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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Appasani, Deepthi Bokkisam, Charan Sai Surendran, Simi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Healthcare monitoring systems have improved with the Internet of Things and machine learning prediction models. Traditional batch machine-learning approaches cannot generate an effective model since most data will be continuous and real-time. Real-time medical data processing is challenging since the entire data is unavailable during prediction. Here, a continuous model adaptation based on incremental learning is demanded. The development of such a system can significantly improve patient outcomes and reduce healthcare costs. This paper proposes an Incremental Naive Bayes Learner that can handle concept drifts in data. The algorithm keeps a sliding window of data constantly updated as new data is added. The adaptive window size determines how much data is used to train the model at any given time. After processing each chunk of data, the algorithm calculates the model's accuracy. The system detects a concept drift and dynamically updates the training set if the accuracy drops below a predefined threshold. We conducted a comparative evaluation of state-of-the-art batch and incremental learning algorithms on different medical datasets, demonstrating the impact of incremental learning. The results demonstrate the effectiveness of our approach: the Agrawal dataset achieved the highest accuracy at 66.6%, followed by Dialysis at 61.6%, while Liver and HCC achieved 50% and 58.3% accuracy, respectively. This approach ensures a sustained high level of accuracy in healthcare monitoring systems over time, even amidst concept drift. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Design of a Secured and Trusted E-Voting System Using Blockchain Technology

Design of a Secured and Trusted E-Voting System Using Blockc...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Elegba Jeremiah, O. Agbelusi, O. Olutayo Victor, A. Akintola Kolawole, G. School of Computing The Federal University of Technology Department of Software Engineering Akure Nigeria School of Computing The Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9798350376838

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since - 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process. © 2024 IEEE.

关键词： Blockchain

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Aural Signatures: Audio Fingerprinting Techniques for Real-Time Audio Recognition 3

Aural Signatures: Audio Fingerprinting Techniques for Real-T...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sharan Nripesh Reddy, B. Sai Venkat, B. Manohar, I. Abhishek, S. Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350396157

Audio fingerprinting is an essential technique for copyright protection and music recognition. The capacity to accurately categorize audio content and spot possible copyright violations is vital for musicians, content creators, and streaming services. With its valuable solutions for copyright protection, content monitoring, and music recommendation, this study has significant implications for the music industry. In the research, the research focused on developing robust audio fingerprinting methods for precise audio content recognition. These tactics have shown to be remarkably accurate and quite effective. The approach is based on the use of strong hashing techniques and the extraction of various audio aspects. Long Short-Term Memory (LSTM) along with SVM, KNN, and CNN were employed in the research. Notably, LSTM outperformed other models, showcasing an outstanding accuracy rate of 93 %. The results hold particular promise for massive music platforms and instantaneous content matching. The proposed methods have demonstrated the effectiveness of these strategies in identifying audio material, even under challenging conditions. © 2024 IEEE.

关键词： Long short-term memory

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Suraksha: Advanced SOS Android App with Intelligent Spam Alert Management and Collaborative Decision-Making 15

Suraksha: Advanced SOS Android App with Intelligent Spam Ale...

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

作者： Padmavathi, A. Shashini, V. Ryan, J. Collin Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Safety is first and foremost in the current busy life, and smartphones give a helping hand in protecting. This paper has presented an Android app, Suraksha, which enhances personal safety during emergencies. This app ensures that assistance is sought immediately, whereas traditional ways require one to call a person or send the person a message or even unlock the phone, which may not happen when in distress. The nature of shrugs allows the app to differentiate a safe situation and a red alert. Through distress shrugs, the app sends the user's location and the user's defined message to the pre-registered ones. Suraksha app depends on humans to judge the alerts and all the information involved, which creates a bundle of evidence that will be sufficient. It is designed with a Machine Learning model, which studies the user's learned things and the emergency type to understand its effectiveness. © 2024 IEEE.

