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

作者： Reddy, Amireddy Shashank Achar, Dishan Narsimhachari Mol, Madithilveedu Sheethal Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Securing digital audio data during any transmission or storage is essential for ensuring privacy and confidentiality. Traditional encryption techniques like Advanced Encryption Standard (AES) offer high security, but they might be vulnerable to attacks that exploit encrypted data patterns. Cellular automata, on the other hand, exhibit complex and challenging behavior, making them attractive for introducing additional layers of security in encryption schemes. However, integrating these techniques effectively remains a challenge. This paper addresses this gap by proposing a novel audio encryption system that combines the features of AES and cellular automata. A pseudo-random number sequence produced by Rule 30, a chaotic cellular automaton rule, is XOR-ed with the AES-encrypted audio data. This disrupts potential pattern recognition attempts, significantly enhancing the overall security of the encryption process. The system operates on WAV format audio files and ensures the confidentiality and integrity of the data by preventing unauthorized access and manipulation. We evaluate the effectiveness of our approach by comparing the characteristics of original and encrypted audio files. The results demonstrate significant alterations in the audio data, rendering it unintelligible without the decryption key. Additionally, the system utilizes the well-established AES algorithm, maintaining efficiency during encryption and decryption. This system defeats other methods in terms of security, complexity, and computational efficiency making this a reliable and efficient method for securing audio data transmission and storage where audio privacy is essential. © 2024 IEEE.

关键词： Advanced Encryption Standard (AES) Audio Encryption Cellular Automata Cryptography Digital Security Rule 30

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Deep Learning-based Cavity Detection in Diverse Intraoral Images: A Web-based Tool for Accessible Dental Care 5

Deep Learning-based Cavity Detection in Diverse Intraoral Im...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Siji Rani, S. Garine, Srija Janardhana, Papolu Hema Reddy, Lakkireddy Lakshmi Prabhanjan Kumar, Penubothu Jagadeesh Venkata Dwaz, Chapa Gagan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Dental cavities represent a widespread oral health issue on a global scale, impacting individuals across all age groups. The conventional approach to detecting cavities involves a visual examination by dentists, which is not only time-consuming but also subjective. Current methods for dental cavity detection heavily rely on subjective visual inspections, which may overlook early or concealed cavities. In this research paper, we present CatchCavity, a web application tool that utilizes deep learning for cavity detection in teeth. The diagnostic tool enables users to upload dental images, facilitating the assessment of dental cavity status. Furthermore, the web application functions as an online dental diagnostic service, providing the capability to securely store patients' dental records and information in a dedicated database. The system is trained on a dataset of annotated images and employs a convolutional neural network (CNN) architecture for accurate cavity detection. We evaluate the system's performance using metrics such as accuracy and loss. Our results showcase that the proposed system attains a high level of accuracy and efficiency in detecting dental cavities, achieving an overall accuracy of 98.7%. Additionally, our system surpasses traditional cavity detection methods, highlighting the possibility of deep learning approaches to enhance oral health outcomes. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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A Novel Residual-Concatenate Feature Fusion Strategy for Building Segmentation from Remote Sensing Images

A Novel Residual-Concatenate Feature Fusion Strategy for Bui...

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2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2024

作者： Byju, Akshara Preethy Vijayan, Lakshmi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350358582

This paper presents a novel encoder-decoder architecture based on U-Net integrated with a residual-concatenate multi-scale feature fusion (RCMFF) strategy to perform efficient building segmentation from remote sensing images. The proposed method considers an end-to-end deep learning framework based on UNet that consists of mainly three modules: i) an encoder with convolutional block attention module (CBAM) employed in their lateral connections;ii) the proposed residual-concatenate multi-scale feature fusion (RCMFF) module;and finally, iii) a decoder with refinement layers. The proposed RCMFF module integrates semantically relevant discriminative features inherent within the last encoder layer with reduced number of parameters thereby simultaneously improving the segmentation accuracy as well as the computational speed. The effectiveness of the proposed module is assessed using two benchmark archives: Massachusetts (MAS) and WHU building datasets. The proposed model demonstrates an overall improvement of 2.30% and 7.01% compared to the baseline models DeepLabV3+ and UNet++, respectively. © 2024 IEEE.

