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Big data analysis for the Asian region’s smart grid

Big data analysis for the Asian region’s smart grid

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Communication, computing and Energy Efficient Technologies (I3CEET), international conference on

作者： Ashish Sharma Rinku Garg Jaspreet Kaur Mohammed Al-Farouni Sanjeev Kumar Shah Protyay Dey Department of Electrical Engineering Chandigarh University Mohali India University Institute of Computing Chandigarh University Mohali India Department of CSE Chandigarh Engineering College Chandigarh Group of Colleges Mohali Department of CSE College of Technical Engineering The Islamic University Najaf Iraq Department of ECE Uttranchal Institute of Technology Uttaranchal University Dehradun India NIMS School of CSAI NIMS University Jaipur Rajasthan India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Big data analytics integration with smart grid systems has become an important field of study with previously unheard-of potential to improve the sustainability, dependability, and efficiency of contemporary power networks. This study explores the various uses of big data analytics in smart grids, with an emphasis on gathering, analysing, and interpreting enormous datasets produced by various grid components. Predictive maintenance, demand forecasting, and real-time monitoring and control using sophisticated analytics are important research fields. The study investigates how smart grids may optimise energy use, proactively reduce possible faults, and dynamically adapt to changing conditions by utilising machine learning algorithms and data-driven models. The study also looks into the cybersecurity ramifications of handling and protecting the enormous inflow of data, guaranteeing the confidentiality and integrity of vital grid data. In addition to acknowledging the revolutionary potential of big data analytics in smart grids, the study tackles issues including data interoperability, scalability, and the requirement for standardised protocols. The results foster a better comprehension of how big data analytics will influence smart grids in the future and open the door to more intelligent, resilient, and sustainable energy ecosystems.

关键词： Protocols Electricity Biological system modeling Ecosystems Big data Real-time systems smart grids Telecommunication computing Computer security Sustainable development

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Developing a SVM Model of Big data analytics for Healthcare Recommendation System

Developing a SVM Model of Big Data Analytics for Healthcare ...

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international conference on intelligent Systems and Sustainable computing, ICISSC 2022

作者： Shailaja, M. Lanka, Lavanya Soma, Krushima Kumar, E. Shravan Prashanthi, B. Malla Reddy University Hyderabad India L.A. Care Health Plan Los Angeles United States Department of CSD Malla Reddy Engineering College and Management Science Hyderabad India

ISBN: (纸本)9789819947164

Today, the data world that rules us made its mark not only in computer science but also in other academic sectors like medicine, finance, news, and marketing, among others. In today’s world, the healthcare business is an important industry, where large volumes of data are constantly collected. Such data have the features of big data and involve analyzing it and uncovering latent relationships between variables in healthcare data. The concept of big data, which has transformed the world of traditional data into a world of digital data, was developed as a result of the quick and large data generation by numerous applications. Big data analytics is an advanced method for analyzing enormous, heterogeneous datasets, including structured, unstructured, and semi-structured data of various sizes obtained from various sources, in order to find patterns, connections, and other insights that were not originally recognized. Healthcare is one of its many applications, in addition to banking, communication, social media, education, social services, commerce, and shipping. It offers medical services like telemedicine, electronic health records (EHRs), and other types of treatment to evaluate patients and increase staff employment. In this analysis, the suggested strategy for creating an SVM model of big data analytics for healthcare recommendation system is described. The performance of the suggested algorithms is calculated on different aspects such as accuracy, precision, and F1-score. Hence, in this analysis, the presented SVM model of big data analytics will yield the best results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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intelligent Decision-making Algorithm for IoT Enabled smart shopping

Intelligent Decision-making Algorithm for IoT Enabled Smart ...

引用

international conference on Communication, computing and Internet of Things (IC3IoT)

作者： J Mythili S T Menmoli S Dhanusuya M Sethuram Department of Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

In today’s fast-changing retail industry, IoT and data-driven analytics have changed consumer buying and business operations. In IoT-enabled smart shopping environments, intelligent decision-making algorithms are crucial. This paper presents an intelligent decision-making system for IoT-enabled smart purchasing. The program improves various shopping aspects using real-time data from IoT sensors, RFID tags, and cameras. The algorithm includes data integration, advanced analytics, decision optimization, real-time adaptation, and customization. The programme provides actionable information from customer behaviour, product inventories, and environmental elements to aid decision-making. These choices include inventory management, pricing, product recommendations, and targeted promotions to enhance the shopping experience. The real-time algorithm adapts to demand, inventory, and client preferences. Personalized recommendations and promotions boost retailer revenue and consumer engagement. The algorithm streamlines inventory management, improving efficiency, cost, and retail competitiveness.

