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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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visual analytics and data visualization (icvadv), international conference on

作者： Venkateswaran Radhakrishnan Vijayakumari Rodda S. Sowmiya Abdur Rahman Sarker R. Palanikumar R. Asokan Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Dhofar Sultanate of Oman Department of Computer Science Krishna University Rudravaram Machilipatnam Sri Eshwar College of Engineering COIMBATORE Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of ECE Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security.

关键词： Deep learning Ciphers Accuracy Computational modeling visual analytics Neural networks Transformers Encryption Pattern recognition Convolutional neural networks

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Preprocessing and visualisation of dataset for Neonatal Sepsis Prediction Using MIMIC-IV

Preprocessing and Visualisation of Dataset for Neonatal Seps...

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visual analytics and data visualization (icvadv), international conference on

作者： Madhura Devendra Ranade Dept. of Electronics Engineering SVKM's Shri Bhagubhai Mafatlal Polytechnic and College of Engineering Mumbai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This paper aims at exploring MIMIC-IV dataset for extraction for neonatal patients diagnosed with “Septicemia [sepsis] of newborn”. The term neonate is used for newborn babies less than four weeks old. Medical Information Mart for Intensive Care version 4 (MIMIC-IV) is a large, open access electronic health record database having more than 3 lakhs patients' data. The main objective of this paper is to access and filter this database using Google Big query. The Neonatal Sepsis is one of the reasons affecting neonatal mortality. Neonatal mortality reduction is one of the goal for sustainable development as mentioned by World Health Organization. This paper serves as the first step in identifying trends from the neonatal records from MIMIC dataset thereby paving away for creation of Sepsis prediction dataset and feature extraction. This paper deals with assessing the combined effect of various modules available in MIMIC database such as patient's demographic information, admission and diagnosis related details. It was found that there were total 96564 entries with sepsis diagnosis out of which 42.3 % were females and 57.7% were male neonates. The highest number of neonates diagnosed with Sepsis were of “White” ethnicity. The highest number of neonates were prescribed to perform “Blood Culture Neonate” test. The majority of neonates were found to have micro-organism named “Staph Aureus Coag+” in their body. This paper successfully concludes that the exploratory analysis of neonates with sepsis would be a foundation step in developing the neonatal sepsis prediction model using machine learning techniques.

关键词： Pediatrics Microorganisms databases MIMICs data visualization Mortality Predictive models Sepsis Market research Internet

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Real Time Sentiment Analysis and Categorisation of News Articles Using VADER and SVM

Real Time Sentiment Analysis and Categorisation of News Arti...

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visual analytics and data visualization (icvadv), international conference on

作者： Indhumathi S F. Mary Harin Fernandez Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This study introduces a novel framework for real-time sentiment analysis (SA) and categorization of news articles using a combination of VADER (Valence Aware Dictionary and sEntiment Reasoner) and Support Vector Machines (SVM). In a time when information spreads quickly, understanding the underlying sentiments in news content is crucial, particularly when the information spans diverse domains such as education, finance, and health. The proposed methodology connects real-time data acquired from APIs, ensuring that the analysis reflects current trends and public opinion dynamics. The first phase of the framework involves data collection from APIs specifically personalized to gather news articles across the three targeted domains. Once the data is obtained, a preprocessing pipeline cleans and normalizes the text, handling issues such as punctuation, stop words, and tokenization to ensure that the subsequent analysis is accurate and efficient. VADER is then applied to each article to perform sentiment scoring. VADER's rule-based approach, specifically tuned for social media and contemporary language, enables it to quickly assess the polarity of the text, identifying positive, negative, or neutral sentiments with high precision. After sentiment extraction, the framework employs the SVM algorithm to classify articles into one of the three categories: education, finance, or health. This classification utilizes a feature set that integrates both the sentiment scores from VADER and domain-specific textual features extracted from the articles. A comprehensive training process on a curated dataset, free from dependence on any preexisting published database, ensures that the SVM classifier is robust and tailored to the unique characteristics of the input data. Experimental results demonstrate that the integration of VADER and SVM facilitates a reliable and scalable solution for real-time sentiment analysis (SA) and domain categorization. The results underline the potential of t

关键词： Support vector machines Sentiment analysis Accuracy visual analytics Finance data collection Market research Feature extraction Real-time systems Monitoring

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Automated Diabetic Foot Ulcer Detection Using Hybrid Short Term Memory Architecture

Automated Diabetic Foot Ulcer Detection Using Hybrid Short T...

