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Deep Evidential Active Learning with Uncertainty-Aware Determinantal Point Process 27th

Deep Evidential Active Learning with Uncertainty-Aware Dete...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhou, Yuxian Yue, Xiaodong Chen, Yufei Xie, Shaorong School of Computer Engineering and Science Shanghai University Shanghai China Artificial Intelligence Institute Shanghai University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9783031781063

Deep learning method requires a substantial amount of labeled data to achieve the state-of-the-art performance. However, annotating a large volume of data is often costly and impractical. Active Learning is a approach that reduces labeling costs by intelligently selecting and annotating the most crucial data points, which benefits from the integration of uncertainty and diversity as key criteria for sampling. Existing uncertainty-based methods often fall short in capturing the distinct sources of uncertainty, resulting in a diminished quality of uncertainty estimation. Additionally, clustering is commonly used to ensure diversity, which requires multiple iterations but overlooks the global correlations present throughout the entire unlabeled dataset. As a result, a well-designed ad hoc combination is essential to balance uncertainty and diversity. To address above limitations, we propose Evidential Uncertainty-aware Determinantal Point Process active learning method. Specifically, we employ the theory of Subjective Logic to measure multifaceted uncertainty including vacuity and dissonance. On these grounds, we first focus on the samples with high dissonance and employ Determinantal Point Process to select the samples considering both vacuity and diversity. The proposed method explores and exploits the information associated with the latent feature space near the decision boundary to select the most valuable samples. The experimental results on various real-world datasets reveal the superiority of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active learning

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Matri Seva: A Telemedicine software for Rural Healthcare Program of RKMHOS, Varanasi 4

Matri Seva: A Telemedicine Software for Rural Healthcare Pro...

引用

4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Kundu, Surajit Das, Soumyajit Mukhopadhyay, Jayanta Divyasukhananda, Swami School of Medical Science & Technology Indian Institute of Technology Kharagpur Kharagpur India Indian Institute of Technology Kharagpur Department of Computer Science & Engineering Kharagpur India Luxa Uttar Pradesh Varanasi India

ISBN: (纸本)9798350389128

Telemedicine system provides an alternative to patients by facilitating virtual clinical consultations over traditional hospital visits. Healthcare benefits ranging from quality treat-ment and access to specialist doctors for residents of rural parts at times of need have proven to be beneficial. Additionally, rural patients employed as daily wage workers save time and money, thereby encouraging them to stay connected throughout the year. It is gradually showing the potential to change the healthcare delivery system for the better especially in the rural areas. At the Department of computer science and engineering, IIT Kharagpur, a open source telemedicine software, iMediXcare, has been developed. A customized version of this telemedicine software, Matri Seva has been developed and deployed to support the rural healthcare program of Ramakrishna Mission Home of Service (RKMHOS), Luxa, Varanasi through its department, Vivek Sanjivani, An Initiative for Healthcare and Education. Vivek Sanjivani provides free telemedicine services to people in rural parts of Uttar Pradesh and Uttarakhand, India. © 2024 IEEE.

关键词： Telemedicine

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Revolutionizing Banking Cybersecurity: Deep Learning Strategies to Thwart Ransomware Attacks

Revolutionizing Banking Cybersecurity: Deep Learning Strateg...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Kumari, Ankita Gupta, Deepali Uppal, Mudita Kumar, Komuravelly Sudheer Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798331505134

Ransomware attacks which use system flaws to lock private data and mess with important financial operations threaten the banking industry more and more. Significant financial losses, harm of image, and falling client trust follow from these strikes. Conventional protection solutions are not always able to match the smart and changing character of ransom-ware. This work offers a new way of banking protection by use of deep learning techniques to successfully address malware risks. Deep learning algorithms are used to quickly find and stop malware activity as they can study huge amounts of data and spot detailed trends. This paper uses a proactive and adaptable approach to safeguard financial institutions from the rising risk of ransom-ware attacks, therefore demonstrating the potential of deep learning to alter banking cybersecurity. Examining coming and leaving data packets helps the system to see any dangers and stop them cold. An artificial intelligence-based technique helps it to more exactly spot emerging risks. In this view, in addition to stopping ransom-ware from getting access to private e-commerce data, the effectiveness of the warning system has dropped financial loss and brand damage. Moreover, in the tests this model showed an 89% accuracy rate, therefore reducing ransom-ware-related risks. Including an artificial intelligence-enabled smart chip into an e-commerce system helps businesses to improve their security stance. This means that they are fewer likely targets of ransom-ware attacks. It therefore became a necessary tool for properly addressing cyber security issues and lowering the risk ransom-ware brought to companies. © 2024 IEEE.

