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An Optimized Steganographic Technique for 24-Bit Digital Images using Slab Partitioning

An Optimized Steganographic Technique for 24-Bit Digital Ima...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Acharjya, Pinaki Pratim Majumdar, Jayeeta Barman, Subhabrata Sinha, Jayashri Deb Ghorai, Ramsundar Ghosh, Arpan Haldia Institute of Technology Dept. of Computer Science and Engineering West Bengal Haldia India Haldia Institute of Technology Dept. of Computer Science and Engineering (Data Science) West Bengal Haldia India

ISBN: (纸本)9798331512088

Based on image slabs and sheets, a novel kind of image steganography method for digital images has been proposed in this research work. Using the binary representation of the pixel, this approach splits the cover image into 24 fixed-size sheets. The secret message's binary counterpart is then incorporated into the cover image. It looks through each sheet's rows and columns to locate the one that most closely matches the secret message's binary counterpart. The row or column's position and how it differs from the message it stores in the LSB slab. Compared to previous ways, the proposed technique somewhat modifies the cover image. © 2025 IEEE.

关键词： digital image image slab secret message steganalysis steganography

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Leveraging AI-Generated Image for Scene Classification: A transfer Learning Approach

Leveraging AI-Generated Image for Scene Classification: A tr...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Seher Khan Krishna Kumar Singh Dept. of Data Science and Data Analytics Symbiosis Centre for Information Technology Pune India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

When it comes to scene classification, the lack of labelled data is a significant obstacle that forces researchers to think about innovative solutions. This work presents a revolutionary method to strengthen scene classification models by combining transfer learning with AI-generated images. A key component of this project is the construction of a carefully curated dataset of 3672 augmented AI-generated photos in a total of 6 scenes. This database not only serves as a helpful resource for additional research but also makes a substantial contribution to this area of research. Using this dataset, a VGG16 model is trained, demonstrating the usefulness of synthetic data with a notable increase in accuracy. The model's performance is further improved through subsequent training using realistic photos through transfer learning, thereby surpassing the initial baseline. This work demonstrates the transformative effect of transfer learning on scene categorization accuracy and verifies its effectiveness using synthetic data through rigorous testing and evaluation. The results open the door for AI-generated photos to be used as an effective approach to address data scarcity issues in scene classification, which will help real-world applications improve.

关键词： Training Scene classification computer vision Adaptation models databases Computational modeling Transfer learning

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Analysis of Prediction of Pneumonia from Chest X-ray images using CNN and Transfer Learning 5

Analysis of Prediction of Pneumonia from Chest X-ray images ...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Vinay, K. Shashanka Kodipalli, Ashwini Swetha, P. Kumaraswamy, S. Dept. of Computer Science and Engineering Global Academy of Technology Karnataka Bengaluru India Dept of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350361155

A good design for image classification problems is the CNN architecture you presented, which consists of 5 convolution blocks followed by 4 fully connected layers. From the input X-ray images, the convolutional blocks extract pertinent features, and the fully connected layers assist in determining the final classification based on those learned features. You have integrated various approaches to improve the performance of your model. The inputs to each layer are normalized through batch normalization, which can speed up training and enhance generalization. By removing certain neurons at random during training, dynamic dropout helps avoid overfitting. L2 regularization weight decay and learning rate decay are two efficient strategies for preventing overfitting and enhancing the model's capacity to expand to new data. Popular optimization algorithm Adam optimizer effectively neural network training. For binary classification problems like the diagnosis of pneumonia, the loss function for binary Cross-Entropy is the best option. To determine your model's efficacy, you must validate it using benchmark datasets that are available to the general public. You can evaluate your model's effectiveness by comparing its performance to that of current methods by conducting experimental investigations on these datasets. Your model performs well as evidenced by accuracy scores of 90.93%, 89.17% for multi-class classification and binary classification. tasks. Automated methods, such as the one you suggested, might help medical practitioners recognize pneumonia and spot diseased spots in chest X-ray pictures. However, it's crucial to remember that automated systems shouldn't take the place of professional radiologists' and doctors' skills and judgment;rather, they should be used as supportive tools. Medical To ensure accurate diagnosis and suitable patient care, specialists should always review and interpret the system's data. It's also crucial to take into account potential drawback

