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The Internet of Healthcare Things (IoHT): Developing Patient Care and Medical Outcomes with Connected Healthcare Technologies

The Internet of Healthcare Things (IoHT): Developing Patient...

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

作者： Arunachalam, P. Yugandhar, Alli. Nag, D. Manesh Sri Reddy, P. Venkateswar VelTech Rangarajan Dr. Sugunthala R&d Institute of Science and Technology Department of Biomedical Engineering Avadi Tamilnadu Chennai India

ISBN: (纸本)9798331527495

Patient care and the outcomes of medical procedures are undergoing a transformation as a result of connected health care technologies. The Internet of Healthcare Things (IoHT), which is a subset of the Internet of Things (loT) that focuses on applications related to medical and health care, is the driving force behind this transition. The objective of this research is to analyze the development of technologies related to the Internet of Things (loT) and the impact that these technologies have had on the healthcare industry. To be more specific, the research studies how these technologies can improve patient monitoring, increase the effectiveness of treatment, and contribute to personalized medicine. Wearable technology, remote monitoring systems, and intelligent medical equipment are all subjects that are studied in this study, which focuses on clinical and home care settings. The study also concentrates on the usage of these technologies. Both the huge amounts of health data that are generated by devices connected to the Internet of Things (loT) and the role that data analytics and cloud computing play in the processing and interpretation of this data are analyzed as well. In terms of improving patient outcomes, lowering healthcare expenses, and increasing the efficiency with which healthcare is given, the research highlights the benefits of Internet of Health Technologies (loHT). In order to achieve this goal, a comprehensive analysis of case studies and applications that are grounded in the actual world is carried out. According to the findings, the Internet of Things has the potential to bring about a revolution in the healthcare industry. This opportunity arises from the fact that it makes it possible to implement proactive and preventive treatment approaches. Nevertheless, the research has also addressed the challenges that are associated with the Internet of Health Technologies (loHT). These challenges include issues over the privacy and security of data, as

关键词： Cloud computing in Medicine Connected Health care Technologies Equipment Healthcare Costs Patient Outcomes Personalized Medicine Remote Patient Monitoring smart Medical Wearable Devices

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Leveraging IoT and Machine Learning for intelligent Water Quality Monitoring Systems

Leveraging IoT and Machine Learning for Intelligent Water Qu...

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

作者： Baranwal, Gaurav Singh, Kaushtubh Gupta, Ayush Tiwari, Harsh N. Gupta, Tukur Ajay Kumar Garg Enginnering College Department of Electronics and communication Engineering Ghaziabad India

ISBN: (纸本)9798331527495

Water quality monitoring is a crucial aspect of public health, particularly in mitigating the risks associated with waterborne diseases and ensuring safe drinking water. Traditional monitoring methods are often labor-intensive, costly, and lack real-time capabilities, making them unsuitable for widespread and rural deployment. This paper examines the integration of advanced technologies, specifically the Internet of Things (IoT) and Machine Learning (ML), to design a sophisticated, low-cost water quality monitoring system. Leveraging an array of sensors, including pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), combined with the advanced embedded processor, the system ensures seamless data acquisition and processing. IoT enables continuous remote monitoring, while ML algorithms provide predictive analytics and intelligent anomaly detection to identify potential contaminants and trends. These value-driven solutions underscore the transformative potential of advanced technologies in revolutionizing water quality assessment. This review article carefully reviews the available literature on water quality assessment in order to lay out an assortment of conclusions on the concerns, problems, challenges, and research gaps that have emerged during the last few years. Research gaps are identified by thoroughly examining the previously suggested methodologies. The study presented in this article primarily identified the problems with the accuracy of the models, the generation of data collection mechanisms, and the types of data that were used in the suggested frameworks. Subsequently, research avenues towards smart water quality monitoring are offered by examining key concepts crucial to the expansion of this field of research. © 2025 IEEE.

关键词： Damage detection

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Enhancing Power Forecasting Through Contextual Awareness with C-Means and K-Means Approaches 21st

Enhancing Power Forecasting Through Contextual Awareness wit...

