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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Thalapathi Rajasekaran H. Shanmugavalli P. Jayalakshmi S. Ambika V. Balaji Dept. of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India Dept. of Computer Science and Engineering Sri Krishna College of Engineering Arakkonam Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of CSE (Cyber Security) Easwari Engineering College Chennai

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses.

关键词： Adaptation models Machine learning algorithms Pandemics Predictive models Prediction algorithms Vaccines Resource management

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Image Processing and Analysis of Multiple Wavelength Astronomical Data Using Python Tools

arXiv

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arXiv 2024年

作者： Bhowmik, Tanmoy Islam, MD Fardin Tasneem, Kazi Nusrat Roy, Rantideb Shahariar, Rownok Dept. of Physics Shahjalal University of Science and Technology Sylhet Bangladesh Dept. of Physical Sciences Independent University Bangladesh Dept. of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Dept. of Physics Govt. Edward College Pabna Bangladesh

We developed a Python based framework for astronomical image processing and analysis. Astronomical image loading, normalizing, stacking, and filtering processes represent visible range images from grayscale. Besides, the blending process helps to analyze the image of multiple wavelengths in the visible range. The methods take advantage of include median filtering for noise reduction, unsharp masking for sharpening details, and intensity normalization techniques. The detailed analysis of pixel intensity distributions and applying Gaussian fitting to variations across different wavelength bands. These methods highlight Python as a valuable tool for astronomers. Copyright © 2024, The Authors. All rights reserved.

关键词： Median filters

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Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Technical Job Distribution at BSD SHARP Service Center Using...

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2022 IEEE Symposium on Wireless technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of Department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

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A Review on Deep Learning-Based Polyp Segmentation for Efficient Colorectal Cancer Screening

A Review on Deep Learning-Based Polyp Segmentation for Effic...

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Samarth Gupta Geeta Sikka Aruna Malik Dept. of Computer Science and Engg. Dr. B. R. Ambedkar National Instituteof Technology Jalandhar Punjab India Dept. of Computer Science and Engg. National Institute of Technology Delhi India

The second largest cause of cancer-related deaths globally and the third most prevalent cancer overall is colorectal cancer. Growths on the inner lining of these organs called polyps commonly herald the onset of human colon and rectal cancer. These polyps have the potential to become cancerous and result in a serious, maybe deadly disease if they are not promptly recognized and treated. However, locating and recognizing polyps during a colonoscopy could be difficult. This is due to the fact that polyps may have a wide range of sizes, forms, and textures and that many other objects may seem like polyps but aren't. Despite being the most reliable procedure for finding polyps, colonoscopy is not error-free and can lead to wrong diagnoses, missing polyps, and other issues. The process of correctly recognizing and separating polyps from other objects in pictures obtained during colonoscopies is known as polyp segmentation. For this purpose, a variety of deep learning methods have recently been created. These methods analyze, interpret, and accurately identify polyps in medical pictures by utilizing machine learning and powerful computer algorithms. This paper thoroughly analyzes the most recent deep learning-based polyp segmentation studies. The primary objective of this study is to present an in-dept. review of recent deep learning-based work on polyp segmentation.

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Design of an Efficient Model for Wheat Crop Disease Detection Using Multimodal Fusion and Uncertainty Quantification

Design of an Efficient Model for Wheat Crop Disease Detectio...

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Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Sadanand Prasad Sumit Chaudhary Purushottam Das Punit Kumar Madhu Kirola Harishchander Anandaram Dept. of Computer Science and Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Dept. of Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Dept. of Mechanical Engineering JB Institute of Technology Dehradun India Amrita School of Artificial Intelligence Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

The basic requirement for precise and proficient yield sickness discovery in wheat development has prodded progressions in rural innovation. Notwithstanding progress, existing strategies frequently miss the mark in completely coordinating assorted information modalities and tending to vulnerabilities inborn in expectation models. This study presents a novel strategy for wheat crop disease detection that overcomes these limitations using novel methods. First and foremost, the constraints of current techniques are outlined, underscoring their deficiency in utilizing multimodal information actually and evaluating vulnerability powerfully. These weaknesses highlight the need for novel philosophies able to synergistically incorporate assorted information sources and give dependable vulnerability gauges. A multifaceted framework comprising Graph Neural Networks (GNNs), Transfer Learning with Attention Mechanisms (TLAM), Bayesian Neural Networks (BNNs), and Graph-based Convolution Networks (GCNs) is what we propose as a solution to these issues. Every strategy is fastidiously picked for its capacity to bridle the intricacy of horticultural information, model complicated connections, and evaluate vulnerability, in this way upgrading the exactness and interpretability of illness location. The proposed approach displays a few benefits, including further developed infection location precision through multimodal combination, upgraded interpretability using consideration systems, vigorous vulnerability measurement utilizing Bayesian methods, and exact spatial-worldly investigation worked with by chart-based convolutions. These headways increase the viability of illness location as well as enable partners with noteworthy experiences for proactive sickness the executives and yield streamlining.

