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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Rajesh Kanna, R. Ulagamuthalvi, V. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348002

Wheat is essential everywhere. Wheat leaf diseases hinder growth. Wheat leaf disease identification is crucial to wheat quality and agriculture. This work presents an integrated machine learning strategy to improve wheat leaf disease identification: Colour Layout Filter with Linear Discriminant Analysis, Colour Layout Filter with Quadratic Discriminant Analysis, RGB Filter with Linear Discriminant Analysis and RGB Filter with Quadratic Discriminant Analysis can identify damaged wheat leaves. The agricultural autonomous leaf infection detection system uses images, processing, feature extraction, selection, and learning. This technology helps farmers quickly and reliably diagnose plant illnesses. Automatic leaf disease detection speeds crop diagnosis. This study's Linear Discriminant Analysis Color Layout Filter classifies wheat diseases well. LDA-CLF is most accurate at 88.33%. QDA-RGBF has 80% accuracy. CLF with LDA 0.88 is ideal. RGBF QDA accuracy is 0.80, poor. LDA-CLF recalls 0.88. RGBF with QDA recall is 0. S0, low. With 0.66 kappa, CLF and LDA lead. RGBF with QDA has lowest kappa (0.49). Our best model is CLF's0.88 LDA F-Measure. QDA-enhanced RGBF has 0.80 F-Measure. LDA-CLF has the highest MCC at 0.66. RGBF QDA MCC lowest is 0.5. 0.93 CLF-LDA ROC. RGBF's LDA-based ROC is 0. S4. RGBF QDA models have the highest PRC (0.92). RGBF-LDA has the lowest PRC (0.78). Linear Discriminant Analysis-based Color Layout Filters outperformed other models in this study. © 2023 IEEE.

关键词： Color

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Arduino Uno-Powered Parking Guidance System with Ultrasonic Sensors 1

Arduino Uno-Powered Parking Guidance System with Ultrasonic ...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Subathra Arul Prakash, A. Ramalingam, Vijay Vignesh, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328042

Arduino Uno - Powered Parking Guidance System with Ultrasonic Sensors"is a smart and innovative solution for simplifying the parking process in crowded urban environments. This paper leverages the power of Arduino Uno microcontroller and ultrasonic sensors to create a cost-effective and efficient parking assistant system. The primary objective of this paper is to assist drivers in parking their vehicles accurately and safely by providing real-time feedback on the distance between the vehicle and obstacles in its vicinity. Ultrasonic sensors are used to detect the presence of obstacles, calculate the distance to these obstacles, and relay this information to the driver through visual and auditory cues. This research comprises an ultrasonic sensor module, an Arduino Uno microcontroller, and a visual display, such as an LCD or LEDs. The ultrasonic sensor emits high-frequency sound waves and measures the time it takes for these waves to bounce back after hitting an obstacle. Using this data, the Arduino Uno calculates the distance to the obstacle and displays it on the chosen output device. The system provides precise distance measurements, allowing drivers to gauge their proximity to obstacles accurately. Drivers receive immediate feedback through a visual display, making parking safer and more convenient. The use of readily available components and open-source Arduino programming makes this parking assistant an affordable alternative to commercial parking aids. The system is designed for ease of use, with clear and intuitive distance information that assists drivers of all skill levels. Users can adapt the system to suit their preferences, including configuring different alert thresholds or integrating additional features like audible alarms. This Ultrasonic Sensor Parking Assistant with Arduino Uno offers a versatile and accessible solution for urban parking challenges, enhancing safety and convenience for drivers while promoting DIY electronics education. Its potent

关键词： Ultrasonic sensors

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Toward Symptom Assessment Guided Symptom Investigation and Disease Diagnosis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1752-1766页

作者： Tiwari, Abhisek Raj, Rishav Saha, Sriparna Bhattacharyya, Pushpak Tiwari, Sarbajeet Dhar, Minakshi Indian Institute of Technology Department of Computer Science and Engineering Patna800013 India Indian Institute of Technology Department of Computer Science and Engineering Bombay400076 India Midnapore Homeopathic Medical College and Hospital Midnapore721101 India All India Institute of Medical Sciences Department of Medicine Rishikesh249203 India

