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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rishi, Midhvana Dubey, Vaibhav Bhardwaj, Mukesh Kumar Singh, Mahesh Kumar Dronacharya Group of Institutions Department of Computer Science Engineering India

ISBN: (纸本)9798350375237

IoT is a lightning-fast technology that uses clever objects or stuff that speak uncomplicatedly to the work of our daily lives. Smart homes, wearables, connected automobiles, vibrant urban communities;savvy retail, agribusiness, healthcare, and other areas are some of the most well-known applications of IoT. This document provides an overview of IoT Crypto a safe platform Internet of Things communication. Typically, they have elements from Internet of Things (IoT), such as the device functionality they need, the requirement to restrict the amount of information sent, and the ability to connect to the Internet. With real business relationships in mind, Innovative lightweight authentication setup and trust concepts are provided by IoT-Crypto. Include a secure communication protocol that only makes use of one DTLS connection that is encrypted. This article discusses and presents the unique components and nuanced execution details of IoT crypto in terms of essentially equivalent arrangements. Post-implementation testing and analysis of the IoT encrypted network have proven its accuracy and safety. Additionally, a test network was conducted to determine if the BLE IPSP profile and coding standards are suitable for IoT. Based on these results, the significance of future research was discussed. The IoT paradigm and communication protocols are introduced at the outset of this work and then compare countermeasures for a few security vulnerabilities at each tier of the IoT model to protect such a large and diverse company. © 2024 IEEE.

关键词： Internet of things

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Gas-Efficient Decentralized Random Beacons 6

Gas-Efficient Decentralized Random Beacons

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Abidha, V.P. Barakbayeva, Togzhan Cai, Zhuo Goharshady, Amir Kafshdar Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350316742

Decentralized random number generation is a widely-studied problem in the blockchain community and much attention has been paid to the so-called on-chain random beacons, i.e. smart contracts that generate randomness which can in turn be used in other contracts. Following the classical methodology of RANDAO, most on-chain beacons receive inputs from a large number n of participants and then aggregate them to compute a final random output. The aggregation is done in a manner that ensures the final output is uniformly random as long as at least one of the participants acts honestly. While being highly successful in providing security guarantes such as unpredictability and tamper-resistance, a major downside of these beacons is their cost. Since every participant has to call a function in the smart contract to provide their input, the total gas usage to generate a single random number is at least Ω(n). In this work, we propose a novel protocol that offloads most of the on-chain communication between the participants and the smart contract to an alternative off-chain communication with a dealer. This leads to a gas-efficient on-chain random beacon with only O(1) gas usage per generated output. Crucially, our protocol is trustless and the dealer is unable to predict or tamper with the result. We maintain the same security guarantees as previous on-chain beacons, while significantly reducing the gas usage. We also show that our protocol is secure even if all but one of the participants, potentially including the dealer, are dishonest. © 2024 IEEE.

关键词： Smart contract

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Improving the Prediction and Fine-Grained Approach for Predicting and Forecasting Cryptocurrency Price Using ARIMA 4

Improving the Prediction and Fine-Grained Approach for Predi...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sega, J.Juslin Shivam Gautam, Yash Yadav, Kshitij Devendra Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350349900

A capacity of foreseeing price fluctuations in bitcoin with exceptionally precise is very worthwhile to investigators and funding sources. However, as the cryptocurrency market is nonlinear, it can be challenging to determine the distinctive features of time-series data, which renders it challenging to forecast accurate price estimations. Massive oscillations in non-stationary cryptocurrency values underscore the pressing necessity to precise forecasting models. The most effective methodology for cryptocurrency price forecasting is machine learning, foremost ensemble and deep learning. Traditional statistical methods are difficult to execute accurately because to the lack of seasonal variations and the need to meet a number of naive requirements. The suggested methodology builds upon the random walk theory, commonly utilized in financial markets to model stock values. To simulate market volatility, this methodology utilizes randomization into the observed feature activations of neural networks at a layer-wise level. Moreover, a mechanism to assess the market's reaction pattern is incorporated into the prediction model. Training was conducted on ARIMA and LSTM, short for Long Short-Term Memory models using Ripple, Ethereum, and Tron as illustrative examples. © 2024 IEEE.

