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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Kaur, Prabhjot Kumar, Nitin School of Computing DIT University Uttarakhand India Department of Computer Science and Engineering National Institute of Technology Uttarakhand India Department of Computer Science and Engineering PEC Deemed to be University Chandigarh India

ISBN: (纸本)9789819717231

Biometric cryptosystems are security tools that use an individual’s distinctive physical or behavioral characteristics to secure sensitive data. Any biometric cryptosystem’s effectiveness is dependent on how well the features are extracted. The suggested solution uses an evolutionary algorithm to first optimize the retrieved attributes. Second, fuzzy vault is used to safeguard these optimized features. The fuzzy vault system is a well-known biometric cryptography device that creates a vault by using a polynomial equation, protecting the biometric information. However, if an attacker has access to the polynomial equation, the security of the fuzzy vault scheme may be jeopardized. The shuffled frog leaping algorithm (SFLA) method has been suggested as a way to improve the security of the fuzzy vault technique in order to overcome this drawback. The SFLA is a meta-heuristic optimization method that resolves challenging optimization issues by simulating the social behavior of frogs. This meta-heuristic technique is used in this paper’s suggested strategy to produce and secure optimized points. The experiments are performed onn AMI and MMU2 databases. The results achieve higher levels of performance compared to the traditional schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optimization

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Biometric-Based Key Handling Using Variation to Scale Invariant Feature Transform amid COVID-19 Pandemic 7th

Biometric-Based Key Handling Using Variation to Scale Invar...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Kaur, Prabhjot Kumar, Nitin Singh, Maheep Department of Computer Science and Engineering National Institute of Technology Uttarakhand Uttarakhand Srinagar India School of Computing DIT University Uttarakhand Dehradun India Department of Computer Science and Engineering PEC Deemed-to-be University Chandigarh India Department of Computer Science and Engineering Doon University Uttarakhand Dehradun India

ISBN: (纸本)9789819950140

The outbreak of COVID-19 pandemic has hardened the biometric recognition, authentication, and cryptographic process based on facial features due to masks coverage. The Biometric cryptosystem (BCS) consisting of touch sensing biometric, such as fingerprint, palmprint, vein, is no longer in use in this scenario. In this context, other key-based biometric cryptosystems based on facial biometric may underperform. Thus, there is a need to simulate existing and implement novel biometric cryptosystems based on other biometric cues such as Ear, Face, Iris, Gait. This paper introduces a novel method for biometric key generation based on Scale Invariant Feature Transform (SIFT). The obtained key is used to encrypt/decrypt the data. At enrollment stage, the biometric key is obtained which subsequently encrypts the data and authentication phase decrypts the secret upon successful biometric match. The proposed method is compared with the state-of-art methods on three datasets namely AMI (ear), FACES (face), and UBIPr (iris). The metrics used to evaluate the performance of proposed method include: NPCR, UACI, PSNR, Correlation, MSE, MAE, SSIM, NRMSE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics

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A Machine Learning Approach for Predicting Dengue Outbreak 3

A Machine Learning Approach for Predicting Dengue Outbreak

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3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Kaur, Lovjot Bhatia, Rajesh Nagpal, Shweta PEC University of Technology Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9781665438117

Dengue, well known as a deadly mosquito disease, is speedily spreading around the globe. The transmission cycle of Dengue virus is highly supported by the Temperature, Relative Humidity and Rainfall conditions as these factors promote its growth and habitat. Till date, there is no vaccination or medication available for Dengue fever. In lack of medical support, the only way to prevent Dengue is by controlling the spread of its vector. In this study, a Hidden Markov Model (HMM) is proposed for Dengue outbreak prediction taking into account the data set of previous 10 years. It elaborates the firm relationship of the Dengue outbreak and meteorological factors. The results obtained from the model predicts the outbreak with accuracy of approx- imately 93.38%.This model can be successfully implemented for control and prevention of dengue outbreak. © 2021 IEEE.