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visual analytics and data visualization (icvadv), international conference on

作者： S. Gandhimathi Alias Usha P.Uma Maheswari S. Dharshini B. Hemaroopa S. Nikila M. Angela Department of Electronics and communication engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Diagnosis of diabetic foot ulcer in early stage is vital to prevent complications viz., amputations and infections. Conventional image processing methods lack to provide automatic and accurate detection of Diabetic Foot Ulcers (DFUs) in earlier stages. Deep learning based method serves as a better solution for diabetic foot ulcer detection by deeply analyzing complex features. This paper proposes a novel model that integrates the strengths of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) for comprehensive analysis and detection of DFUs in the NIH dataset. The combination of the hybrid approach enables the extraction of both spatial and temporal features from DFU images, improving the model's accuracy by learning appropriate patterns for precise diagnosis. The extracted features are collected and given as input to Recurrent Neural Networks (RNNs) to capture temporal dependencies and sequential patterns across successive image frames. Experimental analysis reveals that the proposed method achieves an accuracy of 94.4%, F1 Score of 94.76%, a precision of 96.61 % and a specificity of 93.47%. From the analysis, it is depicted that the hybrid deep learning approach reduces the burden of health care systems that rely on manual feature extraction and it also efficiently learned the subtle features to provide better results.

关键词： Deep learning Accuracy Recurrent neural networks visual analytics Feature extraction Real-time systems Diabetes Convolutional neural networks Long short term memory Medical diagnostic imaging

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HSLNF: Hybrid Socio-Legal Neural Framework for data Analysis

HSLNF: Hybrid Socio-Legal Neural Framework for Data Analysis

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visual analytics and data visualization (icvadv), international conference on

作者： R. Arun Kumar S.K. Kamalakhannan Asha Sundaram Saveetha School of Law Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu Saveetha School of Law Saveetha Institute of Medical and Technical Sciences Chennai 77 SIMATS Saveetha School of Law Chennai 77

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The enactment of the Transgender Persons Act marked a pivotal step in addressing the socio-economic challenges faced by transgender individuals in the labor market. This study, titled “HSLNF: Impact Analysis of Labour Laws on Transgender Persons Post-Enactment of the Transgender Persons Act Using a Hybrid Socio-Legal Neural Framework,” evaluates the socio-economic and legal impact of the Transgender Persons Act on employment trends, wage equality, and workplace discrimination. A Hybrid Socio-Legal Neural Framework (HSLNF) was designed, integrating socio-economic variables such as employment rates and wage gaps with legal metrics like compliance rates and resolved cases. Primary data was collected through surveys, interviews, and focus groups with transgender individuals, while secondary data included government reports and legal documents. Sentiment analysis and text analysis of legal documents were conducted using natural language processing (NLP). The neural model achieved an accuracy of 94.5% in predicting trends and assessing labor law impacts. Results indicate a 12.7% increase in employment rates and a 25.4% decrease in severe workplace discrimination. Wage disparities, however, remain significant, with the gap reducing from $360 in 2015 to $320 in 2024. Legal awareness improved significantly, with compliance rates rising to 70% in 2024. These findings underscore the Act's effectiveness while highlighting the need for further reforms to achieve full inclusivity and equity. The study concludes with recommendations for policy development and advocacy for transgender rights.

关键词： Surveys Sentiment analysis Text analysis Accuracy Law visual analytics Employment Transgender issues Market research Socioeconomics

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Utilizing Attention Based Machine Learning Models for Improved Demand Forecasting in Supply Chains

Utilizing Attention Based Machine Learning Models for Improv...

