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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Deshpande, Anjali Tribhuvan, Vinit Pune Institute of Computer Technology Department of Computer Engineering Pune India Coep Technological University Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Road traffic safety is a matter of crucial concern for the government as well as citizens. Despite all the measures taken to promote road traffic safety in India, there are a lot of regions of vulnerability. This leads to the need of an effective analysis to subdue alarming figures by significant amount. In an attempt to address this problem, we apply statistical analysis and data mining algorithms for analysis, prevention and prediction of road accidents on Indian national highways. To enhance the process of road risk analysis, proposed system puts forth an idea of analyzing the National Highways Authority of India (NHAI) datasets using Hidden Markov Model (HMM) and regression model supported by fuzzy classification. Our proposed system also has an alert module to provide an alarm and also some suggestions to the user through SMS depending upon the travel route. The implementation results indicate that the proposed method yields an accuracy of 84.55% approximately. © 2023 IEEE.

关键词： Baum-Welsh algorithm data mining fuzzy classification Hidden Markov Model (HMM) machine learning regression

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Dimensionality Reduction via Graph-based Feature Selection 8

Dimensionality Reduction via Graph-based Feature Selection

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Bojnal, Hrishik Sai Singh, Parnika Anitha, J. Rv Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Given the rapid influx of data, organizing and managing its features poses a significant challenge. Many features are deemed redundant, impeding efficient storage and training of models. We introduce a method to address this challenge by defining an algorithm that successfully achieves dimensionality reduction. By establishing a threshold, we identify pairs of features with significant linear correlations which are modelled as vertices and edges on a graph. Using graph theory principles, we can pinpoint non-essential features. By strategically removing these points, our method proceeds to streamline data management processes and enhances computational efficiency while outperforming Principal Component Analysis (PCA). © 2024 IEEE.

关键词： Dimensionality reduction

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Smart Trolley: A Smart Solution for Theft Prevention and Social Distancing in Shopping 7

Smart Trolley: A Smart Solution for Theft Prevention and Soc...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Shukla, Deepika Shah, Monika Institute of Technology Nirma University Department of Computer Science And Engineering Gujarat India

ISBN: (纸本)9798350359299

This work presents a novel smart shopping trolley integrating proximity sensors and load cells to address post-COVID-19 public health concerns. By providing real-time social distancing alerts and automatic billing, it enhances efficiency while minimizing contact points. Noteworthy features include scenario-tested distancing alerts and sensitivity to unique situations such as the presence of child in the trolley. This interdisciplinary effort merges insights from epidemiology, technology, and public health, emphasizing the transformative potential of scientific innovation. By fostering safer and more efficient public spaces, this smart trolley demonstrates the critical role of scientific advancements in safeguarding public health. © 2024 IEEE.

关键词： Crime

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Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey

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CAAI Transactions on Intelligence technology 2023年第3期8卷 581-606页

作者： Mehdi Gheisari Fereshteh Ebrahimzadeh Mohamadtaghi Rahimi Mahdieh Moazzamigodarzi Yang Liu Pijush Kanti Dutta Pramanik Mohammad Ali Heravi Abolfazl Mehbodniya Mustafa Ghaderzadeh Mohammad Reza Feylizadeh Saeed Kosari School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenChina Department of Cognitive Computing Institute of Computer Science and EngineeringSaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science Islamic Azad UniversityTehranIran Computer Engineering Department Bu‐Ali Sina UniversityHamedanIran Department of Mathematics and Statistics Iran University of Science and TechnologyTehranIran Department of Mathematics and Statistics University of Northern British ColumbiaBCCanada Peng Cheng Laboratory ShenzhenChina Deprtment of Technology Jodhpur Institute of Engineering&TechnologyJodhpurIndia Department of Computer Engineering Sadjad University of TechnologyMashhadIran Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyDoha DistrictKuwait Department of Artificial Intelligence Smart University of Medical SciencesTehranIran Department of Industrial Engineering Shiraz BranchIslamic Azad UniversityShirazIran Institute of Computing Science and Technology Guangzhou UniversityGuangzhouChina

Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new *** using DL,the extraction of advanced data representations and knowledge can be made *** effective DL techniques help to find more hidden *** learning has a promising future due to its great performance and *** need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the ***,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.

关键词： data mining data privacy deep learning

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems Sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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An evaluation study of non-contact fingerprint presentation attack detection

An evaluation study of non-contact fingerprint presentation ...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Tanuj Sharma, Ram Prakash National Institute of Technology Hamirpur Department of Computer Science & Engineering Hamirpur India

ISBN: (纸本)9798350376876

Non-contact based fingerprint recognition systems are emerging at rapid pace due the fast growing need in the era of contagious disease transmission i.e. COVID-19. This growth also necessitates an effective security system against possible Presentation Attacks (PAs). In recent years, widely popular Convolution Neural Networks (CNNs) have been deployed vastly for the contact based fingerprint Presentation Attack Detection (PAD) systems. In this work, we have evaluated and compared three widely popular deep CNN models namely, DenseNet121, ResNet50, and EfficientNet for the PAD in non-contact fingerprint recognition systems. The study was conducted on a new publicly available Contactless Fingerprint Spoof (COLFISPOOF) and Contact to Contactless (C2CL) dataset. The experiments were conducted using the standard baseline and four Leave-One-Out (LOO) protocols, to observe the generalization capabilities of the considered deep CNN models for unseen data. Overall results indicates that DenseNet121 model is able to generalize well for different evaluation protocols and provide average Attack Presentation Classification Error Rate (APCER) of 2. 8 1 % and Bona fide Presentation Classification Error Rate (BPCER) of 0.25%. © 2024 IEEE.

