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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Jayashree, S. Livedha, S. Ragupathi, T. Hariharan, Kumaran Sanjana, R. Sagar, Shrddha Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India Galgotias University Scse India

ISBN: (纸本)9798331508685

Social media platforms have become a critical space for businesses to interact with their audience. LinkedIn, a professional networking platform, offers metrics like reactions, comments, and shares to gauge post engagement. This paper investigates a data-driven approach for predicting the number of responses to a LinkedIn post. Various features such as post content, company statistics (e.g., followers and connections), and media type are analyzed using machine learning models. The study compares the performance of multiple regression models and explores the importance of features, yielding valuable insights for optimizing LinkedIn post engagement. © 2025 ieee.

关键词： Contrastive learning

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Quality Prediction of Mold Manufacturing using Support Vector Machine with Dynamic Lens Reverse learning based Spider Wasp Optimization 1

Quality Prediction of Mold Manufacturing using Support Vecto...

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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Hou, Weiping Yantai Vocational College Department of Intelligent Manufacturing Yantai China

ISBN: (纸本)9798331529246

Quality prediction of mold manufacturing contains data driven models and Machine learning (ML) method to determine and predict quality of molded products depending on process parameters. It assists in recognizing defects, optimize manufacturing conditions, and makes consistency in production. However, accurately predicting product quality is challenging because of inconsistent process parameters and defect variations which results inefficiencies and maintaining consistent production standards. This research proposes Support Vector Machine with Dynamic Lens Reverse learning based Spider Wasp Optimization (SVM-DLRLSWO) to predict the quality of mold manufacturing accurately. To fine-tune the SVM values, the DLRLSWO is utilized for addressing local optima issue which results in better convergence accuracy. Z-score normalization reduces outliers by standardizing data which ensures a consistent scale for process parameters and minimizes the extreme values impact. The proposed SVM-DLRLSWO obtains a high accuracy of 94.78% compared to existing methods like Decision Tree (DT) and Linear Regression (LR) respectively. © 2025 ieee.

关键词： Contrastive learning

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Retinal Disease Classification: Binary and Multiclass Perspectives 4

Retinal Disease Classification: Binary and Multiclass Perspe...

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Lanjewar, Rutuja Nitin Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331523923

Retinal diseases, among which diabetic retinopathy, age-related macular degeneration, and glaucoma account for the greatest number of blindness cases worldwide, affect more than 338 million people. It is essential that early detection leads to timely intervention to preserve vision, but it is limited due to the ever-increasing demand for ophthalmic care and a shortage of specialists. Machine learning-driven systems, especially Convolutional Neural Networks (CNNs), have shown great promise in overcoming these challenges, achieving high accuracy in medical imaging tasks. This paper explores a unified framework for both multiclass and binary classification of retinal diseases, addressing issues like class imbalance and inter-class similarity while leveraging CNNs for robust feature extraction. Techniques like Grad-CAM also increase model interpretability, allowing clinicians to understand and trust AI-driven diagnostic processes. © 2025 ieee.

关键词： Convolutional neural networks

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Identifying AC control Patterns From a Massive Longitudinal Log dataset Using Deep Clustering

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ieee ACCESS 2025年 13卷 56195-56209页

作者： Bae, Seunghwan Kim, Donghee Seong, Seokhwan Jang, Jaeyeon Suh, Young-Kyoon Kim, Honghyun Han, Dongwoo Jung, Inuk Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Seoul Natl Univ Hosp Biomed Res Inst Seoul 03082 South Korea LG Elect Home Appliance & Air Solut Co Air Solut Res & Dev Lab Changwon Si 51554 Gyeongsangnam D South Korea

An air conditioner is a household appliance and one of the most innovative inventions, primarily used to cool indoor temperatures and lower humidity to provide a comfortable environment. The sale of air conditioners increased continuously, reaching approximately 135 million units annually, with nearly 1.6 billion devices currently in operation. While it is important to devise and develop technologies for improving their energy efficiency, it is even more important to analytically investigate the performance of its main objective, user experience, or comfortableness. Here, we collected a massive set of logs collected in a longitudinal manner during the summer season of the year 2021 from approximately 1.47 million AC devices manufactured by LG Electronics and located in South Korea. Among the many features in the log, the current temperature, target temperature, humidity, energy consumption, and wind strength features were used to identify clusters embedding meaningful time-course feature patterns. We particularly focused on identifying patterns and user groups who either actively or inactively controlled the target temperature since such behavior likely indicates a discomforting air state. As a result, a total of 10 distinctive patterns and 14 representative user groups were searched. Among the 10 patterns, five showed active target temperature control and the other five inactive control. Regardless of control activity, all patterns are highly correlated with the progressing weather conditions during the summer. The feature patterns of the actively controlled group significantly differed from those of the inactively controlled group. Further investigation of the semantic difference between the groups would provide valuable research directions for improving the user experience of AC usage. It may also be used to devise an AI-driven automatic AC controller to minimize user intervention while providing an optimal air state.