关键词： Convolution

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Smart Annotation And Anonymizer 15

Smart Annotation And Anonymizer

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

作者： Reddy, Durga Divya Vishwas, H.N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of computer science and Engineering Bangalore India

ISBN: (纸本)9798350370249

This paper introduces the Smart Annotation and Anonymization tool, a sophisticated software application developed using PyQT5, aimed at optimizing object detection and recognition processes. The tool seamlessly integrates manual and automated annotation, anonymization, and segmentation techniques for both images and videos. By harnessing advanced deep learning algorithms, the Smart Annotation feature enables users to label images and videos manually or automatically, streamlining the creation of precise datasets for object detection and recognition tasks. The paper emphasizes the tool's prowess in automatically recognizing and tagging objects in images and videos, presenting a significant time-saving advantage. The Anonymizer feature addresses privacy concerns by automatically blurring or obscuring faces and other identifying features in images and videos. This functionality is crucial for upholding the privacy of individuals captured by sensors or cameras, especially in applications such as Automatic Driver Assistance Systems (ADAS). Furthermore, the paper underscores the inclusion of Instance and Panoptic Segmentation, dividing pixels in images into regions or contours and assigning a class to each, thereby enhancing object detection and recognition. © 2024 IEEE.

关键词： Anonymity

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Enhanced CNN Architecture with Comprehensive Performance Metrics for Emotion Recognition 1

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13th International Conference on Information Systems and Advanced Technologies, ICISAT 2023

作者： Ravichandran, M. Bharathi, P. Praveenna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9783031605949

ISBN: (纸本)9783031605932

Engaging gameplay in video games provides a wide array of advantages, encompassing entertainment and personal growth. Immersive gaming experiences often stem from games that adapt to the player’s emotional state. In the proposed study, participants engage in games designed to elicit emotions like excitement, normalcy, and boredom. Their facial expressions and heart rate (HR) signals are monitored during gameplay. Facial expressions serve as ground truth indicators of the players’ emotions. These emotions, in tandem with the corresponding HR signals, are then processed to derive normalized features. These normalized features and identified emotions serve as input and output, respectively, for a Convolutional Neural Network (CNN) in a machine learning context. The trained CNN model subsequently enables the classification of participants’ emotions during gameplay. The experimental findings will not only identify the emotions but also gauge their intensity based on HR values, facilitating dynamic adjustments to the gameplay experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Emotion Recognition

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Efficient Text Formatting: A Comparative Analysis of Algorithmic Approaches 1

Efficient Text Formatting: A Comparative Analysis of Algorit...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Nandini, Bandaru Jaya Sahithi, Mamidi Leha Harshika, Siddareddy Gari Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518943

Efficient text formatting, especially when printing with mono spaced fonts, is a very important factor. In an attempt to address the problem of printing clean paragraphs, this work has been presented in the form of implementing and trying to demonstrate the efficiency of a large variety of algorithms. The algorithms include Binary Search, Shortest Path, Greedy Approach, Branch and Bound, Divide and Conquer, and Brute Force. This problem tries to format a set of texts into lines of fixed given width, where at a time it looks appropriate and readable, trying to minimize the number of vacant places and blank spaces. Hence, this dissertation dwells on a text formatting technique that possesses the least number of vacant places and blank spaces yet manages to look nice. In general, a comparison of the methods in the number of vacant places on a line raised to the cube, as a cost metric and the runtime complexity. Performance testing on a very broad spectrum of test scenarios is also there, which is low word count, random content, fixed character length, and fixed word count. The implementation of this problem will help us understand what method is effective and where so that it can be used in a real-world scenario. Text formatting jobs in domains like publishing, academia, and web development are crucial processes for efficient communication. Among these, the Dynamic Programming and Divide and Conquer algorithms achieved the highest performance, with a minimal total cost of 2529 and optimal line breaks in fixed character-length paragraphs. The results demonstrate that these algorithms strike an ideal balance between runtime efficiency and formatting quality, making them well-suited for real-world applications. © 2024 IEEE.