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visual analytics and data visualization (icvadv), international conference on

作者： M.Ramya Krishna Mohammad Alifa Firdhos Farheen D.Leela Kalyani Basic Science Department Vishnu Institute of Technology Bhimavaram India Department of CSBS Vishnu Institute of Technology Bhimavaram India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The goal of the suggested approach is to offer a reliable way to increase supply chains' accuracy in demand forecasting, which will enhance inventory control and operational efficiency. The use of gradient boosting machines (GBM) with attention weights for supply chain demand forecasting is investigated in this study. The attention mechanism improves its capacity to concentrate on the most pertinent data points, such as seasonal trends, promotions, and historical sales patterns. GBM, an ensemble learning technique, is selected for its excellent performance and interpretability. In addition, the incorporation of attention mechanisms with GBM illustrates how the state-of-the-art ML techniques can be adapted to specific domain problems to create innovation in predictive analytics. The study makes use of Kaggle's “Forecasts for Product Demand” dataset, which contains historical sales information for a variety of products from several shops. In order to prioritize important aspects in forecasting future demand while taking non-linear relationships and temporal dependencies into consideration, the model incorporates attention weights. In this framework, attention-based GBM focuses on high-impact features like seasonality and promotions, enhancing demand prediction. Performance measurements like Mean Absolute Error (MAE) of 0.18 and a Root Mean Squared Error (RMSE) of 0.2 with a registered R-squared value of 0.9 in the experiment implemented in Python. The Attention-based GBM outperforms Random Forest and SVM, achieving superior accuracy and minimal prediction error. This study also opens the door for future applications in other commercial sectors by demonstrating the promise of machine learning approaches in tackling practical issues in supply chain demand forecasting.

关键词： Support vector machines Technological innovation Attention mechanisms Accuracy Supply chain management visual analytics Supply chains Demand forecasting Predictive models Boosting

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Telemedicine Platforms with Cloud-Based AI-Driven Diagnostics in Hospitals

Telemedicine Platforms with Cloud-Based AI-Driven Diagnostic...

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visual analytics and data visualization (icvadv), international conference on

作者： S. Thangalakshmi N. Mohankumar L.N. Jayanthi Y M Blessy R. Meenakshi M. Rajmohan Faculty – Electrical School of Marine Engineering and Technology Indian Maritime University Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Hospitals may benefit greatly from telemedicine technologies, including cloud-based artificial intelligence (AI)-driven diagnostics. Regardless of location, instantaneous access to medical knowledge is made possible by these systems that allow for virtual consultations between patients and healthcare practitioners. Critical patient data is always available during consultations due to these systems' use of cloud computing to safely store and handle massive volumes of data. The use of AI-powered diagnostics improves the precision and effectiveness of diagnoses. To help healthcare providers make informed decisions, machine learning algorithms examine patient data, including medical imaging, test findings, and clinical notes. Faster and more accurate diagnoses are possible with the help of this technology since it can spot patterns and abnormalities that human practitioners may miss. Hospitals can streamline their workflow using telemedicine technologies that use AI for diagnoses. They improve the efficiency of patient triage, the prioritization of critical situations, and the allocation of resources. Better healthcare delivery, shorter wait times, and improved patient outcomes are the effects of this.

关键词： Cloud computing Accuracy Hospitals Telemedicine visual analytics Streaming media Sensitivity and specificity Resource management Artificial intelligence Medical diagnostic imaging

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Non-Intrusive Load Monitoring Techniques for Intelligent Energy Management: A Comparative Study of FHMM and LSTM Approach

Non-Intrusive Load Monitoring Techniques for Intelligent Ene...