关键词： Technological innovation Uncertainty Computational modeling Transfer learning Measurement uncertainty Crops Graph neural networks Bayes methods Diseases Farming

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Web-enabled Vision-based Person Counting System for Smart Space Management 18

Web-enabled Vision-based Person Counting System for Smart Sp...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chohan, Jasgurpreet Singh Naser, Zamen Latef Sharif, Hayder Fezaa, Laith H. A. Dixit, Krishna Kant Bisht, Yashwant Singh University Center for Research and Development Chandigarh University Punjab Mohali India National University of Science and Technology Dhi Qar Iraq Al-Farahidi University Medical Technical College Iraq Gla University Dept. of Computer Engineering and Applications Mathura India Uttaranchal Institute of Technology Department of Mechanical Engineering Dehradun India

ISBN: (纸本)9789380544519

Efficient space management has emerged as a crucial issue in the era of smart spaces and IoT-enabled surroundings. In this study, an innovative method is proposed to solve the difficulties of real-time occupancy monitoring and resource optimization in various public and private venues. The system makes use of an ESP32-CAM module with a built-in camera to capture real-time images of the monitored region. The recorded images are analyzed using computer vision techniques and algorithms to track and detect people entering and leaving the area. The basis of the system is the person counting algorithm, which delivers precise and immediate occupancy statistics. The system allows users to log in from any location and monitor space occupancy in real time. The study was conducted based on the performance and accuracy of the system under various circumstances, indicating its appropriateness for a range of applications, from offices and retail outlets to public spaces and smart homes. The importance of this research is found in its contribution to the subject of smart space management, which is still in its infancy but where data-driven choices are essential for maximizing resource usage, guaranteeing social distance compliance, and improving user experiences. This web-enabled vision-based person counting system can help advance smart space management in the context of the Internet of Things (IoT) and smart cities as a creative and useful solution. In the end, the study provides a thorough description outlining its goals, approach, salient characteristics, and prospective effects on smart space management. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Blynk cloud app computer vision ESP32-CAM module smart space management Web-Enabled

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Innovative Blockchain and AI Solutions in Healthcare

Innovative Blockchain and AI Solutions in Healthcare

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Fayzeh Abdulkareem Jaber Redha Jasim Shaker Nour Abdulkareem Jaber Fadheela Hussain Ahmad AbdulKareem Jaber Vishwas Chakranarayan Dept. of Computer Studies University of Technology Bahrain (UTB) Manama Bahrain Dept. of Health Services Management University of La Verne California USA Dept. of Engineering University of Bahrain (UOB) Manama Bahrain Dept. of Administrative and Financial Sciences University of Technology Bahrain (UTB) Manama Bahrain

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Blockchain technology has emerged as a transformative including data security, interoperability, patient data ownership, and operational inefficiencies. This paper evaluates blockchain's impact on electronic health records (EHR) security, supply chain transparency, clinical research cooperation, and patient data control. Through comprehensive literature analysis, we highlight the benefits, limitations, and potential improvements for blockchain adoption in healthcare. Furthermore, a combined methodology of blockchain with artificial intelligence (AI) is presented, offering novel solutions to enhance healthcare data management and analytics. These findings lay a foundation for future research aimed at overcoming blockchain's current scalability and regulatory challenges to promote wider healthcare adoption.

