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Automated Regional Language News Article Classification System for Kannada and Telugu

Automated Regional Language News Article Classification Syst...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Deepa Gupta K Thejaa Aarathi Rajagopalan Nair Panduranga Katti Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The increasing amount of digital news content has created a pressing need for automated systems that can efficiently organize and categorize articles. This study focuses on developing an automated classification system for Kannada and Telugu news articles, targeting predefined categories. For Kannada, the categories include Entertainment, Sports, and Lifestyle, while for Telugu, the categories are Sports, Business, and Entertainment. With the help of the techniques available in natural language processing and the available models in machine learning, the system preprocesses the textual data in both languages to extract meaningful features for classification. Several classification algorithms are explored, with a focus on evaluating model performance using standard metrics. The results demonstrate that embeddings like KannadaSBERT and TeluguSBERT significantly improve classification accuracy, showing a clear advantage in understanding and categorizing regional language text. This work addresses a critical gap in the field of NLP for regional Indian languages by providing a structured approach to classifying Kannada and Telugu news articles. It contributes to enhancing the organization and accessibility of information for Kannada - and Telugu-speaking communities, thereby advancing research in low- resource language processing.

关键词： Measurement Deep learning Standards organizations Pressing Organizations Feature extraction Natural language processing data models data mining Sports

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Advanced Encryption Techniques for Protecting Sensitive data in Multi Cloud Architecture: A Security First Approach

Advanced Encryption Techniques for Protecting Sensitive Data...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： V. Anand N. Saravanan A. Mahendiran School of Computing SASTRA Deemed University Thanjavur Tamil Nadu India Srinivasa Ramanujam Centre SASTRA Deemed University kumbakonam Tamil Nadu India Thanthai Hans Roever College Perambalur Tamil Nadu India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

“Cloud computing” refers to a broad range of computer ideas that include several PCs linked by a real-time transmission network, such the internet. Cloud computing is a new paradigm that has drawn a lot of attention from researchers recently due to its potential to lower computing *** study compares RSA, AES, and a technique that combines both of them. The effectiveness of these methods is examined in relation to encryption and decryption times for various data set sizes. The experiment was designed to be replicable and was conducted on standardised devices using the Mathematical writing and analysis tool, which is based on the Wolfram Language. In this experiment, encryption and decryption speeds were tested and compared for data sizes ranging from 64 to 176 bytes. As a second indicator of the algorithms' performance, the number of iterations each algorithm completed was also *** results demonstrate that AES had the fastest encryption and decryption times, and that the hybrid model had the greatest features of both RSA and AES in terms of speed and security. With encryption times of 0.10 ms and 0.20 ms for 64-byte and 176-byte data, respectively, the hybrid approach provided a balanced performance. AES outperformed RSA in throughput by up to 4 times and the hybrid technique by up to 2 times. According to these results, the hybrid technique successfully strikes a balance between speed and security, even though AES is best for speed.

关键词： Performance evaluation Cloud computing Operating systems Writing Throughput Real-time systems Mathematics computing Hardware Encryption Reliability

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CNN LIPNET : Automated Lip Reading Using Deep Convolutional Neural Networks

CNN LIPNET : Automated Lip Reading Using Deep Convolutional ...

引用

advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： VPV Datta Ganesh O Sai Likith Kumar P. Jithendra S. Satheesh Kumar Computer Science and Information Technology Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Lipreading, or the process of interpreting the speech by analyzing lip movements, is a critical solution when auditory cues are limited or even unavailable. This project introduces the LipNet, a vision-based neural network that uses CNNs and LSTM networks to decode lip movements into text. The model is trained on the Grid Corpus dataset, with data split into 80% for training and 20% for testing. Keyframes extracted from video sequences are processed to capture both spatial features using CNNs and temporal patterns with LSTMs. A SoftMax layer outputs accurate textual representations of speech. Achieving a notable 97.44% average accuracy, LipNet demon- strates robust performance across different speakers, accents, and speaking styles. It has broader implications on human- computer interaction, especially in assistive technologies for the hearing impaired, environments with high noise interference, and silent communication, among others. The model improves focus on crucial visual cues through the integration of attention mechanisms.

关键词： Training Adaptation models Accuracy Lips Computational modeling Training data Feature extraction data models Convolutional neural networks Testing

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A Novel Approach To Brain Tumor Detection using AI ML, DL, with Synthetic data Augmentation

A Novel Approach To Brain Tumor Detection using AI ML, DL, w...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Narenthirakumar Appavu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Effective treatment and better patient outcomes depend on early brain tumor identification. Even if they are trustworthy, traditional diagnostic techniques frequently need manual analysis, which can be laborious and arbitrary. In order to improve brain tumor diagnosis from MRI scans, this work introduces a unique method that combines Support Vector Machine (SVM) clustering, Statistical Gray Level Co-occurrence Matrix (LBP) features, ResNet50, and synthetic data augmentation. SVM clustering overcomes the drawbacks of linear clustering techniques by accurately segmenting tumor areas. Texture-based characteristics like homogeneity and contrast are extracted using LBP, offering vital information for categorization. By utilizing its deep learning architecture, the ResNet50 model attains great accuracy after being adjusted based on the retrieved characteristics. By mitigating data scarcity, synthetic data augmentation ensures the resilience of the model. Improved accuracy, sensitivity, and specificity are demonstrated by experimental findings on publicly available datasets, indicating the efficacy of this strategy in providing scalable, dependable solutions for automated brain tumor diagnosis.