关键词： Humidity Temperature measurement Indexes User experience Temperature distribution Object recognition Market research Home appliances Heating systems Focusing Air conditioner big data time series clustering deep learning

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Critical Analysis of Doctrine Application in Exceptional Cases for Crime Reduction Insights from Bangalore Law Practitioners using Hybrid DL-Optimization Model 3

Critical Analysis of Doctrine Application in Exceptional Cas...

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3rd International conference on Electronics and Renewable systems, ICEARS 2025

作者： Adithya, H.S. Kamalakhannan, S.K. Sundaram, Asha Saveetha Institute of Medical and Technical Sciences Saveetha School of Law Chennai77 India

ISBN: (纸本)9798331509675

The integration of data-driven technologies in legal systems offers transformative potential for optimizing judicial decisions. This study presents a Hybrid Deep learning-Optimization Model designed to analyze doctrine application in exceptional cases, with a focus on reducing crime through enhanced judicial insights. Leveraging annotated data from legal practitioners and court records in Bangalore, the model categorizes doctrines and predicts their applicability across diverse crime categories. Achieving an overall accuracy of 98.76%, the model demonstrated exceptional reliability in aligning predictions with judicial outcomes, particularly in violent crimes and institutional offenses. Metrics such as precision (98.91%), recall (98.72%), and F1-score (98.8%) highlight the model's balanced performance. Key findings revealed patterns in doctrine application, with doctrines like Self-Defense achieving 98.76% effectiveness in violent crimes. Expert surveys further validated the model's utility, with 96.43% of respondents supporting the use of data-driven tools in judicial processes. This research bridges the gap between conventional legal practices and advanced computational methodologies, offering a scalable solution for enhancing judicial efficiency and fairness. Future applications could extend this framework to broader legal systems and global datasets. © 2025 ieee.

关键词： Deep learning

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data-driven Processing using Parametric Neural Network for Improved Bluetooth Channel Sounding Distance Estimation

Data-driven Processing using Parametric Neural Network for I...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tsemko, Andrii Santra, Avik Kapshii, Oleg Pandey, Ashutosh Infineon Technologies IrvineCA United States Infineon Technologies Lviv Ukraine

ISBN: (纸本)9798350368741

Accurate device-to-device distance estimation is crucial for Internet-of-things (IoT) applications. Traditional methods, such as RSSI-based ranging and Time-of-Flight narrowband systems, exhibit limitations. Bluetooth Low Energy (BLE)-based phase ranging, aka Channel Sounding is a preferred technology, but existing approaches can be improved further. This paper proposes a novel approach using a parametric neural network to improve BLE channel sounding performance for device-to-device localization. Our neural network optimizes range estimation by learning from raw channel sounding data, outperforming traditional super-resolution algorithms. We demonstrate significant improvements in accuracy and precision through experimental results, with a reduction in root mean square error of up to 0.4m (indoor) and 0.04m (outdoor) and corresponding standard deviation reductions of up to 0.19m (indoor) and 0.02m (outdoor). This approach enhances device-to-device localization in IoT applications using BLE, enabling a wide range of IoT applications. © 2025 ieee.

关键词： Bluetooth Channel Sounding Deep Parametric learning Home Automation Indoor Localization Machine learning Range Estimation Smart Lock

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AI-driven Patient Monitoring in Smart Hospitals: A Survey on Edge Computing Integration and Applications 2

AI-Driven Patient Monitoring in Smart Hospitals: A Survey on...

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Ranjani, R. Naresh, R. SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chengalpattu Tamilnadu Chennai603 203 India

ISBN: (纸本)9798331505745

The integration of artificial intelligence (AI) and edge computing in healthcare is transforming patient monitoring systems, particularly within smart hospitals. These technologies enable real-time data processing, enhanced decision-making, and improved patient outcomes by shifting computational tasks closer to data sources, reducing latency, and alleviating the burden on centralized cloud infrastructures. This survey paper provides a comprehensive review of the current state of AI-driven patient monitoring systems in smart hospitals, focusing on the role of edge computing. It explores key applications, such as vital sign monitoring, early disease detection, and predictive analytics, while highlighting the benefits of edge computing, including real-time analytics, enhanced data privacy, and resource efficiency. Furthermore, the paper examines challenges in the deployment of AI and edge computing in clinical settings, such as data security, interoperability, scalability, and the need for robust AI algorithms tailored to healthcare environments. The survey concludes with a discussion of future research directions and potential advancements to further enhance AI-driven patient monitoring systems in smart hospitals. This review serves as a valuable resource for researchers and healthcare professionals aiming to understand the intersection of AI and edge computing in modern healthcare infrastructures. © 2025 ieee.