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visual analytics and data visualization (icvadv), international conference on

作者： N. Rajkumar Anitha Govindaram R. Geetha Subhamathi A S F Nithya S. Jose Anand A. Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamilnadu Department of EEE Karpagam Academy of Higher Education Eachanari Coimbatore Tamilnadu Department of IT Veltech High-tech Dr Rangarajan Dr.Sakunthala Engineering College Avadi Tamil Nadu Department of ECE KCG College of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This study addresses the problem of detecting and segregating electrical loads using two alternative data mining algorithms for Non-Intrusive Load Monitoring (NILM). The initial method makes use of a Factorial Hidden Markov Model (FHMM), which uses hidden state observations to forecast each device's usage. The second method uses a Long Short-Term Memory (LSTM) network and time-windowed data to measure each device's consumption over a given period. Experimental results, measured using the Root Mean Square Error (RMSE) metric, reveal that both techniques successfully address the disaggregation challenge, with low error rates and exact forecasting of individual energy use. The study emphasises the importance of input sequences obtained from mixed signals on model predictions and comparison results. Furthermore, using publically available tools and datasets improves data pretreatment efficiency and facilitates the use of NILM approaches. This study improves NILM methodologies by providing insights into effective modelling strategies and emphasising the importance of readily available resources in the energy disaggregation field. Its purpose is to encourage more research and improvement in this field.

关键词： Performance evaluation Energy consumption Load monitoring visual analytics Hidden Markov models Energy measurement Predictive models data mining Root mean square Long short term memory

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A Method and a Library for visual data Schemas

A Method and a Library for Visual Data Schemas

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21st international conference on The Semantic Web (ESWC)

作者： Lace, Lelde Romane, Aiga Fedotova, Jana Grasmanis, Mikus Cerans, Karlis Univ Latvia Inst Math & Comp Sci Riga Latvia

ISBN: (纸本)9783031789519;9783031789526

We describe a method for computing and visualizing data schemas for existing Linked data endpoints, combined with creation of visual query environments over these endpoints. We evaluate the method on a set of externally available small-to-medium size data sets and present the obtained visualization results in a form of interactive library.

关键词： Linked data Knowledge graph schema visual schema diagram

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Zero-Shot Speech-to-Speech Summarization using Unsupervised Learning and Generative Models

Zero-Shot Speech-to-Speech Summarization using Unsupervised ...

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visual analytics and data visualization (icvadv), international conference on

作者： S.Dinesh Dhanabalan S. Praveen Kumar R. Ragupathy Computer Science and Engineering Annamalai University TamilNadu India Department of Computer Science and Engineering E.G.S Pillay Engineering College Nagapattinam TamilNadu India Department of Computer Science and Engineering Annamalai University TamilNadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Parallel dataset which needs large number of aligned sources and target language pairings have traditionally been used to develop speech to speech translation system. This study presents a new method for Zero short speech to Speech translation (ZSSST) that relies on generative methods and other unsupervised learning methods. The main concept is to train the methods that can directly translate spoken language from one language to another without using parallel corpora. This will be achieved by utilizing big, unlabeled multilingual voice datasets. The suggested method learns to translate spoken input from one language into another by using unsupervised learning techniques like generative adversarial network (GAN) and Variation autoencoder. To enable speech to speech translation without direct supervision, this method is trained to produced speech in target language. This technology outperforms the limitations of conventional approaches and may operate with numerous languages concurrently, allowing for cross lingual transmission. Because it doesn't necessitate domain specific data or parallel voice corpora, this method ensures scalability for languages with limited resources. international diplomacy, cross border trade and real time personal communication are just a few examples of the many various applications that could benefit from the proposed method's improved accessibility, adaptability and efficiency in multilingual communication. Experimental results demonstrate the efficiency of this method on multilingual datasets and the metrics of existing methods with proposed methods are compared. By fixing the scalability problem and opening the path for more generic system that can easily handle several languages without expensive and labor-intensive parallel datasets, this method has major consequences for the future of real time voice translation.

关键词： Adaptation models Translation Scalability visual analytics Autoencoders Rate distortion theory Generative adversarial networks Real-time systems Multilingual Unsupervised learning

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