关键词： Support vector machines Visualization Accuracy Brain tumors Feature extraction Brain modeling data models data mining Synthetic data Residual neural networks

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AI-Driven Test Automation for Big data Systems in Cloud Environments

AI-Driven Test Automation for Big Data Systems in Cloud Envi...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Ankit Kumar Gupta Sreeprasad Govindankutty Avinash Reddy Nandyala Justin Rajakumar Maria Thason Sanjeev Pippal Gopal Kumar Gupta Uttar Pradesh Technical University Lucknow Uttar Pradesh India Rochester Institute of Technology Rochester NY United States Texas State University San Marcos Texas USA Maniapl University Gangtok Sikkim India Department of Computer Science and Engineering (AI) G.L. Bajaj Institute of Technology and Management Greater Noida India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

AI-based Test Automation for Cloud-Based Big Advanced data Systems this method used AI and other new data science methods such as big data, cloud computing, etc. Current testing methodologies do not suit the environments of modern big data systems in a cloud-based environment. AI based test automation approaches: Automated test scenario preparation, examination of test events to identify defects and generation of testing issues. Big data systems, on the other hand, can periodically scan for maximum performance with an all-AI approach, carry out A/B testing, and fine-tune performance. This is particularly important for cloud environments when the architecture of the system and data processing continually changes at a high rate. Additionally, AI in test automation is capable of detecting anomalies and predicting possible system breakdown, providing early warning of issues to ensure mitigation and thereby avoiding costly downtime. This way various scenarios and data sets can be tested and covered to ensure that the best results are covered.

关键词： Cloud computing Automation Prevention and mitigation Manuals Computer architecture Big data Real-time systems Complexity theory Artificial intelligence Testing

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advances in Crop Monitoring technologies: A Comprehensive Review

Advances in Crop Monitoring Technologies: A Comprehensive Re...

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Pervasive Computational technologies (ICPCT), international conference on

作者： Divya Rawat Shashank Awasthi Manas Kumar Mishra Ruby Faizan Uttaranchal School of Computing Science Uttaranchal University Dehradun India G.L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Agricultural optimization and food security rely on effective plant monitoring. This essay probes the evolution and comparative analysis of modern technologies that are revolutionizing agricultural monitoring. In this paper, we show how artificial intelligence (AI), machine learning algorithms, and drones combined with the power of IoT sensors (smart agriculture) along rural data collection using remote sensing technologies will help to improve precision farm management by understanding the pros and cons in implementing these AI-driven new tools into smart devices capable of supporting higher crop yields and higher efficiency. Through a review of case studies and market trends, this article delves into how these technologies provide farmers with real-time data, predictive analytics, and actionable insights. Moral, monetary, and ecological reverberations of bonding these advances universally are likewise discussed. Ultimately, this comparative study to develop resilient and sustainable food production systems through the inclusion of modern technologies is an important opportunity for many difficult socioeconomic and climatic issues.

关键词： Smart agriculture Ethics Machine learning algorithms Reviews Tracking Artificial intelligence Socioeconomics Smart devices Monitoring Drones

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Handling Non-stationarity with Distribution Shifts and data Dependency in Time Series Forecasting 25th

Handling Non-stationarity with Distribution Shifts and Data ...

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25th international conference on Parallel and Distributed computing, Applications and technologies, PDCAT 2024

作者： Hua, Qiang Wang, Xiao-Wen Zhang, Feng Dong, Chun-Ru College of Mathematics and Information Science Hebei University Baoding071002 China

ISBN: (纸本)9789819642069

Deep learning models have achieved substantial advancements in time series forecasting, leveraging their impressive capability to capture sequential dependencies. However, time series data often exhibits non-stationarity, where the data distribution undergoes rapid changes over time, posing significant challenges for accurate prediction. Some studies have proposed normalizing data within subsequences to eliminate non-stationarity and adopting a statistical forecasting module to model the evolution trend of the original time series’ statistical characteristics. Based on the predicted future statistical characteristics, denormalization is performed to restore stationarity, achieving accurate predictions of future distributions. This statistical forecasting approach has limitations in handling outliers in statistical features, resulting in insufficient model robustness. To address this issue, the Slice Adaptive Normalization module with Huber loss (H-SAN) is proposed, which effectively enhances the model’s robustness against outliers and improves prediction performance. Additionally, while this method employs local normalization, it utilizes long-sequence modeling for feature extraction, neglecting the inter-slice dependencies. Furthermore, this paper proposes a dual-token input strategy, which achieves simultaneous extraction and effective fusion of both global and local features based on the dual scales of input tokens. A network for non-stationary time series forecasting based on Distribution Shifts and data Dependency Network (DSDDNet) is constructed in this paper. Experimental results demonstrate that DSDDNet significantly outperforms existing benchmark models on standard datasets, validating its effectiveness and superiority. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Pelvic Uretero Junction Obstruction Detection Using Machine Learning Algorithm