关键词： Convolutional neural networks

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Internet of Things IoMT data Aggregation Using Machine Learning 2

Internet of Things IoMT Data Aggregation Using Machine Learn...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Pulabaigari, Lakshmi Sagar Sivakumar, C. School of Computing Mohan Babu University Dept. of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Mohan Babu University Dept. of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350372847

The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learning (ML) applications to be used on IoT datasets, such as 10MT data aggregation. This paper discusses the use of a ML approach for aggregation 10MT data in order to better understand trends and classify patterns in various sectors that would otherwise go undetected. It discusses the benefits and challenges of such a system, and how it could be used to improve the efficiency of industrial operations. It stresses the importance of data integrity in this process, as wrong or incomplete datasets can significantly reduce the accuracy and reliability of the results obtained. Furthermore, it discusses the need for suitable ML algorithms and models, such as clustering and random forest, to be applied to the collected data in order to produce robust and meaningful output. It observes the security and privacy challenges associated with such ML-based 10MT data aggregation systems, and the potential solutions available. © 2024 IEEE.

关键词： Internet of things

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A Study on Innovative Work Behavior with Reference to Software Development Companies

A Study on Innovative Work Behavior with Reference to Softwa...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Kavita Jaimini Nanda Timsy Kapoor Dept. of Computer Science and Data Analytics Prestige Institute of Management and Research (PIMR) Indore Madhya Pradesh India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Innovation is a vital component of success in the software development industry, where companies constantly seek to improve processes, products, and services. While individual creativity and motivation are essential, organizational factors also play a significant role in shaping innovative work behavior. This study delves into the complex interplay between individual and organizational factors that influence innovative work behavior in software development companies. Using a mixed-methods approach, we collected survey data from software developers, conducted in-dept. interviews, and observed work practices. Our results reveal those individual factors like creativity, autonomy, and motivation, as well as organizational factors like leadership support, resources, and a culture of innovation, significantly impact innovative work behavior. We also found that organizational factors moderate the relationship between individual factors and innovative work *** study contributes to the understanding of innovative work behavior in software development, highlighting the importance of aligning individual and organizational factors to foster a culture of innovation. Our findings provide actionable insights for software development companies to develop strategies that promote innovative work behavior, drive competitiveness, and enhance success in the industry. By understanding the interplay between individual and organizational factors, companies can create an environment that encourages creativity, experimentation, and innovation, leading to the development of novel software solutions and improved business outcomes.

关键词： Industries Surveys Technological innovation Leadership Employment Companies Software Interviews Creativity Software development management

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Heart Abnormality Detection Through Neural Network

Heart Abnormality Detection Through Neural Network

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Sarada, B. Smitha Chowdary, Ch. Babu, Jampani Satish Kavitha, K. Dept. of Data Science Secunderabad India Koneru Lakshmiah Education Foundation Dept. of Cse Guntur India P.B.Siddhartha College of Arts and Science Dept. of Computer Science Vijayawada India

ISBN: (纸本)9798350300857

This research study aims to develop an algorithm using phonocardiograms and Deep learning to differentiate between normal and abnormal heart sounds. The increase in cardiovascular diseases (CVD's), primarily caused by unhealthy habits, necessitates efficient diagnosis. The study utilized 1055 data samples, with 833 being normal and 182 abnormal, each lasting 5 seconds. Logistic regression and a three-layered neural network were initially applied, yielding accuracies of 42% and 63%, respectively. By extracting features using spectral roll-off and reapplying the models, accuracies improved to 79% and 86%. Up sampling the data further increased the accuracies to 86% for logistic regression and 89% for the three-layered neural network. The findings demonstrate the potential of audio-based algorithms in diagnosing cardiovascular diseases accurately. © 2023 IEEE.

关键词： Multilayer neural networks

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Wormhole Attack Detection in the Internet of Things using a Hybrid Deep Learning Model 3

Wormhole Attack Detection in the Internet of Things using a ...