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21st international Symposium on Distributed computing and Artificial Intelligence

作者： Teixeira, Brigida Pinto, Tiago Vale, Zita Polytech Porto GECAD Res Grp Intelligent Engn & Comp Adv Innova LASI Intelligent Syst Assoc Lab R Dr Antonio Bernardino Almeida 431 P-4249015 Porto Portugal Univ Tras Os Montes & Alto Douro P-5000801 Vila Real Portugal INESC TEC P-5000801 Vila Real Portugal

ISBN: (纸本)9783031820724;9783031820731

This study delves into enhancing energy consumption forecasting in smart buildings by analyzing the implications of different contextual detection strategies, employing both K-Means and Fuzzy C-Means clustering techniques. Utilizing a comprehensive dataset from various sensors, the research segments power usage data to uncover distinct consumption patterns, extracted from user's behavior. A suite of seven machine learning forecasting algorithms undergoes evaluation, guided by two distinct methodologies: crafting specialized models for individual clusters and embedding contextual data as features within an integrated model. To perform the selection of the most suitable algorithm for each approach and cluster, an automated machine learning process is utilized, streamlining the optimization and comparison process. Moreover, the study explores the influence of soft clustering in providing nuanced, probabilistic cluster memberships and their impact on forecasting accuracy. A baseline scenario, devoid of contextual considerations, serves as a benchmark for comparison, allowing for a clear evaluation of the benefits introduced by integrating context into predictive models. The research aims to identify the optimal contextual detection strategy, considering the unique advantages of soft and hard clustering, to significantly improve energy consumption forecasts.

关键词： Contextual Awareness Fuzzy C-Means (FCM) K-Means Power Consumption Forecasting

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Traffic Incident Detection Algorithm based on Deep Learning: A Statistical Optimization Approach 3

Traffic Incident Detection Algorithm based on Deep Learning:...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jing, Zhao Heilongjiang Institute of Technology College of Automobile and Traffic Engineering Harbin150050 China

ISBN: (纸本)9798331509675

The continuous evolution of smart transportation has led to the vigorous development of research in the field of intelligent vehicle anomaly detection. This study proposes a new traffic incident detection algorithm based on deep learning and combines it with statistical optimization methods for real-time traffic incident monitoring. This model first uses the pre-trained YOLOv5m-c model to detect vehicles and outputs images with vehicle frames from the video stream. At the same time, from the data analysis level, it determines whether there is abnormal behavior between vehicles based on the characteristics of the vehicle frame. The proposed algorithm improves the effect of abnormal events by optimizing the network structure of the YOLOv5 model. Then, starting from the sensor level, the detection method based on the Internet of Vehicles collects and shares vehicle status information in real time. This analysis process fully considers the task offloading and migration strategy in the edge computing environment. Quantitative experimental results show that the proposed algorithm can effectively improve the detection accuracy of traffic incidents and reduce the false alarm rate in real-time traffic data. © 2025 IEEE.

关键词： intelligent systems

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Leveraging Optimization Techniques for Effective Arabic Query Expansion

Leveraging Optimization Techniques for Effective Arabic Que...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

作者： Allahim, Azzah Cherif, Asma College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia IT Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia The Center of Excellence in Smart Environment Research King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819614820

The primary goal of query expansion is to gather terms that are closely associated with the original query terms. To accomplish this goal, similarity measurements are used to evaluate the similarity between the query terms and the collected terms. Unfortunately, existing Arabic query expansion frameworks focus on expanding the query words individually. This approach can affect the expansion process for a complex language such as Arabic. In this paper, we investigate the effectiveness of applying an AI optimization algorithm to Arabic query expansion. The study aims to produce the optimal combination of terms by measuring the similarity of the collected terms against all query terms. We tested two intelligent swarm algorithms: the Cuckoo Search algorithm and Harmony search algorithm. Both algorithms were successful in enhancing the model’s precision and recall score with Cuckoo Search algorithm achieving the best performance of (97.5%) precision and (93%) recall. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Step-Wise Working Status for Next-Generation smart Cities: Integrating Fog and Edge computing Solutions

Step-Wise Working Status for Next-Generation Smart Cities: I...

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Visual analytics and data Visualization (ICVADV), international conference on

作者： L Sherin Beevi V. Nehru P. M. Joe Prathap W. Vinil Dani Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of Computer Science and Engineering Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai Department of EEE St. Peter's College of Engineering and Technology Avadi INDIA

