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18th International Conference on Network and System Security, NSS 2024

作者： Chakraborty, Partha Sarathi Tripathy, Somanath Department of Computer Science and Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819635306

The desire to delegate computation to the cloud arises from the proliferation of small, computational restrained devices. However, to ensure the security and privacy of the stored data in the cloud, data owners always encrypt their sensitive data before outsourcing it. Nonetheless, computation outsourcing proves beneficial only when the returned result is trustworthy. Moreover, it must ensure the security and confidentiality of the computation and data during execution. Thus, the challenge lies in efficiently verifying the correctness of computation results generated over encrypted data and maintaining practical computational time for verifying results. Blockchain emerges as a trustworthy entity, enabling fairness and transparency in the scheme. We propose a secure and efficient smart contract based publicly verifiable polynomial computation with an incentive mechanism to protect confidentiality. In the proposed scheme, the smart contract is a verifier that ensures trusted verification and manages incentive transfer. We demonstrate the proposed scheme’s effectiveness through security proofs and performance analysis, significantly reducing verifier computational costs and enhancing overall efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Polynomials

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Natural Language Processing in Geographic Information Systems 3

Natural Language Processing in Geographic Information System...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic Information Systems (GIS) with Natural Language Processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 IEEE.

关键词： Structured Query Language

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PlaceTech: An AI-enabled solution for smart placement 5th

PlaceTech: An AI-enabled solution for smart placement

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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Alfatmi, Khalid Pande, Sakshi Shahade, Makarand Suryawanshi, Vaibhavi Badgujar, Kalpesh Patil, Vishwajit Kulkarni, Mayuri Department of Computer Engineering SVKM’s Institute of Technology Dhule India

ISBN: (纸本)9781032642987

Educational institutions must keep pace with evolving technology, particularly in managing student placements. The current manual processes for students and Training and Placement Officers (TPOs) involve resume creation, job matching, and administrative tasks, emphasizing the need for automation and efficiency. PlaceTech: An AI-Enabled Solution for Smart Placement introduces a unified platform for students and TPOs to streamline placement processes. It offers a dynamic resume builder, AI-driven chatbot, and online assessments for students, while providing TPOs with efficient tools to manage job description postings and events. This innovation promises to enhance placement through smart automation and improved communication. Institutional and departmental TPOs gain the ability to categorize students, manage job descriptions, and analyze placement data, fostering greater efficiency. By centralizing and automating these operations, PlaceTech embodies a technology-driven approach, providing a scalable solution for streamlined placement process, with implications extending beyond education to various industries. © 2024 The Author(s).

关键词： Students

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Biometric Identification using Periocular Images with ViT-DeepSort and YOLOv7-GAN 7

Biometric Identification using Periocular Images with ViT-De...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Verma, Pratishtha National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350350067

Advanced biometric identification techniques are becoming increasingly necessary in response to rising security requirements. The limits of conventional facial recognition systems have been further brought to light by the COVID-19 outbreak and its emphasis on mask-wearing. utilizing periocular pictures, this study proposes an integrated technique to improve gender categorization and biometric identification utilizing Vision Transformers (ViTs), YOLOv7, DeepSort, and Generative Adversarial Networks (GANs). Our findings indicate that the combined application of these technologies results in a system that is both robust and accurate, particularly in challenging environments. The proposed methodology demonstrates a notable improvement in identification accuracy, with a precision rate exceeding 95%. With this research, secure identification technologies will advance significantly, and non-intrusive, contactless biometric devices will continue to be developed. The findings suggest that the application of these cutting-edge techniques has the potential to completely transform security procedures in industries including public safety, healthcare, and finance. © 2024 IEEE.

关键词： COVID-19

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Blockchain Based Fake Product Detection System: Enhancing Supply Chain Transparency 1

Blockchain Based Fake Product Detection System: Enhancing Su...

