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CardioSense-A Deep Learning Enhanced Early Heart Disease Detection

CardioSense-A Deep Learning Enhanced Early Heart Disease Det...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Thirunavukkarasu, J. Dhivya Dharshini, J. Parthiban, M. Hemamaalani, S.J. Thiyagarajan, Janavi Kirthana Mohan, R. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

Heart disease continues to be a significant reason for death globally, our project highlights the importance of efficient methods for detecting and intervening in the early stages. We use an assortment of cutting-edge bio-sensor technology with deep learning techniques to introduce a new system that predicts heart diseases at early stages. The suggested framework continually tracks the heart rate and sounds through a machine provided with advanced biosensors. These include Random Forest classifiers and Deformable Attention Transformers as well as Vision Transformers that process data collected by the sensors. The LSTM model recognizes temporal patterns in the physiological signals whereas the Random Forest classifier helps to choose reliable features for classification. This is enhanced by Deformable Attention Transformer which improves capability of this model to concentrate on important segments of data increasing its predictive accuracy. Our research findings suggest that this combined system provides an accurate and reliable way to detect heart diseases at earlier stages, which can be very useful in health care sector. The potential of integrating biological data with advanced machine learning algorithms for medical diagnoses is shown by the efficacy of our proposed method. © 2024 IEEE.

关键词： Distribution transformers

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Machine Learning Strategies to Detect Phishing Website 5

Machine Learning Strategies to Detect Phishing Website

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Ashwatha, K. Smilarubavathy, G. St Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

Recent years have witnessed a substantial increase in website phishing attacks. Many researchers have developed software solutions to detect phishing websites, however it cannot detect these attacks completely. There were several minor challenges in identifying the fraudulent websites. Therefore, the most effective way to detect phishing websites is to incorporate machine learning algorithms into the attack detection process. This improves the overall accuracy of the project and allows for more efficient computation. Implementing machine learning algorithms can also help to overcome the challenges faced by the existing phishing attack detection models. The major purpose of this research is to use the dataset collected to train the ENASSEMBLE Machine Learning (ML) model to detect the phishing websites. © 2024 IEEE.

关键词： Websites

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Delineating Indic Transliteration: Developing A Robust Model for Accurate Cross-Script Conversion

Delineating Indic Transliteration: Developing A Robust Model...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Shukla, Aditya Agrawal, Pragati Jain, Sweta Maulana Azad National Institute of Technology Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350348460

The aim of this study was to develop a reliable model of text change between two writing systems. ""(meaning "Hello") can be transliterated into Latin alphabet as "namaste". To achieve this goal, it use open data with multiple parallel reading samples in both languages. The dataset includes source language terms and their translations in the target writing process. This data set plays an important role in training the transliteration model, as it contains linguistic structures and variables that make the model flexible. The transliteration model was developed using a hybrid sequencing approach combined with deep learning models. This method trains an LSTM-based model on a dataset with carefully selected loss functions. Other conceptual techniques and developments can further help the model to capture the complex relationship between the source and target languages. To test our transliteration model, we created a custom test data set. This test data set contains only new samples, so that the accuracy of the samples can be assessed in terms of text texts that are not present in the training data. We measure the accuracy of the model's text conversion using carefully calculated metrics. And for testing purposes, I installed this on my local host as well. This study demonstrates that open-source data sets can be successfully used to develop transliteration models. The excellent performance of the model in the testing process highlights the ability to transfer information between two writing systems. This research advances the field by providing a framework for building script translation models using publicly available data, which can enhance natural language processing and facilitate cross-language communication in language and writing system. © 2024 IEEE.

关键词： Deep Learning Long-Short Term Memory Open-Source Dataset Padding Sequence Sequence2sequence Transliteration

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Aiding Emergency Services Through Real Time Traffic Congestion Monitoring And Route Optimization Using Pic 2

Aiding Emergency Services Through Real Time Traffic Congesti...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： SThamarai Selvi, S.B. Thirumurugan, S. Kanish, P. Surya, M. K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350384277

The management of road traffic has emerged as a pressing concern due to a significant increase in accidents and congestion, particularly in densely populated urban areas. To address these challenges, a comprehensive solution has been proposed, consisting of a smart traffic light control and congestion avoidance system tailored for emergency situations such as ambulance services. This system comprises two main components: a smart traffic light control system and a smart traffic routing system. The former intelligently manages traffic lights to prioritize emergency vehicles, while the latter aims to mitigate congestion by providing alternative routes. Equipped with vehicle sensors linked to embedded systems, which in turn connect to ZIGBEE for data exchange, the controller efficiently regulates traffic flow at signal junctions. By strategically placing sensors near traffic signal poles, the system accurately assesses lane densities, facilitating real-time traffic management. Data storage is facilitated through ZIGBEE, while a microcontroller unit communicates alternative route information via LCD display, enhancing overall traffic management efficacy. © 2024 IEEE.