关键词： Prediction models

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Webcam-Based OCR System with Text-to-Speech Conversion 5

Webcam-Based OCR System with Text-to-Speech Conversion

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： Optical character recognition

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Perceptual Fairness in Image Restoration 38

Perceptual Fairness in Image Restoration

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ohayon, Guy Elad, Michael Michaeli, Tomer Faculty of Computer Science Technion-Israel Institute of Technology Israel Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology Israel

Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to be a correct outcome for a group (e.g., women) only if it falls within the group's set of ground truth images (e.g., natural images of women);otherwise, it is considered entirely incorrect. Consequently, such definitions are prone to controversy, as errors in image restoration can manifest in various ways. In this work we offer an alternative approach towards fairness in image restoration, by considering the Group Perceptual Index (GPI), which we define as the statistical distance between the distribution of the group's ground truth images and the distribution of their reconstructions. We assess the fairness of an algorithm by comparing the GPI of different groups, and say that it achieves perfect Perceptual Fairness (PF) if the GPIs of all groups are identical. We motivate and theoretically study our new notion of fairness, draw its connection to previous ones, and demonstrate its utility on state-of-the-art face image restoration algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Innovative Deep Learning Methods for Soil Classification and Crop Yield Prediction

Innovative Deep Learning Methods for Soil Classification and...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Modi, Soham Kumar Chahat Yadav, Anup Lal Rana, Pratham Singh Kalpana Chandigarh University Department Of Computer Science And Engineering Punjab India

ISBN: (纸本)9798350306446

The significance of soil in sustaining life on Earth cannot be overstated, particularly its role in crop production and environmental sustainability. This paper presents a analysis of various machine-learning models employed in soil classification and their implications for crop productivity. Soil texture and nutrient composition are pivotal factors influencing crop growth and sustainability. Leveraging machine learning techniques, such as classification algorithms and computer vision, this study evaluates the effectiveness of Decision Trees (DT), k nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) in automating the soil classification process. These models utilize detectable features including Soil characteristics like moisture, nutrients, structure, quality, pH, and texture to classify soil types accurately. Additionally, techniques like interpolation of soil particle size fractions using transformed data are explored. By examining the performance and efficiency of these models, this research aims to contribute insights into optimizing soil management practices for enhanced crop production and environmental sustainability. © 2024 IEEE.

关键词： Neural networks

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Collective Diagnostic Prototypical in Internet of Medical Things for Depression Identification using Deep Learning Algorithm

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Recent Patents on engineering 2024年第9期18卷 24-33页

作者： Selvarajan, Shitharth Thirumalaisamy, Manikandan Manoharan, Hariprasath Devendiran, Ramkumar Lakshmi Narayanan, S. Kumar, Ashwani Department of Computer Science Kebri Dehar University Kebri Dehar250 Ethiopia School of Computer Science Engineering and technology Bennett University U.P India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Tamil Nadu Chennai600 123 India School of Computer Science and Engineering VIT-AP UNIVERSITY Andhra Pradesh Amaravathi522237 India Gojan School of Business and Technology Chennai India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

Background: The majority of wearable technology that is present in various patents for Internet of Medical Things (IoMT) health monitoring systems is introduced to recognize various bodily indicators. The enumerated patents indicate that monitored values are sent to a central server, where they are all treated by experts at the appropriate moment. Therefore, a new patent technique by expanding the use of wireless devices, has been discovered that such communication technologies can recognize specific depression traits and mood swings. Objectives: The major objective of the proposed method is to analyze the disputes that arise in the characteristics of an individual by observing the leveling periods that are identified from the processed image. In addition, the rate of data transfer in case of any dispute is maximized therefore recognition problem is solved at a minimized distance. Further, the steady state probability values are achieved at low delay thus minimizing the dropout packets in the monitored system using IoMT and LSTM. Methods: A balanced record with four distinct parameters—such as livelihood, self-reliance, correlation, and precision—is employed with the projected model on IoMT for depression identification. As a result, high data transfer rates and low distance separation are used to process the identification framework. Additionally, by combining an original matrix representation with the input feature set using LSTM, a novel framework with great efficiency is created. Results: In order to assess the results of IoMT using LSTM, four situations are split apart and their probability ratios are calculated. The results of each situation are then contrasted with the current methodology, and it is found that when there is a low dropout ratio, depression in a person is quickly diagnosed. Conclusion: The comparison analysis demonstrates that the proposed method, when compared to the current method, offers the best-compromised outcomes at roughly 64%. © 2024 B