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

作者： Pandey, Shweta Singh, Awadhesh Kumar National Institute of Technology Kurukshetra Department of Computer Engineering Haryana India

ISBN: (纸本)9798350365016

Supply chain management faces numerous challenges, such as service redundancy problems with interdepartmental coordination, and a lack of standardization that is made worse by a lack of transparency. Since traditional methods of detection, such as RFID tags, artificial intelligence, and QR code systems, have limitations, product counterfeiting is a serious threat to legitimate businesses. By tracking the products' supply chain history, the decentralized architecture, data transparency, and immutability of blockchain technology are used by the system in order to guarantee the identification and traceability of authentic products. This paper presents an innovative solution combining blockchain technology, QR codes, and machine learning to detect fake products effectively. The innate security characteristics of blockchain technology provide strong defense against data manipulation and vulnerabilities, protecting the interests of consumers and legitimate businesses. © 2024 IEEE.

关键词： Blockchain

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Impact of Constraint Modifications on the CRYSTALS-Kyber Hardware Design 31

Impact of Constraint Modifications on the CRYSTALS-Kyber Har...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Bohra, Dixit Dutt Banerjee, Dip Sankar Sanadhya, Somitra Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

The lattice-based post-quantum cryptography scheme is widely regarded as one of the most secure solutions to address the emerging threats posed by advancements in quantum computing. Various groups have analyzed and studied the hardware implementation of the CRYSTALS-KYBER design, and they continue to work on optimizing the design. Researchers have applied various techniques throughout the design to enhance power consumption, area utilization, and output speed. The main goal of this work is to present the impact of constraints on design performance, analyzing factors such as Worst Negative Slack (WNS) and total power. After introducing input and output delay constraints, we found that the actual operating frequency was 91.29 MHz, deviating significantly from the initially claimed 169.92 MHz. © 2024 IEEE

关键词： Quantum computers

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Spam SMS Detection Using Natural Language Processing 8

Spam SMS Detection Using Natural Language Processing

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Rajput, Satpalsing D. Chopade, Pratiksha Chivate, Atharva Chitpur, Shreeshail Dashetwar, Isha Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350391770

A spam SMS is an unsolicited text message dispatched to a large population, illegitimately, without the authorization of the recipients. Reports suggest that the number of Spam SMS received by citizens of any country is approximately 400 million, and this number is increasing day by day. These SMS messages may contain malicious links, causing security threats including phishing attacks and malware infections. Some spam SMS messages can involve fraudulent schemes, leading to financial losses for the receiver and can also cause privacy threats. Hence, there is an urge to detect these spam SMSs correctly and avoid the severe consequences for the recipients. The proposed study emphasizes the development of Natural Language Processing based Machine Learning models to identify spam SMS texts. The process involves tokenization of the SMS texts, stemming of the words, and many more abstractions of the keywords for easier classification. Firstly, the text messages are processed using NLP and then the processed data is trained on different ML algorithms. Multiple ML models are created and tested on various kinds of SMS messages for the detection of spam messages. This expert system ensures accurate and precise detection of received messages. This expert system intends to reduce the threats and safeguard the security and privacy of the consumers, by essentially recognizing and steering clear of spam SMS messages. This system aims to empower users with the tools and knowledge needed to navigate the digital landscape safely and securely. © 2024 IEEE.

关键词： Expert systems

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Weighted Ensemble Learning for Accident Severity Classification Using Social Media Data

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SN computer Science 2024年第5期5卷 528页

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, D.V.L.N. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal 506004 India Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Andhra Pradesh Kurnool 518002 India