关键词： Zigbee

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Face Recognition System Using Multicolor Image Analysis and Template Protection with BioCryptosystem 37th

Face Recognition System Using Multicolor Image Analysis and...

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37th International Conference on Image and Vision Computing New Zealand, IVCNZ 2022

作者： Sardar, Alamgir Umer, Saiyed Rout, Ranjeet Kumar Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science and Engineering National Institute of Technology Srinagar India

ISBN: (纸本)9783031258244

This paper presents a multicolor image analysis-based face recognition system in an encrypted domain using the Tree Structure Part Model (TSPM) for face detection and a statistical feature extraction technique. The features are extracted from the RGB and YCbCr components independently of the input images. Then, an enhanced FaceHashing technique has been employed on the extracted feature vectors to generate cancelable biometrics (CB), i.e., cancelable biometrics for red (CBR ), green (CBG ), and blue (CBB ) of an RGB image, and similarly green (CBY ), blue (CBCb ), and red (CBCr ) for an YCbCr image. Further, we encrypted these cancelable biometrics using a novel BioCryptosystem technique and then stored them in a template database. This BioCryptosystem generates a security code called "CryptoCode" during the encryption process, which is stored on a smart card and in the template database as well. This "CryptoCode" is used as a decryption key and smart card verification. Then, a multi-class linear SVM classifier has been employed on the decrypted and query-cancelable feature vectors for both RGB and YCbCr biometrics individually. Finally, we employed the score fusion technique between the best score obtained from the classification of RGB biometrics and the best score obtained from YCbCr biometrics. For the performance evaluation, two benchmark facial databases, CVL and FEI, have been used. The system provides 100 % identification accuracy even after reducing the dimension from 500 to 100. The optimal security, reliability, efficiency, and speed of the proposed BioCryptosystem provide the novelty of this system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Detection of Plant Diseases in Hydroponics Farming Using Deep Learning Techniques

Detection of Plant Diseases in Hydroponics Farming Using Dee...

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Kundal, Ashish Sharma, Teek Parval National Institute of Technology Department of Computer Science and Engineering Himachal Pradesh Hamirpur India

ISBN: (纸本)9798350364040

In a country like India, where most of the income depends on agriculture, it is challenging to rely only on soil-based agriculture in the future. These days, soil-based agriculture faces several difficulties, including the reckless use of pesticides and chemicals, decreasing the land's fertility, urbanization, natural disasters, and climate change. Numerous innovative agricultural techniques have emerged to overcome the drawbacks of conventional farming methods, among which hydroponic farming has appeared as a prominent and influential solution. Hydroponics farming is an innovative way to grow crops without using soil. Instead of planting seeds in dirt, hydroponics uses a nutrient-rich water solution to feed the plant's roots directly. In this work, images of diseased plant leaves caused by the deficiency of nitrogen, phosphorus, and potassium in the nutrient solution and healthy leaves were collected. After that, YOLOv8 active deep learning approach is proposed for detecting and classifying leaf disease caused by the deficiency of these *** proposed Yolov8 model achieves outstanding results in terms of evaluation metrics, namely accuracy, precision, F1-score, and recall, giving 99%, 99%, 98.7%, and 98.0%, respectively. These results represent the model's ability to classify and detect the infected plants accurately. © 2024 IEEE.

关键词： Hydroponics

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Refining of Learning in Human Decision Making Models: A Simple Step towards Machine Unlearning

Refining of Learning in Human Decision Making Models: A Simp...

引用

2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Shrivastava, Alekh Ahirwal, Mitul Kumar Maulana Azad National Institute of technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350364088

In this paper the challenge of human decision-making (HDM) modeling by gambling task is targeted, specifically focusing on the Iowa Gambling Task (IGT). Traditional models train on data generated during gameplay, where decisions are made by the subject, although at times these decisions are random, leading to the model's inadequate understanding of natural human behavior. Here, we proposed a novel approach by utilizing realtime data from the IGT to model the HDM process involving uncertainty, risk, and ambiguity. Methodology in this study incorporates decay reinforcement learning for expectancy value updates, a prospect utility function for calculating utility values, and a time-dependent choice rule to determine the deck selection in the next trial. Additionally, mean square deviation (MSD) is employed to measure the disparity between actual and simulated data. By imposing restrictions on the continuous selection of decks, particularly limiting the number of consecutive selections, is a way towards concept of unlearning through the faulty pattern in data. The significance of this research lies in its ability to mitigate the impact of random deck selections during model training. By imposing constraints and aligning the model with more realistic human decision patterns, our approach offers a refined and accurate representation of human decision-making in gambling tasks. © 2024 IEEE.