关键词： Android Convulated neural networking Gesture GPS Isolation Forest Machine learning Registered Contacts Transmission protocols

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Detect and Alleviate DDoS Attacks in Cloud Environment 1

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8th International Conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Kulwanth, Bandi Srinivasarengan, V. Anish, Peddinte Abirami, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789819976331

ISBN: (纸本)9789819976324

The Internet is now a crucial aspect of our lives, and distributed computing has grown significantly in size, capability, and complexity, becoming an essential element of our daily lives. There are numerous vulnerabilities in distributed network computing, including viruses, worms, distributed denial of service (DDoS), DoS, and more. One of the more newsworthy attacks recently has been distributed denial-of-service attacks. Attacks that cause denials of service (DoS) have developed and had an influence on the Internet infrastructure. A DDoS attack aims to prevent the targeted users from accessing a computer resource. In this paper, we perform a comprehensive survey of detecting DDoS packets via couple of machine learning algorithms such as Random Forest, Gradient Descent, decision tree, Multi-layer Perceptron, and radial basis function using the CIC-DDoS2019 dataset. We also combine all our machine learning models into one hybrid model via an ensemble classifier. After that, we experiment with a DDoS attack on a local server via Ufonet and capture the data packets during the attack using the Wireshark tool. The captured data will be converted to a CSV file and can be given as input to machine learning models. The results show that each machine learning algorithm is predicting DDoS packets incoming into the server. The experimental results show that the proposed DDoS attack detection method based on machine learning has a good detection rate for the current popular DDoS attack. Finally, directions for future research work have been pointed out. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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AudioGuard: Deep Learning Based Telugu DeepFake Audio Detection 15

AudioGuard: Deep Learning Based Telugu DeepFake Audio Detect...

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

作者： Karna, Siwani Haneesha, Samudrala Sai Santhoshi Jahanve, Poluru Reddy Pati, Peeta Basa Balakrishnan, Roshni M Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Recent advancements in deep fake audio technology have led to the creation of highly realistic synthetic voices that mimic human speech patterns. However, this technology raises ethical concerns due to its potential for misuse, including spreading misinformation, identity theft, and undermining trust in audio evidences. To address these issues, this research focuses on developing a method to detect deep fake audio specifically in the Telugu language. This research utilizes various Convolutional Neural Network (CNN) architectures like MobileNetV2, ResNet50, VGG16, and AlexNet to enhance accuracy and resilience. MobileNet achieved the highest accuracy of $\mathbf{9 9. 5 \%}$ and was chosen for deployment. The model is integrated into a user-friendly website using Flask, HTML, CSS, JavaScript, and MongoDB. This web application serves as a comprehensive solution for detecting Telugu deep fake audio, prioritizing user security, ease of use, and accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Multiscale Feature Pyramid Network-Enabled Deep Learning and IoT-Based Pest Detection System Using Sound Analytics in Large Agricultural Field 3rd

Multiscale Feature Pyramid Network-Enabled Deep Learning and...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Akkas Ali, Md. Sharma, Anupam Kumar Dhanaraj, Rajesh Kumar School of Computing Science and Engineering Galgotias University UP Greater Noida India Pune India

ISBN: (纸本)9789819708918

Modern farming techniques can now be implemented globally and at a reasonable cost. In the annals of agriculture, this is a significant turning point moment. The widespread dissemination of information and the advent of cutting-edge technologies have facilitated this revolution. However, pests cause substantial harm to farmland, which has economic, ecological, and societal consequences. As a result, there is a need to employ sophisticated computational methods to eradicate pests before they cause irreparable harm. Recently, ML-based studies have focused on agricultural issues. The study's primary objective is to develop a workable method for identifying pests in big agricultural fields using dependable pest analytics made possible by the Internet of Things. The proposed method incorporates several different sound pre-processing techniques drawn from the field of sound analytics. BPF, Triangular window, Kaiser window, FFT method, DFT, and PLP are all examples. A Multiscale Feature Pyramid Network (MFP-Net) model was trained, tested, and validated using data from an analysis of 650 pest sounds. Compared to the existing MS-ALN, YOLOv5, Faster RCNN, and ResNet-50 models, the recommended MFP-Net model correctly identified the pest with a 99.76% accuracy, 99.03% recall, and 99.06% F1-score. The capability of this research to detect pests in large agricultural fields early on is the primary reason for its significance. As a direct result, the number of crops produced will increase, improving the economic growth of the farmers, the nation, and the world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Weather-Integrated Recommendations for Food Choices with SHAP Explanation 1

