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IoT-Enabled Smart System for Personalized Health Management with Semantic technologies

IoT-Enabled Smart System for Personalized Health Management ...

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Madhavi, Arelli Shnain, Ammar Hameed Keerthana, S. Marimuthu Gayathri, B. Vinoth Kumar, V. Gokaraju Rangaraju Institute of Engineering and Technology Department of Aiml Telangana Hyderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Information Technology Pulloor Tamil Nadu Kariapatti626115 India Cmr College of Engineering & Technology Department of Cse Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331527495

The current world could be a critical representation of the exceptionally speedy improvement of electronic and data innovation. Wellbeing frameworks are not exempt from the consistent changes happening within the industry. For analysts, specialists, and patients, all these modern innovations are opening up a world of unused potential and challenges. The Web of Things is one of the advances that has been brought into the healthcare industry with extraordinary guarantee. These days, a parcel of restorative contraptions and sensors are associated to the Web of Things, giving shoppers real-time control over their wellbeing and specialists the capacity to appropriately screen their patients' conditions from a separate. The failure to effectively coordinated approaching information from heterogeneous sources, share information collected viably and productively whereas keeping up its security and protection, extricate valuable data from this information utilizing proficient information examination apparatuses, and perform expressive and customized visualizations are right now hindering the effective accomplishment of tall quality in wellbeing care hone. We too present the SM-IoT stage, an cleverly, internet-of-things-based, and customized health care stage energetic to progress farther understanding observing in arrange to supply patients with easier-to-access and more successful healthcare. The objective of SM-IoT is to assemble information from different sources of data, get it from relevant sources, combine it employing a adaptable semantic web, store it within the cloud for extra analysis, visualize it utilizing user-friendly interfacing, and make sharing of the information less demanding whereas keeping protection in mind. The wellbeing care division has changed to supply more quick and individualized restorative medicines much obliged to the quick growing Web of Things innovation. This particular study presents a novel Shrewd IoT stage that employments semantic advan

关键词： Personalized medicine

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AI and ML Synergies in Construction and Cybersecurity: A Scientometric Perspective

AI and ML Synergies in Construction and Cybersecurity: A Sci...

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Intelligent data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Rajesh Anandhan Hari Sanjay S Biju Balakrishnan S Pavithra Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India CSE Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

AI and particularly ML have significantly transformed the way construction projects are executed through handling some of the issues that include; delays, high costs and safety. These technologies ensure the efficient use of the resources reduce incidences of security threats and organization of projects. In the cybersecurity field, applications of AI and ML have changed from defence strategies only to such ones that allow finding possible threats and precautions. Framing is achieved through the application of early warning systems such as anomaly detection, Predictive models, and real-time monitoring that counter possible threats on core structures. Nevertheless, while using AI, there are emerging inconveniences such as ethical dilemmas, ambiguous operational occurrences, a vulnerability to malicious attacks, as well as a lack of clear visibility when working. However, thanks to the versatility of the AI and ML approaches in transferring solutions from one industry to another, the difficulties can be overcome. Systematic literature reviews and cooperative frameworks add to this realization by unveiling the scale at which AI applications integrate across domains while stressing on the adventitious nature of AI. The progressive development of these technologies in day-to-day applications indicates that while they provide a means for increasing efficiency and security with experience comes new questions and debates about ethical and better technical advances.

关键词： Industries Ethics Quantum computing Bibliometrics Predictive models data models Safety Security Artificial intelligence Computer security

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A Hybrid Approach for Short-Term Wind Speed Prediction Integrating Temporal Convolutional Neural Network with Covn1D and Gaussian Process Regression 1

A Hybrid Approach for Short-Term Wind Speed Prediction Integ...

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1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Ye, Song Zhu, Shutao Chen, Jingming Tong, Zhong Wang, Piao Gu, Shixiang Hu, Jinghui Zhang, Qiuwen Changjiang River Scientific Research Institute Changjiang Water Resources Commission Wuhan China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China Yunan Institute of Water & Hydropower Engineering Investigation Design and Research Kunming China Branch of Information Technology and Automation Hubei Water Resources Research Institute Wuhan China

ISBN: (纸本)9781643685779

Because of the characteristics of randomness and fluctuation, it is very difficult to predict the short-term wind speed. In this paper, we proposed a hybrid approach that integrates temporal convolutional neural network (TCN) with one-dimensional convolutional (Covn1D) and Gaussian process regression (GPR). The Covn1D is adopted to extract the period and local detail features of time series data, the TCN is utilized to excavate their deep rules, and the GPR is applied in the model training so as to obtain the results of point and interval prediction. A case study shows that compared with other traditional models, the Covn1D-TCN-GPR hybrid model proposed in this paper can obtain both the best point prediction accuracy and the highest interval prediction reliability. © 2025 The Authors.