关键词： Industries Decision making Pricing Inventory management Real-time systems Telecommunication computing Internet of Things

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YOLO-based Object Detection in Panoramic Images of smart Buildings 10

YOLO-based Object Detection in Panoramic Images of Smart Bui...

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10th IEEE international conference on data Science and Advanced analytics, DSAA 2023

作者： Pokucinski, Sebastian Mrozek, Dariusz Silesian University of Technology Department of Applied Informatics Gliwice Poland

ISBN: (纸本)9798350345032

Collecting and analyzing spherical images is one of the crucial fields supporting the digitization of indoor environments, like houses, apartments, offices, or factories, and creating virtual tours of smart buildings. Such images also constitute an important feed for smart indoor devices, like autonomous vacuum cleaners, intelligent production lines, or assistive droids. However, smart devices must correctly identify internal objects on high-resolution spherical images, usually heavily distorted and made with variable lighting conditions. Moreover, the recognition task must be relatively accurate and take place onboard the device, as data transmission to larger analysis centers or workstations does not allow for real-time operation. In this paper, we compare two object detectors from the YOLO family (YOLOv5 and YOLOv8) on the publicly available dataset adjusted to a newer annotation representation that allows for adapting YOLO detectors to spherical images. We verify the effectiveness of these two families of detectors for varying sizes of object detection models, image sizes, and training batch sizes. Our research proves that YOLO models can successfully detect most indoor objects, and their effectiveness is comparable to dedicated detectors having much higher computational complexity. © 2023 IEEE.

关键词： Object detection

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Adaptation of Fitness Application Through AI Integration: An Android Based Application for Home Workout Fitness Experience

Adaptation of Fitness Application Through AI Integration: An...

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2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Daroya, Renz C. Mercado, Mark Anthony B. Dela Cruz, Donnabel C. Sison, Ariel Antwaun Rolando C. Centeno, Criselle J. Marcelo, Erwin D. College of Information System and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (纸本)9798331543617

The 'iGym' fitness application is a user-friendly platform created to assist gym beginners, users who prefer to workout at home, gym enthusiasts, and gym instructors in determining the suitable workout for different body types. Many beginners struggle because they lack guidance on workouts, diet, and exercise intensity. This study aims to bridge that gap by utilizing predictive analytics to offer personalized workout routines and diet recommendations. By analyzing a user's past performance, physical attributes, and other relevant data, the app can forecast the outcomes of specific workouts, leading to tailored exercise and nutrition plans. These customized approaches enhance fitness efficiency, minimize injury risks, and promote overall health. Additionally, a progress tracker will motivate users by showing their achievements toward their fitness goals. The developers will use Multi-Linear Regression (MLR) to predict results based on users' consistent efforts, providing actionable insights for workout and diet adjustments. Users will also have the option to upload photos for visual progress tracking using TensorFlow. To boost engagement, the app will feature a rewards system that incentivizes users upon reaching goals while recommending appropriate food options to align with their fitness objectives. The approach has been successful in correctly identifying different proper workouts and calorie intake for each body type. iGym has proved its ability to appropriately recommend and predict through thorough testing. This achievement demonstrates TensorFlow's usefulness in this setting while also validating the system's design and implementation. While effective, the researchers believe that utilizing algorithms created specifically for complicated visual data could result in even greater accuracy. © 2024 IEEE.

关键词： Predictive analytics

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ICDAR'22: intelligent Cross-data Analysis and Retrieval 22

ICDAR'22: Intelligent Cross-Data Analysis and Retrieval

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ACM international conference on Multimedia Retrieval (ICMR)

作者： Dao, Minh-Son Riegler, Michael Alexander Duc-Tien Dang-Nguyen Gurrin, Cathal Nakashima, Yuta Dong, Mianxiong Natl Inst Informat & Commun Technol Tokyo Japan Simula Metropolitan Ctr Digital Engn Oslo Norway Univ Tromso Tromso Norway Univ Bergen Bergen Norway Kristiania Univ Coll Bergen Norway Dublin City Univ Dublin Ireland Osaka Univ Osaka Japan Muroran Inst Technol Muroran Hokkaido Japan

ISBN: (纸本)9781450392389

We have witnessed the rise of cross-data against multimodal data problems recently. The cross-modal retrieval system uses a textual query to look for images;the air quality index can be predicted using lifelogging images;the congestion can be predicted using weather and tweets data;daily exercises and meals can help to predict the sleeping quality are some examples of this research direction. Although vast investigations focusing on multimodal data analytics have been developed, few cross-data (e.g., cross-modal data, cross-domain, cross-platform) research has been carried on. In order to promote intelligent cross-data analytics and retrieval research and to bring a smart, sustainable society to human beings, the specific article collection on "intelligent Cross-data Analysis and Retrieval" is introduced. This Research Topic welcomes those who come from diverse research domains and disciplines such as well-being, disaster prevention and mitigation, mobility, climate change, tourism, healthcare, and food computing.

