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Optimizing Healthcare Operations With Big data and AI 1

Optimizing Healthcare Operations With Big Data and AI

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dubey, Krati Esnaashariyeh, Ali Nahak, Kirti Hodade, Dipali Nagnath Shrivastava, Kapil Jorvekar, Ganesh Manipal University Jaipur Department of Information Technology Jaipur India Institute of Management and Entrepreneurship Development Pune India Kalinga University Dept of Computer Science & Engineering Chhattisgarh Raipur India Shree Ramchandra College of Engineering Department of Engineering and Applied Science Maharashtra Pune412216 India Department of Computer Engineering and Applications GLA University Mathura India Sanjivani K.B.P. Polytechnic Computer Technology Kopargaon India

ISBN: (纸本)9798350330915

To improve patient care and operational effectiveness, this study investigates the implementation of big data along with artificial intelligence (AI) into medical operations. Utilizing a deductive technique and an interpretive approach, this study collects secondary data from reliable sources using a descriptive design. While AI applications concentrate on clinical decision support, tailored treatment strategies, and predictive analytics, Big data integration includes health records, medical imaging, genome-wide data, and patient-generated information. Private data, interconnection, biases in AI systems, and resource limitations are some of the difficulties. Transparency, justice, and patient autonomy are all important ethical considerations. Strong data security protocols, continuous attempts to mitigate prejudice, interdisciplinary training, and open governance frameworks are among the suggestions offered. Future research should focus on interoperability standards © 2023 IEEE.

关键词： Big data

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Big data Mining for Efficient Real-Time Traffic Management 3

Big Data Mining for Efficient Real-Time Traffic Management

引用

3rd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2025

作者： Kaushik, Priyanka Rawat, Priyanka Divakaran, Prakash Chaturvedi, Vandana Mishra Roy, Ranjeet Kumar Kaushik, Manas Manglik Chandigarh University Dept. of Cse (AIML) CSE-APEX Chandigarh India Chandigarh University Dept. of Computer Science and Engineering Chandigarh India Himalayan University Itanagar India D Y Patil Deemed to be University Navi Mumbai India Lingayas Vidyapeeth Dept. of Mathematics Haryana India Manipal Institute of Technology Mahe Dept. of Electrical and Electronics Karnataka India

ISBN: (纸本)9798331521691

The application of big data mining for real-time traffic management aims to address the growing challenges of urban congestion, traffic accidents, and environmental pollution. As cities expand and vehicle numbers rise, traditional traffic management systems struggle to cope with the increasing complexity. Big data mining involves analysing vast amounts of heterogeneous data from sources such as GPS devices, traffic cameras, social media, and sensors. By employing advanced analytics, machine learning, and predictive modelling techniques, it is possible to extract valuable insights into traffic patterns, predict congestion, and identify potential incidents. These insights enable the development of dynamic and adaptive traffic management strategies, including real-time traffic signal optimization, dynamic routing, and incident response. The integration of big data mining with intelligent transportation systems (ITS) can significantly enhance traffic flow, reduce travel time, improve safety, and minimize environmental impact, paving the way for smarter and more sustainable urban mobility solutions. © 2025 IEEE.

关键词： Big data Intelligent Transportation System Kafka Real-Time Traffic Control

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Role of IoT in Smart Cities: A Review, applications, Open Challenges and Solutions

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Kolla Vivek Gurrala Swetha V T Ram Pavan Kumar M Anusuri Krishna Veni Neti Praveen Masthan Rao Kale Dept. of CSE QIS College of Engineering & Technology (A) Ongole Andhra Pradesh India Dept. of CSE Malla Reddy College of Engineering and Technology (A) Telangana India Dept. of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India Dept. of Computer Science & Engineering – Data Science Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Dept. of Computer Science and Design SRKR Engineering College Bhimavaram Andhra Pradesh India Dept. of CSE Pace Institute of Technology and Sciences (A) Ongole Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Smart cities have emerged as a response to the fast expansion of metropolitan areas; these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions.

