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High Frequency Rule Synthesis in a Large Scale Multiple Database with MapReduce

International Journal of Electronics and Telecommunications 2022年第2期68卷 177-186页

作者： Bisoyi, Sudhanshu Shekhar Mishra, Pragnyaban Mishra, Saroja Nanda Odisha Bhubaneswar India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Dept. of CSE&A IGIT Dhenkanal Odisha Sarang India

Increasing development in information and communication technology leads to the generation of large amount of data from various sources. These collected data from multiple sources grows exponentially and may not be structurally uniform. In general, these are heterogeneous and distributed in multiple databases. Because of large volume, high velocity and variety of data mining knowledge in this environment becomes a big data challenge. Distributed Association Rule Mining(DARM) in these circumstances becomes a tedious task for an effective global Decision Support System(DSS). The DARM algorithms generate a large number of association rules and frequent itemset in the big data environment. In this situation synthesizing high-frequency rules from the big database becomes more challenging. Many algorithms for synthesizing association rule have been proposed in multiple database mining environments. These are facing enormous challenges in terms of high availability, scalability, efficiency, high cost for the storage and processing of large intermediate results and multiple redundant rules. In this paper, we have proposed a model to collect data from multiple sources into a big data storage framework based on HDFS. Secondly, a weighted multi-partitioned method for synthesizing high-frequency rules using MapReduce programming paradigm has been proposed. Experiments have been conducted in a parallel and distributed environment by using commodity hardware. We ensure the efficiency, scalability, high availability and cost-effectiveness of our proposed method. © 2022 Polish Academy of sciences. All rights reserved.

关键词： MapReduce

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Ethical and Practical Implications of Implementing Facial Recognition Technology for Attendance Control in Educational Institutions and Businesses 1

Ethical and Practical Implications of Implementing Facial Re...

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

作者： Kurup, Ravindra Muzalda, Devendra Sharma, Vinod Hudda, Yashika Singh, Vijay Vir Princy, Benila Madhav University Dept. of Humanities and S.Science Pindwara India SLP College Dept of CSE Gwalior India Chandigarh University Dept of Computer Science Engineering Chandigarh India Lingayas Vidyapeeth Dept. of Mathematics Faridabad India Meenakshi A.H.E.R Dept. of English Chennai India

ISBN: (纸本)9798350356816

Facial recognition technology has garnered significant attention recently as a potential solution for attendance management in educational institutions and companies. This study proposes a facial recognition attendance system designed to address limitations of existing systems and navigate the ethical and legal considerations associated with the technology. To ensure accuracy, security, and user acceptance, the proposed system integrates advanced facial recognition algorithms, strict data protection protocols, and intuitive interfaces. The methodology encompasses a thorough literature review, primary data collection through surveys and interviews, system design, testing, and data analysis to evaluate the feasibility, efficacy, and ethical implications of deploying facial recognition for attendance tracking. The experimental setup includes cameras, laptops, servers, facial recognition algorithms, attendance management software, and a diverse collection of facial images. Analysis of the collected data aims to assess the system's accuracy and effectiveness. The study's findings will offer insights into the potential benefits and challenges of adopting facial recognition technology for attendance management in educational and corporate settings, along with recommendations for future research and implementation. © 2024 IEEE.

关键词： Accuracy Attendance Management Data Analysis Diverse Facial Image Facial Recognition

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An Experimental Study on No-Reference Image Quality Assessment of Image Datasets 7

An Experimental Study on No-Reference Image Quality Assessme...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Magotra, Varun Rohil, Mukesh Kumar Bhartiya Vidya Bhavans Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India Birla Institute of Technology and Science Dept. of Computer Science Rajasthan Pilani India

ISBN: (纸本)9781665421683

Image quality assessment algorithms are of various types depending upon the the method they are employing. Over the years, these algorithms have found various applications like image compression, image restoration, image transmission etc. Image quality assessment algorithms have played a significant in these fields by appropriately evaluating the quality of the images. Based on the structure of these algorithms, in this paper, we further explored the application of Image Quality Assessment algorithm especially Non-Reference Algorithm in the world of AI. In this paper, we have showed how the Non-reference IQA algorithms can be used to assess the quality of the images in the data that are being used in for training the deep learning models. In our study, we specially focused on the datasets that are being used in the training of the deep learning models that are used in the healthcare sector. We have showed how non-reference algorithms can be used to create a more quality data so that the efficiency of the automated systems which heavily relies on the data quality can be increased. © 2022 IEEE.

