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Smart City Solutions Leveraging Internet of Things (IoT) Technology 1

Smart City Solutions Leveraging Internet of Things (IoT) Tec...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumawat, Bhawesh Shalini, E. Tomar, Vinay Upadhyay, Rajesh Kumar Gupta, Divyanshu Geetha, P.V. Madhav University Dept. of CSE Pindwara India Meenakshi A.H. Edu. & Research Dept. of Computer Science Chennai India Jiwaji University Dept. of Chemistry Gwalior India Usha Martin University Dept. of EEE Jharkhand India J.S. University Dept. Electronics & Comm. Engineering Shikohabad India Dhanalakshmi Srinivasan College Dept. of AI & Data Science Coimbatore India

ISBN: (纸本)9798350356816

The widespread adoption of the Internet of Things (IoT) is facilitating the development of Smart City initiatives globally. Everyday objects are increasingly equipped with electronic gadgets and connected through various protocol suites, becoming part of a vast network. According to a recent Gartner study, by 2020, smart cities are projected to deploy 50 billion connected devices. This advancement is expected to make urban environments more adaptive and resilient to future changes. This paper explores current and future trends in smart cities and IoT, highlighting the technologies and applications driving their growth. We examine how the expansion of IoT directly impacts the evolution of smart cities and discuss the interplay between the two, as well as the factors motivating their development. Smart cities have the potential to reduce manual labor, mitigate disaster risks, and streamline daily life. Lastly, we address some of the challenges associated with IoT in the context of smart cities and propose potential solutions to these issues. © 2024 IEEE.

关键词： Smart city

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Design and Implementation of a Mobile Application for Intelligent Attendance Tracking 1

Design and Implementation of a Mobile Application for Intell...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Nadhiya, S. Jain, Deepak Verma, Anupama Kumar, Saurabh Bhavadharani, S. Chandigarh University Dept. of Computer Science Engineering Chandigarh India Dhanalakshmi Srinivasan College Dept. of AI & Data Science Coimbatore India IIM Indore Dept. of Management Indore India Usha Martin University Dept. of Business Management & Comm Jharkhand India Mangalayatan University Dept. of Business Management & Commerce Aligarh India A.H. Edu. & Research Dept. of Commerce Meenakshi Chennai India

ISBN: (纸本)9798350356816

Taking attendance is an important task in schools, universities, and other institutions. However, traditional attendance taking methods such as paper-based attendance sheets or manual entry into an electronic system are time-consuming and prone to errors. In recent years, the emergence of facial recognition technology has opened up new possibilities for attendance management systems. This research paper discusses the development and implementation of a Smart Attendance System using Facial Recognition technology using OpenCV in educational institutions. The study explores the benefits, limitations, and ethical considerations associated with this technology and its implications for the future of attendance management systems. This model takes use of LBPH and gja, exfilaface recognition to increase accuracy over conventional models. After this, the connection of recognized faces is compared with the kdatabase containing student's faces. This model will be an effective method for controlling student attendance. © 2024 IEEE.

关键词： Students

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Systems Analysis of Bias and Risk in AI-Enabled Medical Diagnosis

Systems Analysis of Bias and Risk in AI-Enabled Medical Diag...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Moghadasi, Negin Piran, Misagh Baek, Stephen Valdez, Rupa S. Porter, Michael D. Johnson, DeAndre Lambert, James H. University of Virginia Johnson & Johnson Medtech Dept. of Systems and Information Engineering United States Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Ruhr University of Bochum Dept. of Radiology Germany School of Data Science University of Virginia United States Engineering School of Data Science University of Virginia Dept. of Systems and Information United States Engineering University of Virginia Dept. of Systems and Information United States

ISBN: (纸本)9781665430654

AI technologies have made significant advancements across various sectors, especially healthcare. Although AI algorithms in healthcare showcase remarkable predictive capabilities, apprehensions have emerged owing to errors, biases, and a lack of transparency. These concerns have led to a decline in trust among clinicians and patients, while also posing the risk of further accentuating pre-existing biases against marginalized groups and exacerbating inequities. This paper presents a scenario-based preferences risk register11Denotes a methodically arranged document or database detailing potential risks linked to particular scenarios or situations. framework for identifying and accounting AI algorithm biases in diagnosing diseases. The framework is demonstrated with a realistic case study on cardiac sarcoidosis. The framework identifies success criteria, initiatives, emergent conditions and the most and least disruptive scenarios. The success criteria align with the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF) trustworthy AI characteristics, and the scenarios are based on various statistical/computational bias that causes algorithmic bias. The framework provides valuable guidance for leveraging AI in healthcare, enhancing objective designs, and mitigating risks by adopting a figure of merit to score the initiatives and measuring the disruptive order. By prioritizing transparency, trustworthy AI, and identifying the most and least disruptive scenarios/biases, the framework promotes responsible and effective use of AI technologies in healthcare. © 2023 IEEE.

