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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chourasiya, Leeladhar Khatri, Sushma Sethi, Kamal K. Mahajan, Anita Bhawsar, Avani Verma, Tanmay Sharma, Neeraj Acropolis Institute of Technology and Research Computer Science and Engineering Indore India Vasantdada Patil Prasthan College of Engineering and Visual Arts Mumbai India

ISBN: (纸本)9798331518981

The research explores the vital role of traceability in addressing student stress within the education sector. It underscores the importance of systematically tracking and analyzing stress factors and their impacts on students over time. The study aims to develop a framework for capturing, analyzing, and responding to stress-related data in educational settings. This involves understanding student behavior and engagement through machine learning and computer vision technologies. Data collection methods include Google Forms and consultations with psychologists. The document outlines model development, training, real-time monitoring, data visualization, user interaction, and intervention strategies. Furthermore, it elucidates the concepts of traceability and workability in the context of AI, addressing challenges and strategies for overcoming them. The paper also delves into stress-related issues in education and potential solutions. © 2024 IEEE.

关键词： Students

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Privacy-Preserving Human Activity Recognition System for Assisted Living Environments

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2342-2357页

作者： Jain, Ankit Akerkar, Rajendra Srivastava, Abhishek Indian Institute of Technology Indore Department of Computer Science and Engineering Madhya Pradesh Indore453552 India Western Norway Research Institute Sogndal6856 Norway

Automatic human activity recognition has numerous applications, especially in elderly support and healthcare. Several approaches for human activity recognition (HAR) using a variety of sensors are available in literature. While such frameworks are effective, each has limitations related to privacy, convenience, cost, and performance. In this article, a robust framework for automatic human activity recognition is proposed that uses depth sensors that preserve privacy and are cost-effective. The depth sensors provide two data modalities, namely, depth maps and skeleton sequences, used together for activity recognition. Two novel descriptors, joint position descriptor based on the position of joints;and bone angle descriptor based on bone inclination, are generated from the skeleton sequence data. The descriptors convey both spatial and temporal information and are scale and view-point invariant. Depth video clips are used along with the descriptors to deal with the issue of noisy and missing skeleton sequences. The data modalities and descriptors are fused using a two-level fusion strategy for a multichannel convolutional neural network (CNN) framework. The proposed system is validated and shown to be superior to the existing state of the art through comparisons over four widely used public datasets. A computational complexity analysis of the system confirms its efficacy in real time. A prototypical implementation of the proposed system further validates its practicability. © 2020 IEEE.

关键词： Neural networks

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A Review on NFC for Secure Transaction its Fundamental Challenges and Future Directions

A Review on NFC for Secure Transaction its Fundamental Chall...

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Gupta, Kartik Bhanodia, Praveen Sethi, Kamal Kumar Rajput, Shivshankar Patidar, Mukesh Iyer, Vignesh Hariharan Acropolis Institute of Technology and Research Department of Computer Science & Engg. Indore India Independent Researcher Pune India

ISBN: (纸本)9798350388800

Near-Field Communication (NFC) payment solutions are contactless payment solutions which harness the short-range communication capabilities and existing RFID infrastructure. The increasing availability of NFC enabled devices indicates a potential increase in usage of NFC based payment solutions becoming more ubiquitous. In this review, we summarize the working of NFC based solutions, the past works, and concerns with respect to adoption of NFC based payments, the industry standards and how the present scenarios can help or be improved in-order to use NFC based payments on E-Commerce websites. This review aims at gathering input to build an enhanced NFC based payments solution that addresses the existing concerns, improves convenience and is also compliant with industry standards. © 2024 IEEE.

关键词： Electronic money

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Varshney, Santosh Parul Institute of Engineering and Technology Department of Computer Science and Engineering Vadodara India Apex Institute of Technology Chandigarh University Department of Bigdata Analytics Mohali India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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Samarjini - Revolutionizing Household Cleaning with an Affordable IoT-Based Automatic Mopper 3

Samarjini - Revolutionizing Household Cleaning with an Affor...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Joshi, Siddharth Kate, Vandana Bhalerao, Shilpa Tripathi, Prasoon Sharma, Sudhanshu Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India

ISBN: (纸本)9798350343274

The title 'Samarjini' draws its inspiration from the Sanskrit word for a cleaning tool, and this research paper introduces the development of an automatic mopper machine designed to streamline household cleaning tasks while maintaining affordability. In a market filled with high-cost alternatives, this IoT-based device stands out by offering autonomous navigation, obstacle detection and avoidance, and effective floor cleaning at a lower price. The paper explores the machine's design, hardware and software components, evaluates its performance in various scenarios, and underscores its relevance in today's fast-paced lifestyles. Extensive experimentation was conducted to assess the machine's performance in diverse environments and scenarios. The outcomes affirm that 'Samarjini' excels in floor cleaning, adeptly avoiding obstacles and autonomously navigating through spaces. © 2023 IEEE.

关键词： Floors

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The Evolution and Impact of Price Comparison Websites: A Technological and Economic Analysis 4

The Evolution and Impact of Price Comparison Websites: A Tec...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Patidar, Mukesh Vekariya, Daxa Pradhan, Preeti Gupta, Kartik Patidar, Praveen Kumar Khan, Pirmohammad Parul University Parul Institute of Engineering and Technology Department of Computer Science & Engineering Gujarat Vadodara India Madhya Pradesh Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Madhya Pradesh Indore India Parul University Parul Institute of Technology Department of Computer Science & Engineering Gujarat Vadodara India

ISBN: (纸本)9798331518981

Price comparison websites (PCWs) have become integral to the online shopping experience, enabling consumers to make informed decisions by comparing prices, features, and reviews from multiple retailers. These platforms not only benefit consumers by saving time and money but also influence market competition by increasing pricing transparency and driving businesses to optimize their strategies. This research explores the evolution of PCWs, their technological infrastructure, and their business models. Moreover, it examines their impact on consumer behavior, the challenges they pose for retailers, and the future trends shaping the industry. Through an analysis of current practices, technological innovations, and economic effects, this work highlights the growing importance of PCWs in the global e-commerce ecosystem. © 2024 IEEE.