关键词： Data transfer

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PPDHero: Requirements Elicitation and Development of a System to Empower New Mothers on Postpartum Depression 23th

PPDHero: Requirements Elicitation and Development of a Sy...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Sara, Tasfia Akter Nazifa, Sadia Nur Tasneem, Shadmanee Shakib, Tasnim Ullah Islam, Muhammad Nazrul Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9783031648496

This research aims to develop a novel system for screening postpartum depression (PPD) and for providing education and support to new mothers to identify and manage symptoms and connect with health professionals. A requirement elicitation study was conducted following a semi-structured interview (n=6) and survey (n=102) and revealed a set of user requirements that need to be addressed in the proposed system. As such, extensive research and analysis of PPD and related information sources, including healthcare professionals, academic research papers, and online resources, served as a foundation for the development of the web-based system PPDHero. This system goes a step beyond by infusing gamification, setting us apart from the existing digital landscape. The usability of the proposed system was also evaluated through the System Usability Scale (SUS) approach, which was replicated with 18 participants including new mothers and doctors. The SUS scores received from the doctors (84.58) and new mothers (71.79) showed an acceptable quality of the proposed system from a usability perspective. The proposed PPDHero can improve the lives of new mothers by providing them with accessible and efficient tools to manage PPD symptoms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Usability engineering

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Synthetic Data Augmentation Using Large Language Models (LLM): A Case-Study of the Kamyr Digester 3

Synthetic Data Augmentation Using Large Language Models (LLM...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Dhruva, G. Bhat, Ishani Rangayyan, Sanika M. Preethi, P. Pes University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350369083

Traditional machine learning approaches are plagued by data insufficiency and scarcity, making their performance suboptimal regardless of the sophistication of the overarching algorithms. While researchers have employed varying augmentation techniques, such extrapolations are inherently constrained by the limited knowledge space of the initial dataset. Therefore, collecting a balanced, robust and extensive dataset is of paramount importance in the realm of machine learning, despite the challenges associated with collecting such data. This study introduces a novel augmentation technique using Large Language Models (LLMs) to synthetically generate data for mitigating potential data scarcity. It presents a comprehensive case-study of the "Kamyr Digester"used in the paper-and-pulp industry, to demonstrate the advantages of using LLMs to assuage data insufficiency. © 2024 IEEE.

关键词： Paper and pulp industry

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Self-supervised few-shot medical image segmentation with spatial transformations

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Neural Computing and Applications 2024年第30期36卷 18675-18691页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science & amp Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Deep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational resources for training. However, a promising solution to mitigate these challenges is the adoption of few-shot segmentation (FSS) networks, which can train models with reduced annotated data. The inherent complexity of medical images limits the applicability of FSS in medical imaging, despite its potential. Recent advancements in self-supervised label-efficient FSS models have demonstrated remarkable efficacy in medical image segmentation tasks. This paper presents a novel FSS architecture that enhances segmentation accuracy by utilising fewer features than existing methodologies. Additionally, this paper proposes a novel self-supervised learning approach that utilises supervoxel and augmented superpixel images to further enhance segmentation accuracy. This paper assesses the efficacy of the proposed model on two different datasets: abdominal magnetic resonance imaging (MRI) and cardiac MRI. The proposed model achieves a mean dice score and mean intersection over union of 81.62% and 70.38% for abdominal images, and 79.38% and 65.23% for cardiac images. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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