关键词： cross-data analysis privacy-preserving prediction model time-series data forecast cross-modal models image clustering and segmentation social multimedia hidden knowledge reveal transformer federated learning

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A New Approach to Information Literacy in Higher Education in smart Learning Environment 5

A New Approach to Information Literacy in Higher Education i...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Anjanee Kumar, B. Kar, Rohit Sunalini, K.K. Jain, Kaneenika Girdharwal, Nitin Shah, Mohd Asif Dadi Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Anakapalle India St. Claret College Department of Management Bangalore Karnataka India Koneru Lakshmaiah Education Foundation Department of English Andhrapradesh Guntur India S. S. Jain Subodh Management Institute Rajasthan Jaipur India Faculty of Management Madhya Pradesh Indore India University School of Business Chandigarh University Gharuan Punjab Mohali India

ISBN: (纸本)9798350321425

In light of the current epidemic, it is crucial that children acquire better literary skills. That blended learning is considered as one of the most cutting-edge pedagogical strategies. The goal of blended learning is to develop a new learning environment that combines traditional learning with modern methods like e-learning, analytics, game-based learning, and open educational materials in order to improve learning effectiveness and enrich the learning experience. Meanwhile, the idea of smart learning environment has developed with a number of successful implementations as a significant and usable product of smart environments research. This research aims to show that the notion of mixed learning offers a positive outlook for the incorporation of emotional learning methodologies into the increasingly prevalent smart learning environments. Here, the first smart learning environment-based paradigm for enhancing students' information literacy. The outline the components of the intelligent learning environment for the development of students' information literacy based on reviews of the relevant literature and results from prior studies. Researchers found that blended learning increased students' engagement throughout the learning process, which led to better grades and deeper understanding. In addition, this approach could address the drawbacks of both online and in-person instruction, realize big data recording and representation of pedagogical, instructional, and curricular behavior, fully exploit instructors' potential in their various roles, and accurately reflect students' actual knowledge gain. © 2023 IEEE.

关键词： Students

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Advances and Challenges in Automated Agriculture: Integrating 5G Wireless Technology with IoT, Cloud computing and Robotics 5

Advances and Challenges in Automated Agriculture: Integratin...

引用

5th international conference on Electrical, Communication and Computer Engineering, ICECCE 2024

作者： Li, Xinwei Kang, Chia Chao Ng, Zi Neng Minhad, Khairun Nisa' School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798331529437

The agricultural sector is undergoing a transition as automation and robotics converge and evolve. The current labor shortage and the growing need to increase yields are driving innovations in automated agriculture. Automated systems are gradually replacing traditional farming that relies on manpower. The Internet of Things (IoT) with 5G technology has become the key to these traditional automated irrigation systems. The system collects data from soil moisture, rainfall and temperature sensors thus uploads it to the cloud to store data and analyze it to make intelligent irrigation decisions. The proposed innovative system uses iterative learning algorithms and training models to identify pests, diseases and flower types. This article discusses in depth the model evolution of automation technology in the agricultural sector, IoT and cloud computing applications in automated agriculture and the growing use of robotics in agricultural operations. Besides that, it also discusses future directions and challenges. In addition, this article explores the potential for cross-platform collaboration and data sharing to advance the progress of this automation model. However, the field still faces challenges due to the decentralized nature of technology development. © 2024 IEEE.

关键词： smart agriculture

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Cumulative Belief Rule-Based Expert System for Multi-Resident Activity Recognition in smart Home 18

Cumulative Belief Rule-Based Expert System for Multi-Residen...

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18th international conference on intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Yang, Long-Hao Lu, Yi-Xuan Huang, Peng-Peng Ye, Fei-Fei Wu, Hai-Dong Liu, Jun School of Economics and Management Fuzhou University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Cultural Tourism and Public Administration Fujian Normal University Fuzhou China School of Computing Ulster University Northern Ireland United Kingdom

ISBN: (纸本)9798350318401

With the aging of the population gradually serious in recent years, the research of multi-resident activity recognition in smart home has been paid much attention. For this reason, an advanced rule-based expert system, called cumulative belief rule-based expert system, is introduced to develop a novel multi-resident activity recognition model, which not only makes full use of the multiple labels of residents' activities, but also can overcome the problem of excessive data collected from smart home. In the case study, the experimental study shows that the proposed model is more efficient and accurate than the traditional machine learning models and the commonly used activity recognition model for achieving multi-resident activity recognition. © 2023 IEEE.

关键词： Automation

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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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