关键词： Marketplaces

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Predicting Multi-level Classification of Breast Cancer Image by using Three FCM Variants

Predicting Multi-level Classification of Breast Cancer Image...

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2022 International Conference for Advancement in technology, ICONAT 2022

作者： Kate, Vandana Shukla, Pragya Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India Indore India

ISBN: (纸本)9781665425773

Breast cancer is the second leading cause of death among women, after lung cancer. Its initial realization, on the other hand, may significantly improve the chances of patient survival. Histopathological images of impacted tissues acquired from surgical biopsies provide adequate visual information to differentiate between breast cancer types. Breast Cancer Histopathological Images Analysis (BCHIA) is a widely used method for detecting abnormal pathological alterations in breast cancer. In this paper, we propose a machine learning technique for classifying the cancer cells in two classes and subsequently classifying them into one of four sub classes in each category. In this context the Breast Cancer(BC) dataset with 7780 histopathology images scanned at 40X, 100X, 200X and 400X magnification level is used for examination and early detection of breast cancer. We have used histopathology images as they are considered safe in comparison to other radiology imaging techniques. Additionally three variants of Fuzzy C-Means (FCM) image segmentation techniques are proposed namely GEFCM (Gaussian-Euclidean Fuzzy C-Means), GMFCM (Gaussian-Minkowski Fuzzy C-Means), and GMHFCM (Gaussian-ManHattan Fuzzy C-Means). These techniques are developed with the help of Gaussian kernels RBF (radial basis function) and three different distances measuring functions namely Euclidean distance, Minkowski distance, and ManHattan (Texi-Cab) distance functions. The calculated features using these three techniques of image segmentation are classified into one of BC type using multi-class SVM. The implementation of proposed data model is performed using MATLAB tool. Additionally for justification the accuracy, precision, recall and f1 score is measured. The experimental analysis demonstrates the superiority of GMHFCM (Gaussian-ManHattan Fuzzy C-Means) based extracted edge features. © 2022 IEEE.

关键词： Image segmentation

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Exploring the Impact of Node Velocity on Communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Sharma, Dilip Kumar Yadav, Aaditya Patidar, Mukesh Patil, Pooja Sanjay Patidar, Praveen Kumar Mandloi, Devendra Singh Sheikh, Rashid Rajpoot, Shivshankar Ujjain Engineering College Department of Electronics and Communication Engineering Ujjain India Piet Parul University Department of Computer Science and Engineering Vadodara India Credit Acceptance Financial Institution in Southfield Michigan United States Parul Institute of Technology Parul University Department of Computer Science & Engineering Vadodara India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool. © 2024 IEEE.

关键词： Fuzzy clustering

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Transfer Learning for Multiclass Classification of Wheat Leaf Diseases

Transfer Learning for Multiclass Classification of Wheat Lea...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Riya Verma Narendra Pal Singh Rathore Kamal Kumar Sethi Artificial Intelligence and Data Science Acropolis Institute of Technology and Research Indore India Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

In the following paper, we explore a study focusing on the detection of wheat leaf diseases using transfer learning techniques combined with cutting-edge convolutional neural network (CNN) architectures, namely VGG16, ResNet152V2, and Inception. The aim is to enhance the accuracy of disease detection while minimizing computational resources and training time. We conducted experiments on a dataset consisting of wheat leaf images affected with several diseases. Through transfer learning, we fine-tuned pre-trained models on our dataset to leverage their learned features. Our results demonstrate that VGG16 consistently outperforms ResNet152V2 and Inception, achieving the highest accuracy of 98.66% in disease detection. This indicates the efficacy of VGG16 for wheat leaf disease classification tasks, highlighting its potential for agricultural applications requiring robust and accurate detection of plant diseases.

关键词： Training Plant diseases Accuracy Computational modeling Transfer learning Focusing computer architecture Vectors Convolutional neural networks Diseases

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Logistic Regression Based Approach for Human Sentiment Analysis Across Domains

Logistic Regression Based Approach for Human Sentiment Analy...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Sameer Nagar Praveen Bhanodia Kamal Kumar Sethi Narendra Pal Singh Rathore Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This research paper presents a pioneering approach to cross-domain sentiment analysis utilizing logistic regression, a widely employed technique for binary classification tasks. Sentiment analysis, crucial for understanding subjective information in text data, finds applications in market analysis, customer feedback processing, and brand management. Our methodology involves extracting sentiment features from text data, leveraging techniques such as bag-of-words or word embeddings, and training logistic regression models on labeled datasets from diverse domains. Unlike conventional methods relying solely on domain-specific features, our approach incorporates domain-independent features to facilitate knowledge transfer across domains. Through extensive experiments on various datasets including product reviews, social media posts, and news articles, we demonstrate the effectiveness of our approach in achieving competitive performance compared to state-of-the-art methods. Additionally, we explore the impact of domain adaptation techniques such as domain adversarial training and transfer learning on model performance, providing insights into the interpretability of sentiment models and enabling more accurate sentiment prediction in real-world scenarios across diverse domains.

关键词： Training Logistic regression Sentiment analysis Adaptation models Analytical models Computational modeling Transfer learning Predictive models Feature extraction Robustness

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