关键词： Wind forecasting

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Enhancing Cloud Security Implementing AI-Based Intrusion Detection Systems

Enhancing Cloud Security Implementing AI-Based Intrusion Det...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Vinoth Thoguluva Santharam Akhil Raj Gaius Yallamelli Mohammed Alaa H. Altemimi Mahesh Prabu Arunachalam Shukla Rudrank Gauri Ghule Data Analytics Organization-Comcast Chennai India Amazon Web Services Inc Seattle USA Department of Information and Communication Engineering Al-Khwarizmi College of Engineering The University of Baghdad Baghdad Iraq Department of Information Technology and Computer Science Austin Texas United States Kalasalingam Academy of Research and Education (Deemed to be University) Tamilnadu Department of E and TC VIIT Pune India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The increasing complexity of assaults necessitates the use of innovative intrusion detection systems (IDS) to safeguard critical assets and data. There is a higher risk of cyberattacks like data breaches and unauthorised access since cloud services have been used more frequently. The project's goal is to find out how Artificial Intelligence (AI) could enhance the IDS's ability to identify and classify network traffic and identify anomalous activities. Online dangers could be identified with IDS. An intrusion detection system, or IDS, is required to keep networks secure. We must create efficient IDS for the cloud platform as well, since it is constantly growing and permeating more aspects of our daily life. However, using standard intrusion detection systems in the cloud may provide challenges. The pre-established IDS design may overburden a cloud segment due to the additional detection overhead. Within the framework of an adaptively designed networked *** demonstrate how to fully use available resources without placing undue load on any one cloud server using an intrusion detection system (IDS) based on neural networks. To even more successfully detect new threats, the suggested IDS make use of neural network machine learning (ML).

关键词： Accuracy Cloud computing security Neural networks Intrusion detection Telecommunication traffic Feature extraction Security Servers Standards Testing

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Sentiment Detection in Financial News: A Deep learning Approach to Extreme and Moderate Classification

Sentiment Detection in Financial News: A Deep learning Appro...

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Intelligent and Innovative technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Amey Didolkar Prasad Lokulwar Computer Science and Engineering G H Raisoni College of Engineering Nagpur Nagpur India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Today’s financial industries need precise, directed news analysis with sentiment identification more than ever in order to forecast possible future moves. This research focuses on developing a robust system for sentiment detection in financial news using a deep learning approach, enabling both extreme and moderate. The data collection has been ensured through the use of web scraping to gather data from multiple reliable financial news sources. To reliably forecast the attitudes encoded in the news items, this dataset has been processed using state-of-the-art deep learning algorithms. The latest technologies in deep learning have been employed, and a comparative analysis of different models has been conducted to identify the most effective one of this specific task. Providing an easy-to-use interface that allows users to obtain news from many sources and their corresponding sentiment analysis is the main goal of this research. With the help of this technology, stakeholders should be able to make well-informed decisions by using the sentiment trends found in financial news. By providing the effectiveness of deep learning models in sentiment identification, the system not only improves user experience by offering sentiment-based news insights but also advances the field of financial news analysis. The deep learning model that was constructed has demonstrated encouraging results in reliably classifying attitudes through extensive testing and evaluations, indicating that it has the potential to be an instrumental tool for financial analysis. For future research in the fields of sentiment identification and financial forecasting, this study establishes a precedent and emphasis the significance of incorporating cutting-edge technologies into news analysis.