关键词： Dynamic programming

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Improving Obfuscated Malware Classification: A Machine Learning Approach with Feature Selection and Data Balancing 11

Improving Obfuscated Malware Classification: A Machine Learn...

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11th IEEE International Conference on Advances in computing and Communications, ICACC 2024

作者： Bhavana, Gundu Pudota, Mokshagna Bhuvan Chowdary Saravanan, S. Deepak, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379136

Obfuscated malware refers to malicious software that has been deliberately modified to hide its malicious purpose and avoid detection by conventional security mechanisms. Classifying obfuscated malware poses a significant challenge due to its deceptive nature and the dynamic techniques used by attackers to obfuscate code. Existing approaches in malware analysis often struggle to accurately classify obfuscated malware, leading to potential security vulnerabilities and data breaches. In this research, we investigate the application of machine learning algorithms for malware analysis, specifically focusing on multi-class classification of obfuscated malware using the CIC-MalMem-2022, a latest dataset. We apply data preprocessing techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and normalization, to enhance the dataset's balance and consistency. Subsequently, we train and evaluate various machine learning algorithms, including Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), LightGBM, and Decision Trees (DT), on different datasets comprising SMOTE, normalized, and SMOTE+normalized instances. Our study also explores the effect of feature selection process on the performance of the model. We identify the top 35 features for optimal accuracy, addressing the dimensionality challenge inherent in large-scale datasets. Our findings reveal that the Random Forest (RF) model achieves the highest accuracy of 96.23% on the dataset that is balanced by SMOTE with top 35 features. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating Deep Learning with the Gemini API for Improved Pest Management 3

Integrating Deep Learning with the Gemini API for Improved P...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Manoj, Milan Ihsan, Faraz Devadath, G.K. Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798331532420

This study uses the Gemini API and deep learning algorithms to provide a novel approach to agricultural pest management. Pests pose serious difficulties to agriculture, thus prompt and precise pest management is crucial. The method combines the ability of the Gemini API to generate pertinent information on pesticides, home remedies, and nearby retail locations with deep learning models for pest classification. By using user-uploaded photos to identify pests, the deep learning model is taught to make recommendations for pest management tactics. The Gemini API also provides location-based information for nearby retailers carrying suggested products, as well as comprehensive details on safe pesticides and DIY cures. The strategy provides a comprehensive solution for agricultural pest management through integration, giving farmers practical insights for efficient pest control and increased crop output. © 2024 IEEE.

关键词： Agricultural Pest Management Agricultural Sustainability Deep Learning Gemini API Home Remedies Location-Based Services Pest Classification Pesticide Information

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ACDD: Automated COVID Detection using Deep Neural Networks

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IEIE Transactions on Smart Processing and computing 2023年第6期12卷 518-525页

作者： Raza, Ghulam Musa Shoaib, Muhammad Kim, Byung-Seo Department of Software and Communications Engineering Hongik University Sejong Korea Republic of School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic that became known as COVID-19. To suppress the pandemic, early detection of positive COVID-19 patients has become highly important. There is a lack of precise automated tool kits available for use in diagnosing medical conditions, so auxiliary diagnostic tools are in high demand. Important information about this virus can be extracted from X-ray images, which can be used in conjunction with advanced artificial intelligence. This study addresses the unavailability of physicians in remote areas, and the complex algorithm proposed in this study can find potential matches for patients in rural areas who need care. This could help to improve access to care for those who need it most. The purpose of this study is to develop a novel model that can automatically detect COVID-19 by utilizing chest X-ray images. The proposed model incorporates binary and multi-class classification and can be employed by radiologists for timely detection of the COVID-19 virus in an effective manner. © 2023 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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