关键词： Hidden Markov models

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Optimizing Resource Allocation in Healthcare Facilities Through IoT and Machine Learning Predictive Analysis 3rd

Optimizing Resource Allocation in Healthcare Facilities Thro...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Mukherjee, Saptarshi Rahman, Mirzanur Barshe, Shweta B. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Makaut Maulana Abul Kalam Azad University of Technology West Bengal Nadia Kolkata India Department of Information Technology Gauhati University Assam Guwahati India Department of Computer Engineering Bharati Vidyapeeth College of Engineering Mumbai Navi India

ISBN: (纸本)9789819751457

In the evolving landscape of health care, optimal resource allocation emerges as a critical determinant of patient outcomes and facility efficiency. This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) predictive analysis in healthcare facilities to optimize resource allocation. With the omnipresence of IoT devices and the analytic prowess of ML, our research aims to develop a model that can predict real-time resource demands, ranging from patient care equipment to manpower distribution. Drawing data from wearable sensors, embedded devices, and facility infrastructure, the proposed ML model demonstrates significant improvement in predicting short-term resource needs. Preliminary results indicate a reduction in resource wastage, enhanced patient care, and increased operational efficiency in healthcare facilities that incorporated the proposed system. However, the research also acknowledges potential limitations, including data security concerns and system scalability. The findings presented highlight the transformative potential of IoT and ML in revolutionizing resource management in health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Embedded devices Internet of Things (IoT) Machine learning (ML) Operational efficiency Wearable sensors

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Optimized Prognostic Models for Oral Cancer Survival using Feature Selection Methods

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Procedia computer science 2024年 235卷 1832-1840页

作者： Deepali Neelam Goel Padmavati University Institute of Engineering and Technology Panjab University Chandigarh 160014 India Assistant Professor Department of Computer Science Guru Nanak College Budhlada 151502 India Department of Computer Science Punjab Engineering College (deemed to be university) Chandigarh 160012 India

Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinical, DNA methylation, copy number alteration, and mRNA data. The proposed model incorporates four feature selection methods, namely LASSO, Elastic Net, Random Forest Regressor, and Recursive Feature Elimination (RFE), to improve the accuracy. The findings reveal that RFE outperforms the other methods, achieving the highest C-Index of 0.944. This is a significant improvement over previous models, which have a c-index of 0.694 using only clinical data and 0.916 using multi-omics data. In terms of precision, the Random Forest Regressor is the most precise with an MSE of 18.37, followed by RFE of 21.61. Notably, both RFE and Random Forest Regressor outperformed LASSO and Elastic Net in predictive accuracy. Additionally, the results highlight the consistency of RFE, as indicated by its low Median Absolute Error (MedAE) of 2.86. Random Forest Regressor also performed well in this regard, with a MedAE of 4.05. In contrast, Elastic Net exhibited a higher MedAE of 4.55, while LASSO showed more variability, with a MedAE of 8.58. These findings offer valuable insights into survival analysis and feature selection, helping researchers and practitioners select appropriate approaches for survival time prediction, and improving accuracy and reliability in critical applications.

关键词： Oral Cancer Survival Time Feature Selection Accuracy

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Design and Implementation of an IoT-Based Control System for Precision Food Manufacturing 3rd

Design and Implementation of an IoT-Based Control System for...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Suneel, Sajja Veeramanikandan, P. Anil Kumar, N. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Institute of Aeronautical Engineering Telangana Dundigal Hyderabad India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9789819751457

The food processing sector is undergoing a rapid transformation, primarily driven by the infusion of groundbreaking technologies like the Internet of Things (IoT). This study aims to explore the creation and incorporation of an IoT-centric monitoring and control mechanism within food processing to amplify its quality, safety, and efficiency. By melding sensor technologies, instantaneous data interpretation, and cloud tech, we crafted a sophisticated monitoring framework specifically for the diverse demands of food processing. The results highlighted substantial enhancements in live tracking, product traceability, and foresight-based upkeep, resulting in reduced waste and streamlined production timelines. Moreover, the system's versatile nature allows its use across various food processing domains, ranging from milk products to baked goods. This technological integration not only denotes financial gains for the sector but also suggests a transformative change in the industry's perspective toward ensuring quality, safety, and efficiency in our increasingly interconnected era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Processed foods

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Analysis of parkinson's disease diagnosis using a combination of genetic algorithm and recursive feature elimination

Analysis of parkinson's disease diagnosis using a combinatio...