关键词： Cyber attacks

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Enhancing Cryptocurrency Value Prediction: A Comparative Study of Novel Random Forest and K-Nearest Neighbor Algorithms for Improved Accuracy 5

Enhancing Cryptocurrency Value Prediction: A Comparative Stu...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kiruthiga, S. Balamanigandan, R. Mahaveerakannan, R. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Department of Spatial Informatics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350379945

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P © 2024 IEEE.

关键词： k-nearest neighbors

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Securing Smart Homes: Machine Learning Solutions for IoT Cyber Threats

Securing Smart Homes: Machine Learning Solutions for IoT Cyb...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Khan, Aadil Gupta, Deepali Dutta, Monica Lande, Jayapal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798331505134

Smart home technologies simplify light and temperature management, making life easier. These technologies provide smartphone-based home management. IoT improves smart home equipment efficiency and customization. IoT gadgets are linked, so hackers may access security cameras, personal data, and smart locks and thermostats, endangering people and property. A solid cybersecurity policy helps manage these security issues. Cyber threat detection early allows rapid reaction and mitigation, minimizing harm and preventing escalation. Active security breach detection reduces sensitivity and improves cybersecurity resilience by limiting consequences, forbidding illegal access, and protecting vital data. To increase smart home security, this initiative uses Aposemat IoT-23 dataset and machine learning methods. We tested XGBoost, Simple Neural Network, GaussianNB, and Logistic Regression for illegal access and data injection detection. XGBoost and Simple Neural Network show their abilities to safeguard smart home networks by identifying and preventing dangerous activity with over 99% accuracy. These results illustrate how efficiently machine learning algorithms cover IoT devices in smart homes, hence delivering a safer and more trustworthy environment for consumers. © 2024 IEEE.

关键词： Smart homes

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Advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution 2nd

Advances in smart farming for precision agriculture: Green-I...

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2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Digra, Monia Rajput, Preeti Sharma, Pooja Model Institute of Engineering and Technology J&K Jammu India School of Computer Science and Engineering Shri Mata Vaishno Devi University J&K Katra India

ISBN: (纸本)9781032738659

Agricultural development is crucial to ending poverty and boosting economic growth in emerging nations. The global population is expected to reach 9.7 billion by 2050. Smart and precise agriculture must enhance agricultural productivity without compromising quality, nutrition, or natural resources. IoT and AI/ML have significant uses in agriculture due to internet technologies. Although IoT has many benefits, the enormous number of networked devices may raise carbon emissions and e-waste due to energy consumption. Inspired by the goal of sustainable smart agriculture, this article addresses major results and research gaps in IoT-based smart agriculture and provides solutions for energy-saving Green-IoT (G-IoT) applications in agriculture. We start with G-IoT’s evolution and smart agriculture applications. We also examine how green architecture may efficiently apply AI/ML in smart agriculture. We propose an AI/ML G-IoT framework for smart agriculture with green sensor cloud and green AI. This unique G-IoT paradigm provides sustainable and energy-efficient agricultural architecture. We investigate statistical and experimental data and emphasise the limitations of adopting G-IoT and AI/ML architecture in agriculture. In conclusion, G-IoT and AI/ML architecture can enable sustainable, energy-efficient, smart precision agriculture. © 2025 The Author(s).

关键词： Energy utilization

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Triple Diseases Prediction System using Deep Learning Techniques

Triple Diseases Prediction System using Deep Learning Techni...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： R. Vijayan V. Mareeswari A. B. Jaswanth Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu Department of Software and Systems School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu MTech (Software Engineering) School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu

With the existing deep learning models in predicting multiple diseases primarily focus on analyzing individual diseases in isolation, lacking a unified system for multi-disease prediction. This project presents an approach to predict multiple diseases using Flask API, with a specific focus on brain tumors, COVID-19 and pneumonia. The proposed work represents a significant contribution to the field of disease prediction, harnessing the power of deep learning algorithms and modern web application development. The primary focus is on disease prediction, with a particular emphasis on ensuring accuracy and accessibility for end-users. The initial phase of this research involves data collection, where relevant datasets of various diseases are gathered. These datasets serve as the foundation for training and validating the deep learning models. Two prominent deep learning algorithms, Sequential CNN and VGG16, are employed for this purpose. These algorithms are chosen for their ability to handle complex data and recognize patterns within medical images and other health-related data. The core of the research involves training the deep learning models using the collected datasets. This step is crucial in enabling the models to learn and generalize from the provided data, ultimately enhancing their predictive capabilities. The models are modified to elevate their performance and accuracy. Following the training phase, the models are rigorously tested to evaluate their predictive accuracy. This assessment is vital in gauging the real-world applicability of the models in medical diagnosis. To make these powerful disease prediction models accessible to a wider audience, a front-end web application is developed.