Weather-Integrated Recommendations for Food Choices with SHA...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jain, Nikunj Hawari, Jawed Jha, Priyanshu Dinesh Kumar, K. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350372748

In the context of today's rapidly evolving technological landscape and the urbanization of Tier 1 and Tier 2 cities, cloud kitchens have emerged as pivotal players, offering quick and convenient meals without the need for dine-in facilities. The demand prediction for the food has become quite tough and could be considered as one of the significant challenges. Cloud kitchens face challenges in managing stock due to the absence of in-house diners, potentially resulting in wasted food. Moreover, adverse weather conditions often hinder the seamless delivery of online. To address these challenges, this project employs machine learning techniques to predict weather patterns and correlate them with food preferences according to that weather. The key algorithms found to deal with this problem were Random Forest, kNN, Decision tree and Naïve Bayes and Support Vector Machine. This serves the first half of the statement and then for recommendation a suitable system was developed. The pivotal role of the system is to reduce the food wastages for cloud kitchens by fostering adaptive strategies for efficient decision making. The key inputs from the market were found to be that 30-40% of the predicted sales in food industry doesn't meet due to various reasons. The ultimate goal is to empower cloud kitchen operations, ensuring adaptive and informed decision-making in response to changing weather dynamics. The union of both of these are explained using Interpretability tool SHAP for a convenient study. © 2024 IEEE.

关键词： Decision trees

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A Multi-Class Classification Framework with SMOTE Based Data Augmentation Technique for Alzheimer's Disease Progression

A Multi-Class Classification Framework with SMOTE Based Data...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Saketh, C.H. Ramasamy, Gayatri Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350371567

The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented capabilities in analyzing medical imaging data, particularly in neuroimaging like MRI scans. However, class imbalances within the dataset persist, making it difficult to discern intricate patterns indicative of Alzheimer's pathology. This project aims to overcome this challenge by integrating advanced DL techniques with data augmentation methodologies, specifically SMOTE. This strategic integration aims to overcome the limitations of class imbalances within the initial MRI dataset, enhancing the precision and reliability of the classification model. This interdisciplinary exploration aims to redefine Alzheimer's detection capabilities, enhancing diagnoses and offering the potential for broader applications in understanding and managing complex neurodegenerative disorders. © 2024 IEEE.

关键词： Classification (of information)

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Infield disease detection in citrus plants: integrating semantic segmentation and dynamic deep learning object detection model for enhanced agricultural yield

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Neural computing and Applications 2024年第35期36卷 22485-22510页

作者： Rani, N. Shobha Krishna, Arun Sri Sunag, M. Sangamesha, M.A. Pushpa, B.R. University Bengaluru India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Chemistry The National Institute of Engineering Mysuru India

Simultaneous and dynamic detection of diseases from infield images of citrus plants is the primary objective of this investigation. Refraining from discarding the disease-related issues would reduce agricultural yield and production, resulting in significant crop loss and economic instability. Computer Vision offers an inexpensive and efficient solution for detecting and predicting multiple diseases from infield images of citrus plants in various stages of plant growth. This study aims to perform simultaneous detection and prediction of cankers, mites, and nutritional deficiencies from infield images of citrus plants. For this purpose, over 4914 samples are annotated that are obtained from 441 image samples collected from orchards of citrus plants. Initially, the input image is subject to preprocessing followed by segmentation of prominent leaf regions and disease prediction. Contributions investigated in this work are two-fold: a deep semantic segmentation model named Dynamic U-Net is employed to extract prominent regions of interest, and a dynamic, lightweight object detection deep learning model for predicting diseases. From experimental outcomes, the segmentation efficiency is found to be 89.07% foreground accuracy, 0.7881 of IoU, and 0.9188 of the Dice coefficient. The object detection performance is quantified using the mapped metric, resulting in 0.85, 0.71, 0.64, and 0.27 efficiency concerning plant diseases, cankers, mites, and nutritional deficiency. As per the findings, the proposed approach is an effective solution to perform automated detection and prediction from infield images of citrus plants. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Citrus fruits

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