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the fast changing environment of smart cities, combining fog and edge computing with powerful IoT devices like Hikvision cameras is revolutionising urban administration and public safety. Hikvision, a video surveillance systems pioneer, offers a variety of IP and analogue cameras, thermal imaging, and intelligent software platforms used for traffic management, public safety, environmental monitoring, and infrastructure security. These technologies, which combine high-definition video quality, advanced image processing, and cognitive analytics, enable real-time monitoring, event detection, and quick responses. Traffic cameras collect real-time data for vehicle counts and congestion management, while environmental sensors monitor air, water, and noise levels to aid in health warnings and pollution control. smart meters and wearable health gadgets monitor energy, water, and health indicators to optimise resource management. Parking sensors and waste management sensors boost parking and rubbish collection efficiency. The integration of fog and edge computing processes data locally, lowering latency and bandwidth utilisation, while fog nodes aggregate data for thorough analysis. Cloud storage enables long-term data analytics, which improves decision-making for city planners and emergency responders. Hikvision's software, which includes HikCentral and iVMS-4200, combines video surveillance, access control, and intelligent traffic management to create a comprehensive urban security framework. These systems are crucial to public safety because they provide real-time monitoring, facial recognition, and motion detection. For city planners, such innovations provide essential insights for urban growth and sustainability programs. The integration of Hikvision video surveillance with fog and edge computing is a game-changing approach to smart city management, improving urban safety and efficiency while improving inhabitants' quality of life.

关键词： Cloud computing smart cities Video surveillance Cameras Real-time systems Public security Internet of Things intelligent sensors Water resources Edge computing

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Step-Wise Working Status for Next-Generation smart Cities: Integrating Fog and Edge computing Solutions

Step-Wise Working Status for Next-Generation Smart Cities: I...

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2025 international conference on Visual analytics and data Visualization, ICVADV 2025

作者： Beevi, L Sherin Nehru, V. Joe Prathap, P.M. Dani, W. Vinil R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India St. Peter's College of Engineering and Technology Department of Eee Avadi India

ISBN: (纸本)9798331521394

关键词： Water pollution control

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Application of intelligent Techniques in smart Grid Automation

Application of Intelligent Techniques in Smart Grid Automati...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Manish Kumar Babu Papia Ray Anita Swain Department of EE VSSUT Burla India Department of ECE BCE Khordha India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The smart grid is an advanced power system network that integrates cutting-edge technologies to enable efficient, reliable, and sustainable energy generation, distribution, and utilization. This paper provides a comprehensive survey of intelligent techniques, including machine learning for load forecasting and fault detection, optimization algorithms for demand-side management and renewable energy integration, and data analytics for real-time grid monitoring and decisionmaking. These approaches enhance grid performance by improving flexibility, operational efficiency, and resilience to disruptions. Specific examples, such as predictive maintenance using machine learning and optimization-based energy scheduling for microgrids, are discussed to illustrate their practical applications. The paper also explores challenges like cybersecurity and data quality, offering potential mitigation strategies. Additionally, successful implementations and their outcomes are highlighted, showcasing the transformative impact of intelligent techniques on smart grid automation. Finally, future research directions, such as leveraging blockchain for secure transactions and quantum computing for advanced optimizations, are identified to guide advancements in this field.

关键词： Renewable energy sources Automation Demand side management data analysis Machine learning Real-time systems smart grids Optimization Resilience Predictive maintenance

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Energy Consumption Optimization using Big data analytics for smart Grid Management

Energy Consumption Optimization using Big Data Analytics for...

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Emerging Systems and intelligent computing (ESIC), international conference on

作者： K. Sumithra Devi Dipenkumar Contractor V. Deepa Lakshmi T K Santhikala M Kamati Mounika Department of Computer Science and Engineering KCG College of Technology Chennai India Department of Computer Engineering Government Engineering College Dahod India Department of Information Technology Sri Ramakrishna College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Science and Engineering Malla Reddy University Hyderabad Telangana India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Pharmacy Practice MB School of Pharmaceutical Sciences Mohan Babu University Tirupathi Andhra Pradesh

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The application of big data analytics to improve energy usage efficiency in smart grids has been prompted by the rising need for power management. Adding to the above, it poses a big data solution for demand prediction, load consolidation, and fault diagnosis. Establishing the new and state-of-the-art innovative LSTM network that was employed in the forecast yielded a higher demand forecast accuracy of 94.2% compared to the previous systems. The load balancing index achieved 92% with an accuracy of 93.7% in fault detection, thus minimizing maintenance cost by 14.7% while improving grid reliability. The proposed model was found to give better results than previous conventional machine learning techniques and even modern ones which are more complex, especially for an ever-changing grid environment. Overall, these findings show the promise of big data solutions for handling the challenges of the contemporary smart grid while improving its efficiency through energy optimization. Consequently, this work lays a framework for subsequent studies on the remaining applications of advanced analytics in energy management systems.

关键词： Fault diagnosis Accuracy Fault detection Big data Load management Real-time systems smart grids Maintenance Optimization Load modeling

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Leveraging Large Language Models for smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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