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Enhancing Tourism Experiences Through Immersive Technologies: The Role of Virtual and Augmented Reality 9

Enhancing Tourism Experiences Through Immersive Technologies...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Vashisht, Sanchit Sharma, Bhanu Aluvala, Srinivas Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350377972

This study examines the influence of immersive technologies, particularly Virtual Reality (VR) and Augmented Reality (AR), on the tourism industry, emphasizing recent developments and the obstacles encountered in their integration. VR and AR deliver transforming experiences through virtual tours, interactive guides, and augmented on-site information, so redefining the marketing, exploration, and enjoyment of destinations. Current systems encounter obstacles like elevated development expenses, technical intricacies in attaining realistic and engaging user experiences, and constraints in accessibility and scalability across many platforms. This study emphasizes cutting-edge technologies such Unity3D, Vuforia, and Blender 3D, elucidating their functions in the creation of a VR and AR application aimed at enhancing tourism experiences. A framework is designed to address these problems, delivering scalable, user-friendly, and visually immersive applications that may establish new benchmarks for accessibility and environmental sustainability in tourism. The findings illustrate the potential of VR and AR to enhance travel experiences by making them more engaging, accessible, and sustainable, highlighting the necessity for continuous study and innovation to fully realize their capabilities within the tourism sector. © 2024 IEEE.

关键词： Virtual environments

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Semantic-Guided Voting with Segmentation for Visual Localization 6

Semantic-Guided Voting with Segmentation for Visual Localiza...

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6th International Conference on software engineering and computer science, CSECS 2023

作者： Wang, Chao Xie, Dong Cao, Qingjun Guo, Rui Zhu, Feng Nanjing Research Institute of Electronic Engineering Nanjing China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350306378

Scene coordinate regression has significantly enhanced visual localization by segmenting landmark patches and aggregating votes for landmark points within these patches. However, this approach neglects dynamic semantic objects and patch-level semantic inconsistencies, leading to inaccuracies in the voting process. In this paper, we present a novel visual localization framework guided by semantics. Our framework introduces two critical modules: the Pruning Semantically Dynamic Object module, which leverages semantic segmentation to reduce interference from dynamic semantic objects, and the Eliminating Semantic Label Inconsistencies module, aimed at mitigating patch-level semantic inconsistencies. Comprehensive tests on 7-Scenes and Cambridge datasets exhibit the superior performance of our approach. Across both datasets, our method outperforms the scene coordinate regression technique, validating the effectiveness of our proposed framework. © 2023 IEEE.

关键词： Semantics

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Smart Expiry Tracker and Product Recommender using Google ML Kit OCR Engine 2

Smart Expiry Tracker and Product Recommender using Google ML...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Krishanamurthy, Vallidevi Kurumthottam, Aleena Bosco Ali, Shireen Aliza Anandan, Paul Danny Kavitha, Muthu Subash Karthikeyan, K. Kumaresh, V. Manikandan, S. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Mechanical Electrical and Manufacturing Engineering Loughborough University United Kingdom School of Information and Data Sciences Nagasaki University Japan Senior Data Analyst Ninjacart Bangalore India Software Development Engineer Vegrow Chennai India iOS App Developer Facilio Chennai India

ISBN: (纸本)9798331504106

Expiration dates play a crucial role in both personal and business contexts, influencing everything from health management to operational efficiency. Track Expiry Dates (TED) is a comprehensive mobile application designed to address two key needs: document management and expiry tracking. We designed a secure app which offers users to store personal documents such as driving licenses, passports, general documents, product warranty cards, and bills, ensuring that critical information is readily accessible and safeguarded against loss or damage. The proposed system provides a seamless and efficient solution for tracking expiry dates and storing essential documents such as Aadhar cards and PAN cards. It automatically sends reminders to users and their nominated family members prior to document expiration, ensuring timely renewals. From a business perspective, the platform enables companies to leverage data from uploaded documents to offer targeted, content-driven recommendations for products and brands. With its intuitive user interface and robust back-end infrastructure, the app delivers a comprehensive system for document management and expiry tracking, effectively meeting the diverse requirements of both individual users and businesses. ©2024 IEEE.

关键词： Driver licensing

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