Pelvic Uretero Junction Obstruction Detection Using Machine ...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Divya Mohan Vimanyu Jain Aryan Naresh Dayalani Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Uretero-Pelvic Junction (UPJ) obstruction, if undetected, can lead to severe kidney dysfunction, underscoring the need for early and reliable diagnostic solutions. This research aims to develop an effective, non-invasive diagnostic tool by integrating real-time ultrasound imaging with advanced machine learning techniques. Pre-processed ultrasound images are analyzed using MobileNetV2 for feature extraction, followed by classification through a Support Vector Machine (SVM). SVM was selected for its robustness with limited training data, its ability to operate effectively in high-dimensional spaces, and its interpretability—an essential factor in medical diagnostics. By combining the strengths of MobileNetV2 and SVM, the model ensures a balance between computational efficiency and diagnostic accuracy, addressing challenges like limited datasets and the complexity of medical imaging. The purpose is to enable precise detection of UPJ obstruction while minimizing the limitations of traditional diagnostic methods. The proposed model demonstrates significant accuracy, supported by evaluation techniques such as confusion matrices and t-SNE. This approach underscores the transformative potential of combining machine learning with medical imaging, offering a pathway to enhanced patient outcomes through timely and reliable intervention.

关键词： Support vector machines Ultrasonic imaging Accuracy Computational modeling Training data Machine learning Real-time systems Junctions Kidney Biomedical imaging

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Multi AI Agents based Black Box Audio Transcriber and Aviation Incident Report Generator

Multi AI Agents based Black Box Audio Transcriber and Aviati...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Ghanashyam Vagale Mohammed Naif Khan Rajat Goutam Devarakonda Nikhil Matur Rohith Kumar Tarun Narula Khoury College of Computer Sciences Northeastern University Boston USA Department of Computer Science Ramaiah University Bangalore India Centre for Artificial Intelligence National Institute of Technology Jalandhar India Department of Computer Science National Institute of Technology Jalandhar India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This paper introduces a framework that automates the transcription of black box audio data and the generation of aviation incident reports using AI agents. Traditionally, manual transcription of cockpit recordings is time-consuming and errorprone. The proposed system employs Groq-Whisper for audio transcription and various AI agents for report generation, streamlining the analysis of incident data. Each agent focuses on specific tasks, such as refining transcriptions and structuring conversations, leading to the identification of critical events and causal factors in the reports. This approach enhances accuracy and efficiency in aviation safety investigations, providing a reliable tool for real-time decision-making. The paper highlights the significance of AI integration to improve efficiency while ensuring explainability and trust in aviation contexts, ultimately contributing to enhanced air travel safety.

关键词： Electronic equipment Accuracy Flight recording Refining Manuals Real-time systems Safety Reliability Noise measurement Artificial intelligence

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Edge Based Smart Energy Management System Using Object Detection

Edge Based Smart Energy Management System Using Object Detec...

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international conference on advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Bennish, M. Saravanan, M. Department of Networking and Communications SRM Institute of Science and Technology Chennai Kattankulathur India

ISBN: (纸本)9789819795062

With rising demand for energy efficiency and sustainability, advanced technologies to optimize energy consumption in various domains are in high demand. This paper proposes an Edge-based Smart Energy Management System that intelligently monitors and controls energy usage in real-time using object detection techniques. This system employs a distributed architecture to efficiently manage energy consumption by combining the power of edge computing and computer vision. Smart computer vision devices are deployed at the edge to collect real-time data from the environment. These devices use YOLOv8 object detection algorithms to identify and track energy-intensive objects such as appliances like lights, and fans. The detected objects information is then processed and analyzed at the edge devices, which generate the corresponding threshold value for managing the electrical appliances. These processed data will be sent to the cloud infrastructure for further analysis and decision-making. Through the integration of object detection and YOLOv8 machine learning techniques, this system offers several key benefits. Firstly, it enables real-time monitoring of energy consumption at a granular level, allowing users to identify the number of persons and take appropriate actions to reduce electricity consumption. Secondly, the system provides accurate energy forecasting, enabling proactive load management and efficient allocation of energy resources. The proposed system will implement the computer vision in real-time monitoring and evaluation to assess its performance, scalability, and energy-saving potential. The evaluation results will demonstrate the proposed system’s effectiveness in optimizing energy usage, reducing costs, and contributing to a more sustainable environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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