关键词： Technological innovation Electric potential Scalability Supply chains Medical services Knowledge discovery Blockchains Artificial intelligence Electronic medical records Interoperability

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ClimateCast: ML-Powered Temperature Forecasting for North 24 Parganas

ClimateCast: ML-Powered Temperature Forecasting for North 24...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Abhirup Paria Anindya Ghatak Biswajit Jana Arpan Adhikary Priyanka Sen Ruma Das Dept. of Computer Sc. & Engg. (Artificial Intelligence & Machine Learning) Haldia Institute of Technology Haldia India School of Computer Sc. Engg. & Tech. Bennett University Greater Noida Uttar Pradesh Dept. of Computer Sc. & Engg. Haldia Institute of Technology Haldia India School of Applied Science & Humanities Haldia Institute of Technology Haldia India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Global warming is increasing at a rapid rate. Several efforts are made to understand its influence worldwide and in particular regions as well. Therefore, temperature prediction has become one of the most important and influential research topics because of its application in agricultural, ecological, industrial, and environmental sectors. This study reviews dif-ferent machine learning algorithms for temperature prediction and observes their superiorities. The area of study was done in North 24 Parganas, West Bengal, India. Parameters used in the dataset of the North 24 Parganas include wind speed, different types of UV radiance, cloud amount, soil moisture, precipitation rate, surface pressure, and specific and relative humidity. The relevant features are chosen from all the parameters using feature selection. The feature selection technique used is SelectKBest, using Mutual Info Regression. The different ML models used are KNN Regression, Linear Regression (LR), Decision Tree Regression, Support Vector Regression, Random Forest Regression, LightGBM Regression, XGBoost Regression, AdaBoost Regression and Ridge Regression. The experimental results were evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and R Squared (coefficient of determination). The result of this experiment revealed that XGBoost Regression has MAE, MSE, RMSE, and R Squared respectively 0.361, 0.273, 0.523, 0.989 followed by LightGBM Regression with MAE, MSE, RMSE and R squared followed by 0.366, 0.276, 0.526 and 0.988. These results show that XGBoost Regression possesses a high capability for temperature prediction

关键词： Support vector machines Temperature distribution Machine learning algorithms Accuracy Wind speed Soft sensors Soil moisture Feature extraction Vectors Regression tree analysis

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Demosaicing of Multispectral Imaging Camera data using Neural Networks

Demosaicing of Multispectral Imaging Camera data using Neura...

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BIT Conference (BITCON), International

作者： Sumita Gupta Sapna Gambhir Saru Dhir Simran Sapui Rana Majumdar Deepsikha Naskar Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP Department of Computer Science College of Engineering and Computing George Mason University USA Dept. of Information Technology Amity School of Engineering & Technology Amity University UP India Department of Computer Science Sister Nivedita University

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

In most digital cameras, the sensor uses a Bayer filter array to capture the image. This array records only one colour, either blue or green or red for every image pixel, resulting in a mosaic image. To retrieve the missing colour information in the captured image making use of cross-channel interpolation is referred to as demosaicing. Demosaicing, especially in the context of colour demosaicing (CDM), plays a vital role as the first step in obtaining high-quality colour images with single-chip cameras. Traditional demosaicing methods, used to reconstruct colour images from raw sensor data in digital cameras, have several drawbacks. They often result in a loss of spatial resolution, introduce artifacts, lack robustness in challenging conditions, and may require manual tuning for optimal performance. In contrast, neural networks offer advantages like end-to-end learning, improved image quality, robustness, flexibility, and state-of-the-art performance. They learn complex mappings directly from raw data, adapt to various conditions, and produce visually pleasing, high-resolution images. However, they require substantial training data and computational resources, making them less suitable for resource-constrained applications. In this paper, conventional interpolation methods are compared to deep-learning based approaches for image demosaicing. To validate the approach to image demosaicing, aerial images captured from the Mars Colour Camera have been used.

关键词： Image quality Interpolation Visualization Image color analysis Neural networks Training data Robustness Digital cameras Spatial resolution Tuning

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Improving GenIR Systems Based on User Feedback

arXiv

引用

arXiv 2025年

作者： Ai, Qingyao Dou, Zhicheng Zhang, Min Dept. of Computer Science and Technology Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

In this chapter, we discuss how to improve the GenIR systems based on user feedback. Before describing the approaches, it is necessary to be aware that the concept of"user" has been extended in the interactions with the GenIR systems. Different types of feedback information and strategies are also provided. Then the alignment techniques are highlighted in terms of objectives and methods. Following this, various ways of learning from user feedback in GenIR are presented, including continual learning, learning and ranking in the conversational context, and prompt learning. Through this comprehensive exploration, it becomes evident that innovative techniques are being proposed beyond traditional methods of utilizing user feedback, and contribute significantly to the evolution of GenIR in the new era. We also summarize some challenging topics and future directions that require further investigation. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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