关键词： Palmprint recognition

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An Optimized SIMON Lightweight Image Encryption Algorithm for Internet of Things: Balancing Performance and Security 1

An Optimized SIMON Lightweight Image Encryption Algorithm fo...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Verma, Abhay Thokchom, Surmila National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India

ISBN: (纸本)9798350365016

The paper presents an optimized SIMON lightweight cryptographic algorithm that balances security, performance, and cost in contexts with limited resources. By lowering the computational cost and preserving strong security characteristics, the suggested changes make the algorithm appropriate for use in embedded systems, Internet of Things (IoT) devices, and mobile platforms. To improve encryption efficiency, changes have been made, such as fewer bit shift operations and the consolidation of bit-wise functions. A thorough evaluation process is applied to the updated algorithm, which involves calculating measures like information entropy, UACI (Unified Average Changing Intensity), and NPCR (Normalized Pixel Change Rate). Empirical findings show that the optimized SIMON algorithm significantly increases encryption efficiency and speed while maintaining the security of the algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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Precision Farming with Versatile Robotic Systems 2

Precision Farming with Versatile Robotic Systems

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sharan, D. Leelipushpam Paulraj, Getzi Jeba Jebadurai, Immanuel Johnraja Sharon Jemimah Peace, C. Karan, I. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350349221

This paper introduces a groundbreaking mobile agricultural robot tailored to tackle contemporary farming challenges. With precision pesticide application and effective weed removal, this robot offers a comprehensive solution for enhanced field management. Controlled through a smartphone interface, it ensures precise pesticide usage, reducing environmental impact and chemical wastage. The robot's userfriendly controls cater to various farming scales, providing flexibility for both manual and autonomous operations. Equipped with obstacle detection sensors and powered by sustainable solar energy, the robot prioritizes safety, equipment protection, and environmental responsibility. This innovative solution not only overcomes limitations in traditional practices but also aligns with the sustainability goals of modern agriculture, marking a transformative step forward in the industry. © 2024 IEEE.

关键词： Agricultural robots

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Glaucoma Detection Using Convolutional Neural Network

Glaucoma Detection Using Convolutional Neural Network

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2024 International Conference on Advances in Computing Research on Science engineering and technology, ACROSET 2024

作者： Khichi, Divya Jangde, Priyanka Sethi, Kamal Kumar Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

One of the quintessential examples in ophthalmology is the early detection of glaucoma, a disease that accounts for significant irrecoverable vision loss when caught later. Conventional diagnostic methods are based on visual inspection, which may be time-consuming and open to subjective interpretation. We suggest a deep learning method using the Convolutional Neural Network (CNN) model to solve this. With the ORIGA data (a labeled fundus image collection where each has been identified as containing glaucoma), we trained CNN to learn about complex spatial patterns that may discriminate against the disease. We can see a reduction in loss and improvement in accuracy through the graphs;our model is learning well. A confusion matrix with highly accurate favorable rates also shows the effectiveness of CNN, and its predictions individually were accurate. Our CNN model reduces loss by up to 49.3% and improves validation accuracy by nearly 3.97%, which is highly competitive compared to standard logistic regression and machine learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Application Deployment: GitOps for Faster and Secure CI/CD Cycles

Efficient Application Deployment: GitOps for Faster and Secu...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Saikiran Reddy, J. Durga Prashanth Padal, S. Albert Mayan, J. Catharine, A. Shanthamalar, J. Jeslin Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350371567

In the rapidly evolving landscape of technology, the need for swift and efficient deployment of software and applications has never been more critical. Due to the rapid and constant evolution of innovation, there is a consistent demand for quicker, seamless deployment of software and apps. Currently, most of the programming and applications are designed in such a manner that they may encounter diverse operational scenarios. The current study explores the crucial significance of agile approaches, with a particular emphasis on GitOps integration as a paradigm-shifting strategy in the modern software development environment. Leveraging DevOps best practices to underpin automation, GitOps is a useful solution that provides a way to accomplish safer and more efficient system cycles. Teams may improve security and viability while accelerating arrangement times by using Git as the one source of truth for foundation and application code through the use of GitOps. In order to improve the CI/CD pipeline and enable a more automated, straightforward, and simple exporting technique, this paper explores the potential real-world applications of GitOps concepts in AI application design. It looks at how agile approaches and GitOps practices work together to address the various and multifaceted problems that modern programming development encounters. The main emphasis is on how these approaches may guarantee a high level of adaptability and readiness in a technological environment that is always changing, in addition to being able to react quickly to changes. The aim is to explore innovative strategies and methods to enhance the speed and efficiency of software and application delivery, while examining the complexities of a constantly evolving technological landscape. © 2024 IEEE.

关键词： Machine learning

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