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

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Dept. of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Dept. of Data Science Sri Krishna Adithya College of Arts and Science Tamil Nadu Coimbatore India Kerala Thrissur India Kerala Mala India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Internet growth has led to widespread adoption and transmission of the IoT in numerous areas. From personal networks to industrial applications, healthcare to transportation, IoT is everywhere. Due to their widespread usage, attackers deploy sophisticated methods and software to infiltrate IoT networks. IoT devices' low processing power and resources can threaten security and privacy. This vulnerability has inspired attackers to undertake a variety of attacks against IoT networks. Wormhole attacks (WHA) are routing attacks which can interrupt IoT network packet routing and flow. To address the security issues related to IoT devices, a novel hybrid deep Learning (DL) method is proposed to classify potential security risks in IoT circumstances. The proposed module combines the Bidirectional Long Short-Term Memory with attention mechanism (ABS-BiLSTM) and AlexNet CNN to achieve higher detection accuracy. The security service provided by the proposed monitoring solution ensures compatibility among various IoT network communication protocols. The results clearly show that the effectiveness and excellence of the schemes in terms of network performance, accuracy, and detection speed enhancement. © 2025 IEEE.

关键词： Network intrusion

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Driver Facial Emotions and Drowsiness Detection for Enhanced Advanced Driver Assistance Systems(ADAS): Safety Management

Driver Facial Emotions and Drowsiness Detection for Enhanced...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Bharadwaj, Sippee Chotai, Nila Bharadwaj, Chinmoy Islam, Atowar Ul Devi, Mampi International School of Business and Research Dept. of Business Analytics Bangalore India International School of Business and Research Dept. of MBA and Executive Education Bangalore India University of Science and Technology Dept. of Computer Science Meghalaya India National Forensic Sciences University Tripura Campus Dept. of Cyber Security and Digital Forensic India

ISBN: (纸本)9798350369175

The proposed approach seeks to recognize real-time emotions from human facial expressions. Facial emotions are the most essential visual indicators for identifying a driver's emotional state and weariness. The framework work is developed to allow the computer to automatically extract the facial identification of human emotions using a deep face library and the amount of fatigue of eyes calculating eye-aspect-ratio (EAR) with Open CV2. From the results of our study, the model shows that if eyelids remain closed for longer than projected, this is a sign of weariness or drowsiness. A warning signal is provided to the driver if the system finds symptoms of sleepiness straight ahead concerning the driver's safety on the road. The project proposes to construct a non-intrusive driver warning system as part of Advanced Driver Assistance Systems (ADAS) to increase the safety of all road users while driving. © 2024 IEEE.

关键词： Advanced driver assistance systems

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ClinicSum: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations

ClinicSum: Utilizing Language Models for Generating Clinical...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Neupane, Subash Tripathi, Himanshu Mitra, Shaswata Bozorgzad, Sean Mittal, Sudip Rahimi, Shahram Amirlatifi, Amin Dept. of Computer Science and Engineering Mississippi State University Potentia Analytics Inc. Dave C. Swalm School of Chemical Engineering Mississippi State University

ISBN: (纸本)9798350362480

This paper presents CLINICSUM, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a retrieval-based filtering module that extracts Subjective, Objective, Assessment, and Plan (SOAP) information from conversation transcripts, and an inference module powered by fine-tuned Pre-trained Language Models (PLMs), which leverage the extracted SOAP data to generate abstracted clinical summaries. To fine-tune the PLM, we created a training dataset of consisting 1,473 conversations-summaries pair by consolidating two publicly available datasets, FigShare and MTS-Dialog, with ground truth summaries validated by Subject Matter Experts (SMEs). CLINICSUM's effectiveness is evaluated through both automatic metrics (e.g., ROUGE, BERTScore) and expert human assessments. Results show that CLINICSUM outperforms state-of-the-art PLMs, demonstrating superior precision, recall, and F-1 scores in automatic evaluations and receiving high preference from SMEs in human assessment, making it a robust solution for automated clinical summarization. © 2024 IEEE.

关键词： Modeling languages

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