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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Akbar Basha, K.M. Gumasthi, Nikhil Baswade, Anand M. Indian Institute of Technology Bhilai Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The saturated licensed spectrum is insufficient to meet the increasing demand for data traffic, which paves the path for exploring new bands. Since the unlicensed bands are free of cost, efforts are made to offload the cellular data to unlicensed bands, which results in unlicensed versions of cellular technologies. New Radio Unlicensed (NR-U) is the unlicensed version of New Radio (NR). Since anyone can access unlicensed frequency, the devices follow listen before talk (LBT) to gain access to the unlicensed band. Unlike WiFi, the dominant technology in unlicensed bands, the cellular node can not transmit data immediately after the LBT procedure. The node has to wait for the next licensed slot boundary to occur since it is scheduled technology. However, the LBT procedure can end before the occurrence of slot boundary;the period after LBT to the slot bound boundary by sending a reservation signal (RS) or staying idle using a gap. The RS approach yields high fairness with less efficiency, whereas the gap-based approach gives better fairness with comparatively less fairness. To deal with the tradeoff in fairness and efficiency, we have proposed a hybrid channel access method that uses both gap and RS approaches. In the proposed method, the duration of RS is limited by a threshold, and the RS is transmitted as fragments. The proposed method achieves 54 % to 128 % higher fairness than the gap-based approach and 10 % to 28 % than the RS-based approach for the given simulation scenario. © 2024 IEEE.

关键词： Channel access Coexistence LBT NR-U Unlicensed bands Wireless networks

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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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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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Enhancing Deployment and Performance Measurement of Serverless Cloud Microservices with Warm Start 15

Enhancing Deployment and Performance Measurement of Serverle...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Amrutha, L. Jayalakshmi, D.S. Geetha, J. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

This paper introduces a comprehensive framework for deploying and evaluating the performance of applications in serverless cloud environments for microservices. It addresses challenges stemming from the distributed nature of microservices and the imperative to protect sensitive data. Key aspects covered include architectural design considerations, emphasizing decomposition into independent microservices with clear interfaces. Robust security mechanisms such as encryption, authentication, and authorization are discussed for safeguarding data shared between microservices. Deployment strategies on serverless cloud platforms like AWS Lambda are explored, highlighting benefits like automatic scaling and cost efficiency. The framework also integrates warm start techniques to mitigate cold start latency issues commonly associated with serverless computing. Techniques for monitoring and logging performance metrics such as responses times, error rate, and supply usage are discussed. Real-world case studies demonstrate the practical implementation and effectiveness of the framework in overcoming challenges associated with deploying an application in serverless cloud environments for microservices. © 2024 IEEE.

关键词： Cloud platforms

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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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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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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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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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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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