关键词： Waste management data privacy Technological innovation Smart cities Scalability data security Medical services Public security Internet of Things Interoperability

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment

Image based and Point Cloud based Methods for 3D View Recons...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Arya Agrawal Teena Sharma Nishchal K. Verma Shantaram Vasikarla School of Computer Science and Engineering Vellore Institute of Technology Vellore India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati India Dept. of Electrical Engineering Indian Institute of Technology Kanpur India Dept. of Computer Science California State University Northridge CA

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more.

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Design and Evaluation of Reward Functions for Load Balancing in Cloud datacenters

Design and Evaluation of Reward Functions for Load Balancing...

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Innovative Trends in Information technology (ICITIIT), International Conference on

作者： Samriddhee Ghosh Karthick Seshadri Chidambaran Kollengode Dept. of Computer Science and Engineering National Institute of Technology Andhra Pradesh India AI and Data Platforms LinkedIn Bangalore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Task scheduling and virtual machine (VM) consolidation techniques optimize resource utilization and distribute the load evenly across physical machines and virtual machines in a virtualized data center. Users’ requests to applications hosted on the virtual machines are dynamic. Though schedulers assign tasks to VMs based on the current state of the VMs and the expected resource requirements of the tasks, the requirements of the tasks typically tend to deviate from the expected requirements. This eventually leads to load imbalance and skewed energy consumption across virtual machines. Load imbalance and high energy consumption occur due to dynamic nature of workloads across the data center. An overloaded VM experiences degradation in performance thereby violating Quality of Service (QoS) and Service Level Agreement (SLA) requirements. In this work, a reinforcement learning (RL)-based task scheduling approach is utilized to minimize load imbalance, wherein we compare the effectiveness of using different reward functions. The RL agent learns a policy to minimize the number of overloaded VMs and to reduce deviation from a load-balanced condition by efficiently scheduling tasks using Q-learning algorithm. It takes scheduling decisions with the help of previous experiences when it encounters similar workload patterns. Experiments have been performed on the real time Google cluster traces and the proposed reward functions were assessed for their performance.

关键词： Energy consumption data centers Q-learning Quality of service Virtual machining Scheduling Real-time systems

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Enhanced Building Prediction for Smart City Surveillance

Enhanced Building Prediction for Smart City Surveillance

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Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Yuvaraj Natarajan Gitanjali Wadhwa Lekshmipriya Saravanan Mathivathani Natarajan Dept. of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Dept. of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercussions is vital. By incorporating intelligence into applications, everyone in the current situation has become intelligent, which lessens the load of constant interruption or human control. By using Machine to Machine interactions, intelligence enables the ability to connect more real-time parameters and make moral judgments in perilous circumstances. The current study examined the framework, approach, and performance of currently available AI-based techniques. According to the research, choosing the best machine and deep learning model for a given task can be difficult. Among the primary areas of study in relation to the vast amounts of data collected is the viability in the practice of the used algorithms, both standard machine learning and the more recent branch known as deep learning. Present a quantitative analysis contrasting the effectiveness of conventional deep learning and machine learning strategies using the direction of human movement in detecting the applications based on the analogue pyroelectric infrared sensor (PIR) outputs. Here, Random Forest is used as the machine learning method because of its great prediction accuracy. Over deeper datasets, deeper CNNs outperform shallow models as a deep learning method. The study demonstrates that traditional Machine Learning performs with detecting values within the first few seconds, improving real-time detection. Additionally, it might be used on more smart buildings without upgrades.

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Fake News Detection Review: Concepts, Tools, Research GAPS, and Possible solutions

Fake News Detection Review: Concepts, Tools, Research GAPS, ...