关键词： Image compression

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A Hybrid Machine Learning and Deep Learning Approach for Diabetes Prediction with a Tkinter-Based User Interface

A Hybrid Machine Learning and Deep Learning Approach for Dia...

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

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

ISBN: (纸本)9798350369175

Diabetes prediction is a critical task in healthcare that can significantly benefit from advancements in machine learning and deep learning technologies. This paper presents a hybrid approach combining deep learning and traditional machine learning models for diabetes prediction. The proposed system integrates a Tkinter-based Graphical User Interface (GUI) to make it easier for users to interact and make predictions. In our discussion, we will cover the design and implementation of the system, and evaluate the performance of different models using precision, F1 score, recall, and, accuracy to provide insights into their effectiveness. Results demonstrate the efficacy of the hybrid approach, with a focus on model accuracy and user experience. © 2024 IEEE.

关键词： Prediction models

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Evaluating Visual Transformers for Wafer Defect Detection: A Feasibility Study

Evaluating Visual Transformers for Wafer Defect Detection: A...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Lafrance, Samantha Bernadin, Shonda FAMU-FSU College of Engineering Dept. of Electrical and Computer Engineering Tallahassee United States

ISBN: (纸本)9798331504847

Wafer defect detection is a critical aspect of semi-conductor manufacturing, where even minor defects can lead to significant performance issues or device failures. Traditional methods, such as rule-based approaches and Convolutional Neural Networks (CNNs), have achieved moderate success but face limitations in scalability, adaptability, and the ability to model complex spatial patterns in high-resolution wafer imagery. These limitations make it critical to investigate advanced architectures that can accurately capture complex spatial relationships. Vision Transformers (ViTs) have emerged as a promising alternative within computer vision, offering global attention techniques that are particularly effective in recognizing long-range dependencies. This study evaluates the feasibility of ViTs for wafer defect detection through a literature-based empirical validation approach, synthesizing findings from prior research on ViT-based classification, defect localization, and industrial feasibility. Results indicate that ViTs offer superior accuracy and interpretability compared to CNN s, particularly in detecting rare and mixed-type defects. However, challenges remain in terms of computational cost and real-time deployment in high-throughput manufacturing settings. The findings of this study underscore the potential of ViTs as a transformative technology for semiconductor defect inspection while highlighting key areas for further development. © 2025 IEEE.

关键词： Convolutional neural networks

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Causes and Consequences of IPD Mismatch in XR Devices

Causes and Consequences of IPD Mismatch in XR Devices

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Wilcox, Laurie M. Allison, Robert S. Department of Psychology York University TorontoM3J1P3 Canada Dept. of Electrical Engineering and Computer Science York University TorontoM3J1P3 Canada

Binocular 3D display systems, including virtual, augmented and mixed reality (collectively referred to as XR) devices support stereoscopic dept. perception by presenting dichoptic views separately to the two eyes rendered according to their vantage points. A common human factors issue is a mismatch between the observer’s physical interpupillary distance (IPD) and 1) the separation between the device optics and 2) the rendered/captured content. This paper provides a theoretical review of the importance and consequences for user experience of accurately estimating IPD and incorporating it into HMD adjustments, calibration and rendering. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Stereocenters

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Citrus Leaf Disease Classification Using Different Deep Learning Models: A Survey of Southern part of Bangladesh

Citrus Leaf Disease Classification Using Different Deep Lear...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Masud Arpita Chokroborty Md. Masudur Rahman Partho Sarathi Sarker MD. Jubayar Alam Rafi Mirza Raquib Dept. of Computer Science and Engineering University of Global Village Barishal Bangladesh Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Citrus plant cultivation is becoming increasingly popular in South Asia, more specifically in India and Bangladesh region due to its rapidly growing demand as well as suitable weather conditions for farming. It can be affected by several diseases and requires keen attention to detect and cure the diseases in time; otherwise, significant monetary loss is incurred. With the advancement of computer vision and deep learning techniques, identifying various diseases is becoming simpler. However, this process requires a proper dataset of infected leaves and a suitable detector to recognize the diseases, and the existing renowned citrus disease datasets are based on a particular zone that may not be like the actual data that apply to the Barishal region. Therefore, a new dataset is proposed by doing two-step surveying in the southern part of Bangladesh that comprises images of infected citrus leaves with multiple classes of diseases i.e. 5 types of diseases, including precise annotations. Our subsequent investigation with these datasets consists of selecting basic CNN models for classification as well as applying the dataset to existing deep learning models such as VGG16, Xception, and InceptionV3 for performance and computational analysis. The high performance of those models proves the potentiality of the dataset. The dataset's diversity, clear images, transparent backgrounds, and real-life weather conditions led to the models achieving remarkable accuracy.