关键词： Deep learning Analytical models Sentiment analysis Accuracy Computational modeling Transformers User experience Reliability Stakeholders Testing

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Wearable Assistive Device for Enhanced Navigation in Visually Impaired Individuals Using YOLO-Based Object Detection

Wearable Assistive Device for Enhanced Navigation in Visuall...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Abhishek S. Rao Ramaprasad Poojary Karthik Pai B H Rohan Arya A R Utkarsh Harsh Kumar Darshan S H Nagesh Shenoy Department of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte India School of Engineering and IT Manipal Academy of Higher Education Dubai Campus Dubai United Arab Emirates Department of Computer Science and Engineering Canara Engineering College Benjanapadavu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The proposed study addresses the pressing challenge of enabling visually impaired individuals to navigate their surroundings with enhanced safety and autonomy. To this end, we propose a wearable assistive device designed to improve spatial awareness and facilitate environmental interaction. The primary objectives include developing an accurate object detection system, integrating real-time distance measurement capabilities, and incorporating situational awareness through weather data integration. The study methodology employs cutting-edge computer vision and machine learning techniques, with a YOLO-based object detection model at its core. This model ensures robust identification of objects, achieving an impressive 97% precision and recall. To support safe navigation, real-time distance measurement functionality was implemented. Additionally, the device incorporates real-time weather data via the Open Weather API, delivering comprehensive updates on temperature, precipitation, and wind conditions to assist in user decision-making. Design considerations focused on user-centric aspects such as battery life, portability, and cost-effectiveness to ensure practicality and accessibility. Evaluation results demonstrated an average mAP of 98.34% at IoU 0.5 across all object classes, with some reaching up to 99.50%. User testing validated the system's efficacy, achieving a 95% success rate in object identification and spatial guidance. These findings underscore the device's potential to enhance independence, safety, and quality of life for visually impaired users, promoting inclusivity through advanced assistive technology.

关键词： Navigation Computational modeling Estimation Object detection Real-time systems Distance measurement Safety Object recognition Assistive devices Wearable devices

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Adversarial Artifact Removal: A GAN-Based Framework for Enhanced Image Quality and Reliability

Adversarial Artifact Removal: A GAN-Based Framework for Enha...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Pournima Pande M. Misba Deepak Gupta V.Kanpur Rani Hassan Safi Ahmed A. Balakumar Department of Applied Chemistry Yeshwantrao Chavan College of Engineering Nagpur India Department CSE (AI&ML) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of EEE Hindustan Institute of Technology and Science Chennai India Department of Biomedical Engineering Al-Farahidi University Baghdad Iraq Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Artifacts in images are significant challenges in many fields and degrade the quality and reliability of data analysis. Existing methods for artifact removal face a problem of complex and diverse artifacts, limiting their effectiveness. Traditional techniques for removing artifacts have a problem with low adaptability to different types of artifacts and can sometimes delete important features of an image. The artifact removal method takes the concept of GANs into use to remove artifacts within the adversarial training model from images. It rectifies the issues stated previously through the proposed approach based on training a GAN. The G learns artifact-free versions of the images and the D tells them clean or corrupted ones while the G continues its progression towards artifact removal guided by D. Once it was trained, G had completely removed artifacts in novel unseen images. Results prove the superiority of this proposed artifact removal compared with existing approaches across a spectrum of datasets including different forms of artifacts: noise compression, and occlusion results of removing such different kinds of artifacts without sacrificing much the image quality along with preserving other critical features that need removal. Adversarial removal artifacts can be taken as one such error value 112.31 solution in making an improved version of enhancing quality as well as reliability of the image while making applications over the range.

关键词： Training Image quality Deep learning Limiting Image coding data analysis Noise Generative adversarial networks Software reliability Faces

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Enhancing Loan Default Prediction with Human-in-the-Loop and XGBoost Ensemble Learning

Enhancing Loan Default Prediction with Human-in-the-Loop and...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Shamim Ahmad Khan Rahama Salman Al-Hussein Maysir Majid M. Mythili Malik Bader Alazzam Ponni Valavan M Department of ECE Glocal School of Science & Technology Glocal University U.P India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan KSA Department of Biomedical Engineering Al-Farahidi University Baghdad Iraq Department of English Nandha Engineering College(Autonomous) Erode India Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Human-in-the-Loop (HITL) Machine Learning with Ensemble Learning uses the best of both automated algorithms and human input in making predictions and decision making when it comes to Machine Learning (ML). In HITL frameworks they include human inputs in model training and validation, allowing the resultant models to be built based on actual needs rather than white algorithms, which may be insensitive to some details otherwise. It is more relevant in such areas or applications that involve higher risk such as credit risk applications in today's financial world where predictions form the basis for major loans' approval. Although previous studies that employed fully automated ML models have some shortcomings that include being prone to giving biased predictions, being less interpretable and scalability issues and failure to handle edge cases hence reducing reliability and trustworthiness. To overcome these problems, this study introduces a new approach of combining the HITL with the XGBoost, which is one of the most potent ensemble learning algorithms to develop a balanced model of loan default prediction. Through the analysis of loan data from Lending Club, this method has attained an accuracy of 99.4% along with a high precision and a high recall, thus pointing to well-rounded performance of the model. Expert knowledge in the form of the HITL loop improves the regularization and optimization of the XGBoost model leading to a better, efficient, reliable, and more interpretable model for credit scoring. It was in python the model was developed taking advantage of the powerful ml and data analysis libraries which make it easy to develop, scalable and reusable for real life organizations especially in the financial arena where precision and accuracy are paramount. This approach lays some basis for subsequent innovations in the ICT applications in HITL ML systems in different fields.