关键词： Reinforcement learning

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An In-Depth Survey on Environmental Sustainability: Mitigating Energy Footprints for an Advanced Future Outlook 3

An In-Depth Survey on Environmental Sustainability: Mitigati...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Gunalan, K. Dakshana, M. Sangeetha, Sn Anandan, P. Saveetha, R. Abirami, S. Bannari Amman Institute of Technology Department of Computer Technology Tamilnadu India Sathyabama Institute of Science and Technology Department of Physics Tamilnadu India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350370867

The rapid proliferation of interconnected devices leveraging a diverse array of sensors for internet connectivity, communally known as the Internet of Things (IoT), holds immense potential to reshape both the natural environment and the economic landscape. Despite the minimal individual energy requirements of each sensor, the cumulative power consumption of trillions of such devices is substantial. Consequently, prioritizing the development of long-term, energy-efficient solutions within the IoT framework becomes imperative. This article advocates for the adoption of environmentally conscious IoT technology, elucidating its stance through an exhaustive analysis of energy-saving perspectives and techniques within the IoT domain. The core focus is on fostering the creation of environmentally IoT solutions that are energy-efficient and sustainable. To achieve this overarching objective, attention is directed towards four pivotal components: Machine-to-Machine (M2M) communication, Water-Efficient Telecommunications, Eco-Sustainable and Power-Efficient Wireless Sensing Networks (WSN);RFID (Radio-Frequency Identity) systems with reduced energy consumption, and Power-Efficient Microprocessor Units and Integrated Circuits (IC).Through the incorporation of these foundational principles, this study aims to ensure that the economic and environmental ramifications of IoT remain positive and enduring in the years to come. The ultimate goal is to steer the trajectory of future generations towards the development and adoption of IoT solutions that are not only environmentally conscious but also economically viable. © 2023 IEEE.

关键词： Environmental impact

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The Eye: An AI-Powered Video Streaming Platform to Protect Children from Inappropriate Content 6

The Eye: An AI-Powered Video Streaming Platform to Protect C...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： El Bakri, Ahmad Yehia, Abdullah Ali, Mariam Osama, Reem Adel, Zeyad Gamal, Othman Saleh, Sherine Nagy Arab Academy for Science Technology and Maritime Transport Computer Engineering Department Alexandria Egypt

ISBN: (纸本)9798350378511

The Internet is now a vital part of our daily life including young children who watch their favorite cartoons using different streaming platforms. Unfortunately, these platforms often host content deemed inappropriate for children due to limited regulations. Manually reviewing the vast content is impossible, leaving parents with limited options to protect their children. Since parents in the Arabic culture have different beliefs on the content their children should be exposed to, and cannot rely on generic Kids' filters, accordingly 'The Eye' is proposed. The Eye is a streaming platform that allows children to watch videos while providing parents full flexibility to choose the exact content flags they deem inappropriate, ensuring their children enjoy a safe and age-suitable entertainment environment. The proposed model presents a hybrid CNN-RNN model to classify video content based on the predefined flags. A dataset was meticulously collected serving the different flags needed to train the proposed model and accordingly, the proposed trained model achieved an accuracy of approximately 67% on the test data. The model is deployed as a cloud-based service utilizing AWS infrastructure for efficient video processing and classification. © 2024 IEEE.

关键词： Video streaming

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Tracking Waste Disposal and Generation of Fine to control and promote proper waste segregation

Tracking Waste Disposal and Generation of Fine to control an...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Ashok, K.N. Kavyashree, K.S. Keerthana, S. Nagashree, C.R. Navya, L. S J C Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

The metropolitan cities in India alone produce about 10 million tons of waste. That is, if left untreated, the waste materials will cause several types of pollution which are water, soil and air that destroy our ecosystem completely. People can avoid this madness by being waste conscious at an individual level. Lack of awareness and weak imposition of rules by authorities on separate household garbage pick-up are some reasons why many policies have failed to accomplish this. Thus, we aim to monitor and fine them whenever they don't obey the rules in segregating their household wastes. This project mainly deals with using web camera images to determine the percentage of non-segregating biodegradable from non-biodegradable waste during its collection by the municipal bodies for determining the fines based on improper disposal methods of waste products. One way of analysing the proportion of nonsegregated garbage is through Convolution Neural Networks (CNN), a deep learning algorithm. This groundbreaking solution advances responsible trash disposal methods that enhance a cleaner living environment for all people and future generations alike © 2024 IEEE.

关键词： Convolutional neural networks

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