关键词： Deep learning Surveys computer vision Analytical models Adaptation models Accuracy Computational modeling Information technology Diseases Meteorology

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Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks 2

Enhancing Stock Price Predictions Through LSTM-based Recurre...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishna Kumar, G.V. Natarajan, B. Venkatraman, K. Devi, S. Gayathri Selvam, Prabu Nagarajan, N.R. Amrita Vishwa Vidyapeetham Dept. of Artificial Intelligence Chennai India VIT University School of Computer Science and Engg. Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of CSE Chennai India Sri Ramakrishna Institute of Technology Dept. of Computer Science and Engg. Coimbatore India K. Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350317558

Analyzing finances has become increasingly challenging in today's investment landscape, where making valuable and informed investment decisions is crucial. The fluctuation of share prices plays a pivotal role in determining investors' profits or losses. Current forecasting techniques encompass both linear and non-linear algorithms. However, these methods primarily emphasize forecasting changes in the stock index or forecasting prices for individual companies based on their daily concluding rates. The proposed methodology introduces a model-agnostic method. Rather than conforming data to a specific model, this approach aims to identify unseen trends inherent in the data through deep learning models. In this study, three distinct deep learning models - HMM, RNN, and LSTM - are employed to predict prices using the dataset from ICICI Bank. Their performances are compared, revealing that the LSTM model adept.y discerns evolving trends. The LSTM model displays the lowest error percentage at 2.36%, outperforming other models such as HMM (7.32%) and RNN (3.94%). © 2024 IEEE.

关键词： deep learning LSTM stock market stock Price prediction Time series

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Building Wireless Observation Network to Track Biological Clustering

Building Wireless Observation Network to Track Biological Cl...

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2022 IET International Conference on engineering Technologies and Applications, IET-ICETA 2022

作者： Guo, Yun-Ru Huang, Yu-Chin Horng, Gwo-Jiun Southern Taiwan University of Science and Technology Dept. Computer Science and Information Engineering Tainan Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

In recent years, wireless sensor network has been widely used to automatically collect the field ecological environment and observation data, so that researchers can analyze the natural changes of the field ecological environment through the huge amount of data. In this study, biological observation data are transmitted back to the cloud server through wireless communication network, and new wireless communication technologies are developed to improve the data transmission mechanism and overcome the difficulties of remote wireless communication in field areas. In addition, the data transmission efficiency is improved to ensure that the data is successfully transmitted back to the server in real time and quickly, so that the observers do not need to go to the tracking target trap location to read the observation related data. © 2022 IEEE.

关键词： Wireless sensor networks

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HILO: A Large-Scale Heterogeneous Object Dataset for Benchmarking Robotic Grasping Approaches

HILO: A Large-Scale Heterogeneous Object Dataset for Benchma...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Xinchao Song Sean Banerjee Natasha Kholgade Banerjee Dept. of Computer Science Clarkson University Potsdam NY USA Dept. of Computer Science & Engineering Wright State University Dayton OH USA

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Robust object manipulation is essential for robotics applications in real-world environments, especially when handling diverse and complex everyday objects. To facilitate this research, we present HILO, a large-scale dataset of 253 real-world everyday objects and 288 diverse scenes. HILO bridges a crucial gap in existing manipulation datasets through its heterogeneity and dual-resolution approach, combining high-resolution individual object scans with low-resolution scans of cluttered scenes. This provides both the precise geometric data needed for grasp planning and realistic environmental context. The dataset's comprehensive representations enable rigorous benchmarking of robotic grasping algorithms. Our evaluation of three leading grasping algorithms-Contact-GraspNet, GraspNet Baseline, and DexNet 4.0-reveals critical trade-offs between grasp quantity and quality, demonstrating the dataset's value in advancing robotic grasping research. HILO's rich object diversity and dual-resolution methodology provide a foundation for developing more versatile robotic systems capable of reliable real-world robotic manipulation. Our dataset is available at https://***/hilo_dataset/.

关键词： Bridges Automation Diversity reception Collaboration Grasping Benchmark testing Diversity methods Planning Reliability Robots

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