关键词： Training Analytical models Machine learning algorithms Accuracy Predictive models Prediction algorithms Human in the loop data models Ensemble learning Reliability

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A Deep CNN Self-Attention Model for Multidimensional Speech Quality Prediction Using Crowdsourced datasets

A Deep CNN Self-Attention Model for Multidimensional Speech ...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Annapurna Gummadi Chamandeep Kaur Deepak Gupta Vuda Sreenivasa Rao Santosh Tripurana P. Nagaraj Department of CSE (Data Science) CVR College of Engineering Hyderabad India Computer Science & Information Technology Department Jazan University Jazan Kingdom of Saudi Arabia Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Dept of ECE Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamil nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Predicting voice quality is critical for improving communication systems and the user experience. In this article, we offer a unique technique to multidimensional speech quality prediction using a Deep Convolutional Neural Network (CNN) enriched with self-attention techniques and crowdsourced datasets. The proposed model incorporates both spatial and temporal information from speech signals via numerous convolutional layers, enabling it to accurately represent the intricate patterns and features seen in speech data. Furthermore, self-attention methods are used to let the algorithm to focus on relevant parts of the input data, increasing its discriminative capacity and tolerance to interference. Transfer learning is utilized to leverage pre-trained representations from large-scale speech datasets, enabling the model to generalize well to diverse speech characteristics and environments. By fine-tuning the pre-trained model on the specific crowdsourced dataset, we adapt it to the task of speech quality prediction while mitigating the need for large annotated datasets, which are often costly and time-consuming to obtain. Experimental results demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance on various metrics for multidimensional speech quality prediction tasks. Furthermore, extensive ablation studies are conducted to analyze the contributions of different components of the model, highlighting the importance of both the CNN architecture and self-attention mechanisms. Overall, our findings suggest that the proposed deep CNN self-attention model, combined with transfer learning from crowdsourced datasets, presents a promising approach for accurate and efficient speech quality prediction in diverse real-world scenarios

关键词： Measurement Adaptation models Convolution Computational modeling Transfer learning Interference Predictive models User experience data models Convolutional neural networks

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AI-Enabled Muscle Oxygen Sensors for Personalized Exercise Intensity Monitoring and Recovery Analysis

AI-Enabled Muscle Oxygen Sensors for Personalized Exercise I...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： K. Balachander K. Balasubadra Bharat Tidke Sree Southry S B Meenakshi S. Murugan Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Puduvoyal Chennai Tamil Nadu India Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosisinternational (Deemed) University Pune India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Through the integration of artificial intelligence (AI) with muscle oxygen sensors, this work presents a unique way to exercise intensity monitoring and recovery analysis. Subjective assessments or heavy equipment are the backbone of the traditional approaches to evaluating exercise intensity and recovery. Using the latest developments in AI and wearable sensor technology, our system offers real-time personalized feedback to athletes and fitness enthusiasts. Our Muscle oxygen sensors were vigorously tested to ensure their precision and performance. Wearing the sensors, participants varied their workout regimen while the sensors sent data on the participants' muscle oxygen levels to an AI program. According to the results, the algorithm could correctly determine workout intensity levels and establish individual recuperation timeframes. Moreover, the AI system learns the user's physiological traits and can personalize recommendations and training programs for everyone. Our approach may help improve athletic performance, avoid overtraining injuries, and maximize fitness results by giving practical advice on recovering from exercises at the right intensity.

关键词： Training Medical services Muscles Sensor systems Real-time systems Sensors Artificial intelligence Biomedical monitoring Monitoring Injuries

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