y. Patil Institute of Technology Pimpri India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Mathematics KCG College of Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

A network of computing devices and sensors, the Internet of Things (IoT) rapidly exchanges data to assess circumstances and propose new services. intelligent home technologies rely on IoT. Contemporary smart home systems offer automatic lighting control, smoke detectors, temperature sensors, and smart lock installations. But these technologies raise serious privacy and security concerns. Security flaws let hackers access sensitive data, sendoff false alarms, and take control monitoring systems. Smart home technology is vulnerable to many threats because consumers are wary of it. Recommended steps are preprocessing, sensing, and model training. Preparing raw, unstructured data for analysis. DHT11 humidity and temperature sensors and HC-SR501 PIR motion detectors collect data. It trained the model using Multiscale DGCN data. Better than GCN and BiLSTM, Multiscale DGCN performs. It uses Internet of Things-based smart home security data well, with an average accuracy rate of 93.87%. Identifying outliers in sensor data solves major smart home security challenges. Increasing smart home system accuracy reduces security concerns and boosts user trust, which may drive adoption.

关键词： Temperature sensors Accuracy Bidirectional long short term memory Smart homes DH-HEMTs Safety Security Internet of Things Integrated circuit modeling intelligent sensors

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