Due to the heavy increase in social media usage, like Twitter, there is a growing interest in the research community in developing automation tools like accident severity-based tweet classification models. These tools aid in automatically extracting severity information from the accident tweet content. Moreover, prediction models are essential for predicting the severity of an accident to increase the safety efficacy of the road traffic system. However, the difficulty lies in the collection of sufficient labeled data. We propose a model called weighted ensemble-based self-training with decision tree (WESTDT), a semi-supervised methodology with a dynamic data labeling strategy. The base classifier in this model is a homogeneous weighted ensemble of decision tree classifiers for better prediction of pseudo labels. We also propose a novel performance measure called risk factor to estimate the amount of risk present in the application using the prediction model. The proposed model outperformed the state-of-the-art model, namely reliable semi-supervised ensemble learning (RESSEL), and the baseline models, namely decision tree (DT) and self-training with decision tree (STDT), in terms of both the traditional and the proposed measures. The results indicate that the proposed framework outperforms all other models on all datasets in terms of precision, recall, and accuracy by a range of 5–18.3%, 3.9–9.3%, and 6.6%, respectively. These findings can be helpful for the development of efficient and sustainable systems for traffic management and safety. It is also crucial for assisting government authorities in devising prompt, proactive strategies to prevent traffic accidents and enhance road safety. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accident severity classification Decision tree Self-training Semi-supervised learning Weighted ensemble learning

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AI based solution for Predicting Hepatitis C Virus from Blood Samples

AI based solution for Predicting Hepatitis C Virus from Bloo...

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2024 International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2024

作者： Bharathi, P.T. Bindu, S.N. Deepthi, S.G. Gunakeerthi, H.U. Harshitha, K.U. Siddaganga Institute of Technology Department of Computer Science & Engineering Karnataka India

ISBN: (纸本)9798350364040

Hepatitis - C Virus (HCV) infection stands as a prominent cause of chronic liver disease on a global scale. Predicting HCV poses a significant challenge in the medical field, as the abundance of healthcare data remains underutilized. To tackle this issue, it is imperative to collect relevant data, extract valuable insights and employ them effectively. Consequently data mining holds immense potential across various industries and scientific domains. The objective of this research work is to meticulously analyze and visualize the dataset's variables aiming to identify essential factors related with HCV. Given the substantial prevalence of HCV, its impact on liver health and potential fatality emphasize the urgent and potential need to develop an accurate prediction models. However, the classification modelling like Support Vector Machine, Decision Tree, Logistic Regression & Random Forest were implemented with the help of feature selection and without feature selection. From the results, it is observed Random Forest with feature selection out performed with an accuracy of 89% and without feature selection with an accuracy of 88.33%. These models are indispensable in contemporary society. © 2024 IEEE.

关键词： Logistic regression

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Vision Semantics Image Captioner

Vision Semantics Image Captioner

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Pereira, Gavin Kundar, Risha Kushwaha, Devendra Shaikh, Phiroj Don Bosco Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350382693

This paper introduces the completed project development of a cutting-edge Vision Semantics Image Captioner., a comprehensive platform aimed at generating contextually rich descriptions for images. Focused on leveraging advancements in vision semantics, our system utilises state-of-the-art techniques to provide detailed and meaningful captions for a wide array of images. The primary objective of this research is to enhance the understanding and interpretation of images through automated captioning, contributing to the fields of computer vision and natural language processing. The image captioner goes beyond basic descriptions by incorporating semantic nuances, ensuring that the generated captions capture the essence and context of the visual content. Our platform addresses the need for a reliable and efficient image captioning system by offering a user-friendly interface and incorporating robust algorithms. The implementation plan involved thorough literature surveys, enabling us to integrate the latest advancements in vision semantics into the image captioning process. In addition to the technical aspects, the paper discusses the practical applications of the Vision Semantics Image Captioner, emphasizing its potential in various domains such as accessibility, content indexing, and assistive technologies. The research also includes guidelines for optimising and customizing the system for specific use cases. By presenting a comprehensive solution for image captioning, this research contributes to the advancement of automated visual understanding. The platform's capabilities are demonstrated through a series of evaluations, showcasing its effectiveness in generating accurate and meaningful image captions. We believe that the Vision Semantics Image Captioner has the potential to revolutionize the way images are interpreted, providing valuable insights across diverse applications. © 2024 IEEE.

关键词： Intelligent systems

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