关键词： data Analytics

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A Self-Attention Enhanced Deep CNN-LSTM Based Irregular Surface Recognition Approach for Integration into Lower Limb Prosthesis systems to Ensure Safety through Predictive Walking

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ieee Access 2025年 13卷 92596-92610页

作者： Subari, Norazian Ghazali, Kamarul Hawari Ji, Yuanfa Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Campus Faculty of Electrical and Electronics Engineering Technology Pahang Pekan26600 Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Campus Centre for Advanced Industrial Technology Pahang Pekan26600 Malaysia National & Local Joint Engineering Research Center of Satellite Navigation Localization and Location Service Guilin541004 China

Advancements in instrumentation and control systems for lower limb prostheses have substantially improved mobility for amputees. However, significant challenges persist when users encounter irregular surfaces, as most prosthetic systems lack the capability to dynamically adapt to surface variations. This limitation restricts mobility, compromises safety, and diminishes user confidence and security during walking. To address these challenges, integrating inertial measurement units (IMUs) with artificial intelligence (AI) techniques, particularly deep learning (DL) methods, has emerged as a promising solution for surface classification and safety enhancement. This study proposes a self-attention enhanced deep CNN-LSTM model to automatically classify walking surfaces as regular or irregular, utilizing IMU acceleration data collected from prosthetic limbs. The model employs the strengths of convolutional and recurrent neural networks combined with a self-attention mechanism to enhance feature representation and improve classification accuracy. Experimental evaluations reveal that the proposed method achieves a classification accuracy of 99.94%, outperforming existing approaches. This result underscores the model’s potential to serve as the basis for AI-driven IMU-based systems, enabling real-time surface recognition and safety alerts in prosthetic devices. By enhancing walking safety and user confidence, this method represents a significant advancement for lower limb prosthesis systems. © 2013 ieee.

关键词： Convolutional neural networks

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Evaluation of English Online user learning Behavior based on Self-Attention with Fuzzy Neural Network 1

Evaluation of English Online user Learning Behavior based on...

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1st International conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Feng, Qiaoli Jiang, Hua Guangdong University Faculty of Foreign Language Studies Zhanjiang China Zhanjiang Public Security Bureau Zhanjiang China

ISBN: (纸本)9798331529246

Nowadays, online English learning platforms creates large amounts of behavioral data and while making it important to analyze user interactions for improved learning outcomes. Traditional models often fail to capture complex dependencies between behaviors with low interpretability and task-irrelevant data which are leading to inaccurate predictions of engagement and performance. This research proposed a Deep learning (DL) based model name called Self-Attention (SA) with a Fuzzy Neural Network (FNN) to prioritizes key behavioral patterns and handles uncertainty in learning activities. Initially, Coursera is employed for dataset and then data cleaning, padding is incorporated in pre-processing to convert the unstructured data to labeled data. Further, the features are selected through Recursive Feature Elimination (RFE) to eliminate unrelated features which decreases complexity and increases the model's capability to simplify to hidden data. Finally, the proposed SA-FNN model was measured using accuracy of 98.79%. Hence, the proposed model provides higher accuracy value when compared to Long Short-Term Memory (LSTM). © 2025 ieee.

关键词： Contrastive learning

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Smart Sanitation Management: Integrating IoT, AI-driven Analytics, and Automated Sensing for Efficient Public Toilet Maintenance 2

Smart Sanitation Management: Integrating IoT, AI-Driven Anal...

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Haripriya, K. Preethi, C. Hammed, S.Shahul Pavalarajan, S. Sharon, S. Akhil Suryakumar, S. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

An IoT-based self-sustained public toilet maintenance system aims to improve hygiene, efficiency, and resource management. It integrates smart sensors, microcontrollers, and cloud connectivity to monitor parameters such as water levels, air quality, and toilet usage in real-time. AI-driven predictive maintenance optimizes cleaning schedules, minimizes downtime, and ensures efficient resource use. Automated water control prevents wastage, while air quality sensors detect harmful gases and activate ventilation. A smart notification system alerts municipal authorities about supply shortages, enabling timely replenishment of water, soap, and tissues. By reducing manual intervention, the system lowers operational costs and improves service quality. Cloud connectivity allows seamless data processing, offering insights for better sanitation management. The integration of IoT and AI ensures proactive maintenance responses. A comparative analysis highlights the novelty of this system in automating and optimizing maintenance. © 2025 ieee.

关键词： Replenishment (water resources)

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