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Revolutionizing Transportation Infrastructure and Communication technology through Blockchain and 5G Simulation 2

Revolutionizing Transportation Infrastructure and Communicat...

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Pallavi, N. Naik, Darshana A. Soumya, C.S. Mahadesh, Jayanth Braganza, Joseph A. Singh, Prakhar Nanda, Rohith Ramaiah Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350306415

This paper presents the outcomes of two key projects dedicated to the enhancement of modern transportation infrastructure and communication technology. The first project introduces a web-based application for toll tax collection, with a particular focus on bolstering security and transparency through the implementation of blockchain technology. By leveraging blockchain, this system successfully mitigates fraud and enhances accountability. Simultaneously, our second project utilizes the Open Air Interface for simulating 5G network node *** toll tax collection project yields substantial results by establishing a secure, decentralized platform through blockchain integration. This, in turn, leads to a notable reduction in fraud and a significant improvement in accountability. The system utilizes an immutable ledger and smart contracts to streamline toll collection processes, ultimately enhancing operational efficiency. In tandem, the 5G network simulation project contributes to the advancement of network technology, delivering improvements in efficiency, transparency, and user experience within toll collection systems, while also catalyzing the evolution of state-of-the-art communication infrastructure. © 2024 IEEE.

关键词： 5G Networks 5G OpenAirInterface (OAI) Blockchain technology Cash-Based Transactions Environmental Impacts Fraudulent Activities High Operational Costs Manual Toll Booths Operational Inefficiencies Toll Collection Method Traffic Congestion User Convenience Vehicular Ad-Hoc Networks (VANETs)

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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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Statistical curriculum learning: An elimination algorithm achieving an oracle risk 37

Statistical curriculum learning: An elimination algorithm ac...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Cohen, Omer Meir, Ron Weinberger, Nir Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Israel

We consider a statistical version of curriculum learning (CL) in a parametric prediction setting. The learner is required to estimate a target parameter vector, and can adaptively collect samples from either the target model, or other source models that are similar to the target model, but less noisy. We consider three types of learners, depending on the level of side-information they receive. The first two, referred to as strong/weak-oracle learners, receive high/low degrees of information about the models, and use these to learn. The third, a fully adaptive learner, estimates the target parameter vector without any prior information. In the single source case, we propose an elimination learning method, whose risk matches that of a strong-oracle learner. In the multiple source case, we advocate that the risk of the weak-oracle learner is a realistic benchmark for the risk of adaptive learners. We develop an adaptive multiple elimination-rounds CL algorithm, and characterize instance-dependent conditions for its risk to match that of the weak-oracle learner. We consider instance-dependent minimax lower bounds, and discuss the challenges associated with defining the class of instances for the bound. We derive two minimax lower bounds, and determine the conditions under which the performance weak-oracle learner is minimax optimal. © 2024 O. Cohen, R. Meir & N. Weinberger.

关键词： Contrastive Learning

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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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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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SE-VGGNet: Liver Tumor Detection Using Squeeze Excited VGG Network 2

SE-VGGNet: Liver Tumor Detection Using Squeeze Excited VGG N...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Jose, Andrews Juliet, Sujitha Anitha, J. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837242672

Liver tumors, including metastatic cancer and cholangiocarcinoma, pose a significant threat to global health, necessitating accurate and timely detection. This paper presents a novel deep learning model, SE-VGGNet, for liver tumor classification, which innovatively combines Squeeze-and-Excitation (SE) blocks with a VGGNet backbone. This integration enhances feature extraction by allowing the model to recalibrate channel-wise feature responses, focusing more effectively on the most relevant image regions. SE-VGGNet was evaluated on a liver tumor dataset, achieving a classification accuracy of 99.09%, precision of 98.15%, and recall of 100%, significantly outperforming traditional CNN and VGG-16 models. The novelty of this approach lies in its ability to achieve high performance even with small datasets, addressing a critical challenge in medical imaging. Additionally, this method offers the potential for broader application to other medical imaging tasks, highlighting its adaptability and impact on early cancer diagnosis and clinical decision-making. © The Institution of engineering & technology 2024.

关键词： Deep learning

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Decentralized Fund Management System for Indian Temples Powered by Blockchain 8

Decentralized Fund Management System for Indian Temples Powe...

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

作者： Thaker, Hriday Bachute, Aryan Mahajan, Ayushi Darekar, Omkar Dhumane, Amol Chiwhane, Shwetambari Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350391770

This research provides a proposal to integrate blockchain technology with a web application to come up with a solution for the fund management issues that the temples face. The main purpose of the research is to solve the problems of transparency, corruption and trust amidst donors and authorities of temple. Enabling the combination of a database management arrangement and a user-friendly website, a private blockchain was developed. The website is powered by smart contracts and serves as a solution that is capable of making fund operations accountable and visible. The outcomes of using this solution showed improvement in transparency, less corruption and increase of trust among donors. The temple authorities became more answerable and efficient as donors observed the movement of the donations in real time. This study gives a possible solution for temples to get the trust of their members back in order to ensure that the donations are properly implemented and a generation of cultural heritage is conserved. © 2024 IEEE.

关键词： Decentralized finance

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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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FoodExpert: Portable Intelligent Device for Rapid Screening of Pulse Quality and Adulteration

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第1期2卷 2-11页

作者： Pandey, Harsh Arya, Subhanshu Das, Debanjan Udutalapally, Venkanna International Institute of Information Technology Naya Department of Electronics & Communication Engineering Naya Raipur493661 India National Institute of Technology Department of Computer Science & Engineering Warangal506004 India

Pulses are one of the most important food crops in the world due to their higher protein content, approximately 21%-25%. Therefore, it is crucial to analyze the crop's quality and impurity levels. Stones, pebbles, marble chips, and synthetic dyes, such as lead chromate, metanil yellow, and artificial colors, are some of the impurities added to pulse products, accidentally or on purpose. The existing analysis techniques are mostly laboratory-based, time-consuming, costly, and require human examination. To address this issue, this article presents an intelligent system, FoodExpert, based on image processing that automatically uses an image of a pulse sample to identify the region of interest and essential attributes. Then, machine learning frameworks are used to predict pulse quality and adulteration levels based on the obtained parameters. On the test dataset, the suggested model had a 96% accuracy rate for pulse quality prediction and 94% accuracy for adulteration level prediction. The model was successfully deployed on a Raspberry Pi-based hardware prototype and mobile application. © 2023 IEEE.

关键词： Feature extraction

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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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