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2020 World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020

作者： Goyal, Jinee Khandnor, Padmavati Aseri, Trilok Chand Pec University of Technology Computer Science and Engineering Chandigarh India

ISBN: (纸本)9781728168234

Parkinson's disease is a neurological disorder which affects different body parts. Voice distortion is one of the early symptoms to appear. It is present in most of the Parkinson patients and has a lot of potential in early diagnosis of the disease. In this paper, potential of feature selection techniques is explored in diagnosis of Parkinson's disease using speech features. In this, two-stage feature selection is applied which combines the benefits of Genetic Algorithm and Support Vector Machine-Recursive Feature Elimination techniques into one. Classification is done using SVM and results are compared with SVM without feature selection and individual selection techniques. The two-stage feature selection technique not only reduced the dimensionality of the dataset but also improved the performance of the system. © 2020 IEEE.

关键词： Genetic algorithms

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Machine Learning in K–12 Educational Settings

Machine Learning in K–12 Educational Settings

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ramy Riad Al-Fatlawy Dadamuxamedov Alimjon R. Udayakumar Ashish Sharma Jaspreet Kaur S. Padmanaban Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Senior Lecturer at the Department of "Modern Information and Communication Technologies" of International Islamic Academy of Uzbekistan Tashkent Uzbekistan Dean CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Science and Humanities Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and *** article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.

关键词： Training Technological innovation Philosophical considerations Education Machine learning Learning (artificial intelligence) Market research Telecommunication computing Programming profession Next generation networking

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Analysis of Parkinson's disease diagnosis using a combination of Genetic Algorithm and Recursive Feature Elimination

Analysis of Parkinson's disease diagnosis using a combinatio...

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World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)

作者： Jinee Goyal Padmavati Khandnor Trilok Chand Aseri Computer Science and Engineering PEC University of Technology Chandigarh India

ISBN: (数字)9781728168234

ISBN: (纸本)9781728168241

关键词： Feature extraction Support vector machines Genetic algorithms Parkinson's disease Statistics Sociology Biological cells

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Optimizing Hindi Paragraph Summarization through PageRank Method

Optimizing Hindi Paragraph Summarization through PageRank Me...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Sheshang Degadwala Dhairya Vyas Khush Nilaykumar Patel Mukesh Soni Pavitar Parkash Singh Ramya Maranan Associate Professor & Head of Computer Engineering Department Sigma University Vadodara Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India Charotar University of Science and Technology Anand Gujarat Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab India Lovely Professional University Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

This research paper presents a novel approach to optimize Hindi paragraph summarization using the PageRank method. With the increasing volume of Hindi language content on the internet, there is a growing need for effective summarization techniques tailored specifically for Hindi text. The proposed method leverages the inherent structure and connectivity of Hindi paragraphs to extract the most important sentences for summarization. The PageRank algorithm is applied to construct a rank representation of the text, with sentences as nodes and their co-occurrence patterns as edges. By calculating PageRank scores iteratively, the algorithm identifies the most central and significant sentences in the paragraph. Linguistic features such as sentence length, position, and content relevance are incorporated to enhance the quality of the summaries. Additionally, the algorithm dynamically adjusts sentence importance based on their relations with other sentences rank. Experimental results on a comprehensive dataset demonstrate that the PageRank-based approach outperforms existing summarization techniques in terms of informativeness and coherence, as evidenced by higher similarity score. The proposed method offers an effective solution for optimizing Hindi paragraph summarization, facilitating the management and comprehension of Hindi text in various applications.

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