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International Conference on Network applications Protocols and Services (NETAPPS)

作者： Sapana Yakkundi Rudragoud Patil Swetha Indudhar Goudar Dept. of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department. of Master of Computer Applications KLS Gogte Institute of Technology Belagavi Karnataka India

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

The increasing number of social platforms means that there is a rapid expansion of online data. However, not all of this data can be trusted, as some users intentionally manipulate information to spread false news for personal or political reasons. Fake news can significantly impact public perception and societal outcomes. Its widespread influence was especially noticeable during the 2016 United States election cycle, leading to heated discussions about its role and consequences. Given the changing nature of news, real-time data processing has become essential for effectively identifying and countering misinformation. This research provides a comprehensive examination of the datasets, tools, and techniques utilized for detecting and combating fake news. Many researchers have developed models based on specific datasets, limiting their scope to particular domains. A thorough review of advanced models for real-time fake news detection highlights existing research gaps. The analysis of the review reveals that numerous datasets used in the literature are outdated, and there is a methodological challenge associated with the use of traditional machine learning approaches. With the continuous evolution of news, there is an urgent demand for utilizing sophisticated deep learning models for the automated, real-time verification of misinformation. This research presents promising approaches to tackle the challenge of detecting fake news.

关键词： Accuracy Protocols Limiting Reviews Social networking (online) Voting Real-time systems data models Internet Fake news

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Design and Implementation of Multipurpose Chatbot 4

Design and Implementation of Multipurpose Chatbot

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4th International Conference on Smart Systems and Inventive technology, ICSSIT 2022

作者： Pavitha, N. Bhatele, Priyanka Desai, Sharmishta Pande, Himangi Dept. of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune India Department of Computer Engineering Vishwakarma Institute of Technology Maharashtra Pune India School of Computer Engineering and Technology MIT World Peace University Maharashtra Pune India

ISBN: (纸本)9781665401180

Chatbots, also called chatterbots are applications that simulate human conversation either taking voice or text commands or both as input. They are automated programs that utilize Natural language processing techniques to understand process and responds accordingly to the users. Chatbots are cost effective and require a very little investment cost in the beginning stage of development. The software program can be built using AIML that use Extensible Mark-up Language as a base. In python, libraries like NLTK and JSON package can be used for the efficient designing of a Chatbot model. Chatbots use Corpus which is the vocabulary or the huge amount of data from different human dataset to the model. This paper elaborates on a Chatbot model that uses the functions of nltk library in python to pre-process data and responds accurately to the user using the cosine similarity approach. This chatbot can be implemented for any specific domain by changing the corpus. The corpus used in this design is about chatbots itself. The chatbot is capable of accurately responding to the queries related to chatbots. Accuracy of the proposed chatbot is 93% that is out of 150 queries it answered 140 queries correctly. © 2022 IEEE

关键词： Python

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A Review on Non-Intrusive Load Monitoring

A Review on Non-Intrusive Load Monitoring

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Power, Instrumentation, Energy and Control (PIECON), International Conference on

作者： Prabhash Kumar Sonwani Manini Swarnkar Gurpinder Singh Amit Soni Anil Swarnkar Khaleequr Rehman Niazi Dept of Electrical Engineering Malaviya National Institute of Technology Jaipur India Dept. of computer science Manipal University Jaipur India Dept. of Electrical Engineering Manipal University Jaipur India

Non-Intrusive load monitoring (NILM) is an emerging technology for extracting potent information from a consumer’s electric load profile. The NILM techniques are gaining popularity among researchers as they reduce the requirement for external hardware for load monitoring. These techniques require only the smart meter’s data, thus reducing the need for other measuring and sensing devices. In this paper, the research status of non-intrusive load monitoring has been reviewed. The utilisation of NILM techniques for event detection, load monitoring and energy disaggregation has been discussed in this paper. Furthermore, the research gap and future aspects are discussed. This paper has a great utility factor for upcoming researchers interested in NILM techniques.

关键词： Load monitoring Event detection Instruments Hardware Sensors data mining

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