关键词： Algorithm ethics cardiac sarcoidosis computational intelligence disease progression legal implications risk management scenario-based preferences social impacts

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Prediction and Classification of Brain Tumor using Modified Transfer Learning

Prediction and Classification of Brain Tumor using Modified ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sukanya Roy Bijoy Kumar Mandal Koustov Khamaru Arghya Bhattacharya Dept. of Computer Science Engineering University of Engineering and Management Kolkata India Dept. of Computer Science Engineering NSHM Knowledge Campus Durgapur India

This study uses a novel method of segmenting and computing the tissue area in brain MRI images to predict the presence of brain tumors and classify the type of tumor into common types (pituitary, meningioma, and glioma). The tissues that make up the brain have intricate boundaries. In order to more accurately classify the different types of tumors, we have thus divided the brain tissues into three segments and examined for enlarged segments using Convolutional Neural Networks (CNNs) and a Modified CNN that uses transfer learning and VGGNet. The paper’s methodology involves using CNNs with Transfer Learning for efficient image segmentation and clustering, followed by VGGNet for noise reduction and image segmentation refining. We are optimizing the internal parameters of VGGNet in order to outperform the competition. The volume and density of malignant clusters are computed through pixel counting. This kind of method works pretty well for figuring out tissue volume in a wide range of illnesses that affect people of all ages.

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Question Categorizer: An Automated Approach For Organizing Queries 2

Question Categorizer: An Automated Approach For Organizing Q...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Roy, Naina Upadhyay, Khushi Doshi, Payal Nagarhalli, Tatwadarshi P. Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India Prime SofTech Solutions Pvt. Ltd. Pune India

ISBN: (纸本)9798350365269

Question categorization is an essential task in natural language processing (NLP) and information retrieval which is the process of automatically assigning a category or class label to a given question based on its content. Categorizing questions plays a pivotal role in enhancing comprehension and facilitating targeted responses. This paper delves into the development and implementation of an advanced question categorization system which classifies questions into eight distinct categories: Analytical, definition, Theoretical, Inferential, Formulation, Differential, Mathematical and Statistical thereby encompassing a wide spectrum of question types. The foundation of this system lies in the integration of a sophisticated BERT architecture, natural language processing techniques, and domain-specific expertise. The system autonomously processes input queries, identifying the underlying characteristics that define each category and assigns the category to the input data. The model is trained on a curated set of data and the proposed methodology achieved promising results with an accuracy score of 0.98 for the 8 categories multiclass classification. © 2024 IEEE.

关键词： Classification (of information)

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Clear Text Visibility: A CNN-LSTM Approach for Frame of Interest Identification in Book Flipping Videos

Clear Text Visibility: A CNN-LSTM Approach for Frame of Inte...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Buddhawar, Gaurav Dave, Dharin Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Dept. of Computer Science and Engineering Surat India Pune India Flame University School of Computing and Data Sciences Pune India

ISBN: (纸本)9798331533311

Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model's capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos. © 2024 IEEE.

关键词： Convolutional neural networks

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A Framework for Identifying Emotions in Text through Machine Learning and Natural Language Processing Methods 1

A Framework for Identifying Emotions in Text through Machine...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, P. Damodaran, B. Reshma, P. Ganguli, Ranjan Shyamu Kajla, Amar Chandigarh University Dept of CSE AIML-CSE Chandigarh India Meenakshi A.H.E.R Dept. of Psychology Chennai India Dhanalakshmi Srinivasan College of Engineering Dept. of AI & Data Science Coimbatore India Usha Martin University Dept. of CSE Jharkhand India J.S. University Dept. of Computer Science Shikohabad India Chandigarh University India Dept. of CSE Chandigarh India

