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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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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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Exploring Deep Learning Models for Kidney Stone Prediction: A Comparative Study of ResNet and SENet Architectures 15

Exploring Deep Learning Models for Kidney Stone Prediction: ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Terrance Clifford, B. Raj, A. Samson Arun Department of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331300579

This study investigates the use of deep learning models to forecast the development of kidney stones. We perform extensive preparation using a Kaggle dataset in order to get the data ready for analysis. We separately apply the ResNet and SENet designs, leveraging the residual learning of ResNet and the squeeze-and-excitation method of SENet, to determine their effectiveness in collecting complex patterns suggestive of the presence of kidney stones. By means of comprehensive training and assessment, we measure the predicted precision of every model. In the context of kidney stone prediction, a comparison of the ResNet and SENet designs highlights their distinct advantages and disadvantages. Our results not only shed light on the differences in performance between various architectures but also highlight the potential of deep learning methods to improve kidney stone prediction-related medical diagnostics. This study offers insightful information that can guide the creation of prediction algorithms for kidney stone identification that are more precise and effective, improving patient outcomes and healthcare procedures. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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An Integrated Development Of IoT Based Machine Learning For Data-Driven Travel Recommendations

An Integrated Development Of IoT Based Machine Learning For ...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Rifath, Shakeel Sridhar Xavier, Constance Brindasri, S. Hindustan Institute Of Technology And Science Computer Science And Engineering Department Chennai India

ISBN: (纸本)9798350364828

An innovative methodology is introduced in this Research to enhance data-driven travel decision-making by integrating IoT technology and data science techniques. With a specific focus on air and sound pollution, the research utilizes real-time sensor data from various urban sources to develop predictive models for forecasting future pollution levels. These models serve as the backbone for decision support tools seamlessly integrated into open-source platforms like ThingSpeak and IFTTT, facilitating proactive travel planning for both the general public and patients. The core of this methodology lies in the real-time aggregation of IoT sensor data, offering users comprehensive insights into prevailing air pollution levels at their intended destinations. Augmented by predictive machine learning algorithms such as logistic regression and decision trees, the system's real-time monitoring capabilities are significantly enhanced, with the decision tree algorithm particularly noted for its accuracy in assessing destination safety for patients. The forecasting accuracy and reliability of the model are upheld through rigorous training on historical data. Notably, the synergy between ThingSpeak and IFTTT enables the delivery of personalized travel recommendations grounded in predictive analytics and real-time environmental monitoring. This research underscores the pivotal role of data-driven decision-making in travel planning, empowering patients with tailored guidance derived from robust data analysis. © 2024 IEEE.

关键词： Predictive analytics

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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computer Modeling in engineering & Sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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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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A Novel Machine Learning-Based Approach for Analysis, Prediction and Prevention of Accidents on Indian National Highways 7

A Novel Machine Learning-Based Approach for Analysis, Predic...

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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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Echo Guidance: Voice-Activated Application for Blind with Smart Assistive Stick Using Machine Learning and IoT

Echo Guidance: Voice-Activated Application for Blind with Sm...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Abishek Kumar, G. Surya, G. Sathyadurga, V. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

Navigating the world without sight presents profound challenges for millions of visually impaired individuals. Echo Guidance includes a pioneering application, addresses these obstacles by facilitating independence and mobility through voice-driven interaction and an IoT smart assistive device for obstacle detection by utilizing Ultrasonic, GPS and GSM modules powered by Arduino. This study explores the design and evaluation of Echo Guidance, highlighting its utilization of advanced algorithms like RGB888 conversion, matrix transformation, and Optical Character Recognition (OCR) for real-time object recognition. The application boasts a voice-centric interface tailored to the needs of the visually impaired, seamlessly integrating day-to-day functionalities such as weather forecasting, reminders, and a calculator accessible through voice commands. Central to Echo Guidance is its innovative object detection and recognition approach, employing the MobileNetObjDetector class powered by TensorFlow Lite for swift and accurate object detection. The OverlayView class visually represents detected objects, enhancing spatial awareness. Additionally, the integration of IoT devices, such as the smart stick with ultrasonic sensors for obstacle detection and head-level obstacle detection, coupled with SOS functionality in case of any emergency using GPS and GSM modules, further enhances Echo Guidance's capabilities. This research underscores Echo Guidance's transformative potential in improving the quality of life for visually impaired individuals, representing a significant advancement in assistive technology. Prioritizing accessibility and user experience, this paper empowers individuals worldwide with newfound independence and confidence in navigating their surroundings. © 2024 IEEE.

关键词： Linear transformations

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Deep Learning Model for UAV aided Traffic Analysis and Vehicle Classification 5

Deep Learning Model for UAV aided Traffic Analysis and Vehic...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kumar, Mayank Anwar, Shamama Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India

ISBN: (纸本)9798350389609

Intelligent transportation systems (ITS) depend upon a range of communication that allows for data exchange between vehicles and traffic information data centers to create applications for providing information like traffic alert in view of lane closure, traffic congestion, track unsafe driving behaviour, optimize routes and emergency management, etc. An important component in this setup are video clips of highways or cross-section areas which needs to be monitored. Traditionally, a closed circuit television (CCTV) is the most often used method of monitoring traffic information, but have limitations like fixed positions, small monitoring region leading to numerous CCTV installations which are costly. A UAV, unlike CCTV, is capable of recording an extensive area of roadways by elevating its altitude or altering its location, as well as travel to a specific spot to monitor unforeseen circumstances such as crashes. Additionally, a UAV observes highways perpendicularly, thus the automobiles in the captured footage do not overlap. This paper explores the issue of vehicle identification on highways using UAV images through deep learning models. Deep learning approaches can automatically learn problem-specific features, eliminating the need for standard feature extraction procedures. Three pre-trained deep learning based model VGG16, ResNet-18 and EfficientNetB3 models have been used for the vehicle classification problem, which is essentially a five class classification problem. The highest accuracy of 97% is reported by the EfficientNet model. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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