ISBN: (纸本)9798350356816

This research introduces an innovative approach that leverages machine learning and natural language processing (NLP) techniques to detect emotions in text. The proposed system, equipped with a Google Chrome extension, allows users to easily and rapidly evaluate the emotional content of the websites they browse. By employing various NLP methods, including keyword extraction and tokenization, the system effectively extracts relevant features from the text. These features are then used to train a machine learning model capable of accurately predicting the emotional tone of new text. The system demonstrates exceptional accuracy across several benchmark datasets and proves its practical utility in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Deep Learning Approaches for Content-Based Image Retrieval System in Skin Cancer Diagnostics

Advanced Deep Learning Approaches for Content-Based Image Re...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Margret, Issac Neha Kasetty, Sai Bhargav Sudha, Nuthalapati Ojha, Rajesh Kumar Madhuri, K. Sai Rajakumar, K. V.I.T University SCOPE Tamilnadu Vellore India CYS VNR VJIET Dept. of CSE - DS AI&DS Telangana Hyderabad India Silicon University Dept. of Computer Science & Engineering Odisha Bhubaneswar India BVRIT Dept. of CSE - Data Science Telangana Narsapur Hyderabad India

ISBN: (纸本)9798350369175

The objective of this paper is to develop and assess the performance of a deep learning model-based content skin cancer detection image retrieval system. In this regard, CNN, VGGNet, ResNet, DenseNet, InceptionNet, and MobileNet were tested on recall, accuracy, mean average precision, F1, and precision metrics. VGGNet performed best among them, with an accuracy of 92.3%, outperforming all other models in precision, F1-score, and recall. CNN also gave the best performance with 91.5% accuracy. The CBIR system retrieved relevant images for the given lesions, especially melanoma lesions, which were relevant and high in precision within the top 5 retrieved images. These results also confirm the efficiency of CNN and VGGNet in enhancing accuracy during the retrieval of skin cancer diagnosis images. © 2024 IEEE.

关键词： Contrastive Learning

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Advanced Machine Learning Approaches to Combat Fake News in the Digital Era 4

Advanced Machine Learning Approaches to Combat Fake News in ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kaur, Tejinder Kumar Gantayat, Pradosh Das, Swatismita Majhi, Madhusmita Dept. of Mmictbm Mullana Haryana Ambala133207 India Department of Artificial Intelligence and Data Science Telangana Hyderabad501203 India Raajdhani Engineering College Bhubaneswar India Department of Computer Science and Engineering Telangana Hyderabad501203 India

ISBN: (纸本)9798350387490

This paper aims to provide the sustainable design for the user to provide them with ability to check whether the information is fake or real by providing the accuracy of the news using Machine learning via various algorithms with the help of database, which will respond as whether the news is accurate or not. Fake news has become a prevalent issue in today's digital age, causing significant societal impacts by spreading misinformation. This paper explores the application of machine learning techniques for detecting fake news. The paper also addresses challenges such as data scarcity, model interpretability, and the dynamic nature of fake news. We conclude with recommendations for future research directions, emphasizing the need for real-time detection systems and cross-disciplinary collaboration to enhance the effectiveness of fake news detection using machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Lighter Neural Network Performance with Layer-wise knowledge Distillation and Selective Pixel Attention 13

Enhancing Lighter Neural Network Performance with Layer-wise...

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13th International Conference on Computer and knowledge engineering, ICCKE 2023

作者： Zaravashan, Siavash Torabi, Sajjad Zaravashan, Hesam Iran University of Science and Technology Dept. Computer Engineering Tehran Iran Data Scientist Part Research Center of Ai Tehran Iran

ISBN: (纸本)9798350330151

Deep neural networks have made a revolution in different areas of Artificial Intelligence such as image classifi-cation and object detection. However, most of these models are heavy and time-consuming at the inference stage. If we desire to use real-time applications, it's necessary to lighten them up. However, this could negatively affect the performance of models. One suggested approach for enhancing the capabilities of smaller models involves a technique called knowledge distillation. In this method, a less advanced model, referred to as the student model, learns from and emulates the more advanced teacher model to enhance its performance. This method has been proven as an effective technique to compress the model and increase its accuracy. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. In this paper, we investigate the effect of connections between different layers of teacher and student networks and reveal their great importance. We have employed three different loss functions - attention-driven distillation, hierarchical context loss, and non-local loss - to enhance the classifier's overall performance. Extensive experiments on the CIFAR-100 dataset show that the proposed method has a great performance improvement. For the benefit of the research community, we will make the code for this study available on GitHub. © 2023 IEEE.

关键词： Image classification

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