Automatic disease diagnosis has gained immense popularity and demand over the past few years, and it is emerging as an effective diagnostic assistant to doctors. Diagnosis assistants assist clinicians in conducting a thorough symptom investigation and identifying possible diseases. Doctors correctly diagnose patients by observing only a few symptoms in most cases, even though the diagnosed disease has numerous symptoms. Also, some common symptoms, such as fever and headache, usually emerge due to other symptoms, which do not play a major role in identifying suffering diseases. In this work, we investigate the role of symptom importance in disease diagnosis through several feature engineering techniques and propose a novel symptom assessment guided symptom investigation and disease diagnosis (SA-SIDD) assistant using hierarchical reinforcement learning (HRL). The proposed SA-SIDD assistant first collects an adequate set of symptoms/sign information through conversing with users and then diagnoses a disease based on the extracted symptoms. We incorporated a symptom assessment module with the diagnosis framework that evaluates the relevance of current inspected symptom at each turn and reinforces the assistant to investigate distinctive and context-aligned symptoms using an assessment critic. The proposed methodology outperforms the state-of-the-art method, HRL, on two publicly available datasets, which firmly establishes the crucial role of symptom importance in disease diagnosis and the need for the proposed symptom assessment incorporated disease diagnosis framework. Furthermore, we have also conducted a human evaluation, revealing that the diagnosis method greatly enhances end-user satisfaction because of context-aligned relevant and minimal symptom investigation. © 2020 IEEE.

关键词： Reinforcement learning

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Unveiling Hidden Patterns: Exploration of Data Dependencies in Disease Prediction

Unveiling Hidden Patterns: Exploration of Data Dependencies ...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Durga Devi, P. Akila, K. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350394580

In the current scenario, the possibility of getting affected by disease is high so it is important to predict in advance. Due to technological advancement, it is for early prediction. Hence, this paper is focused on analysis of various DL algorithms for prediction system. This work provides a comprehensive report for the disease prediction system with the analysis of test input parameters for different disease. Further, different algorithms useful for the prediction framework has been discussed. Studying the interdependence of these risk factors in patients' medical records and comprehending their respective contributions to stroke prediction is critical. Deep learning techniques are crucial in overcoming these issues since they enable automatic disease identification in healthcare facilities. The prediction framework is analyzed using data from the Electronic Health Record dataset. Parametric statistics are used to evaluate the results. © 2023 IEEE.

关键词： Electroencephalogram(EEG) Electronic Health Record(EHR) Gated Recurrent Unit(GRU) Long Short Term Memory(LSTM) Recurrent Neural Network(RNN)

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Forest Cover Type Classification Using Machine Learning Algorithms 2

Forest Cover Type Classification Using Machine Learning Algo...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Suhail, Shaik Javeed Refonaa, J. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347456

It is important to understand the different types of forest cover because forests are critical for decreasing air pollution, controlling greenhouse gas emissions, and preserving ecological equilibrium. So, it becomes very important to understand the types of trees present in different parts of the forest. So, using machine learning techniques, a structure is developed to entirely automate this procedure. A variety of classification algorithms were used to classify different forest cover types based on cartographic features. Supervised machine learning algorithms were used. Classification algorithms used in this study were Decision Tree Classifier, Multinomial Logistic Regression, k-Nearest Neighbors Classifier, and Random Forest Classifier. Different performance criteria were performed to measure each classifier's performance. The Random Forest classifier outperforms the other three classifiers in this study. It helps the users classify the cover type easily without having to deal with all that data. In the end, it is integrated with the Flask framework. With the help of this classification, further research can be carried out regarding the inhabitants of forests, deforestation, forest fires, and many other concerns. © 2023 IEEE.

关键词： Deforestation

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Enhanced Predictive Model to Diagnose Coronary Heart Disease Using Decision Tree 2

Enhanced Predictive Model to Diagnose Coronary Heart Disease...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Joshi, Sujata Dixit, Ananya Nair, Mydhili K. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. Institute of Technology and Management Department of Information Science and Engineering Bangalore India School of Computer Science Rv University Bangalore India

ISBN: (纸本)9798350373110

As per WHO reports, Heart diseases or Cardiovascular Diseases (CVDs) rank as the leading cause of death worldwide. Approximately 17.7 million individuals succumbed to CVDs in 2015, accounting for 31% of all global fatalities. Among these fatalities, an estimated 7.4 million were attributed to coronary heart disease, one of the prevailing forms of heart disease.. According to the Global Burden of Disease study, nearly quarters (24.8%) of all deaths in India are due to CVD. Hence there is a need for careful and periodic methods of examining heart related diseases. The majority of cardiovascular diseases found in India are coronary heart diseases (CHD) The main contribution of this work is to develop efficient predictive models for Coronary heart diseases using decision trees. In this work, the predictive models are developed for diagnosis of heart disease using the cardiovascular disease dataset from UCI repositories with the objective of improving the accuracy. We have also obtained echocardiograph data obtained from M. S. Ramaiah hospital and used in this work. It is found that the data used in this work is heterogeneous in nature and the decision trees work well on heterogeneous data and also they are expressive and interpretative, we have opted to use decision trees. The predictive model namely Enhanced-Pred-Model CVD-DATA-DT is developed using both the datasets. It is found that the model shows marginal accuracy increase from 77% in baseline model to 80% by the proposed model. The proposed method has better performance in terms of accuracy as well as error rate. The models are also tested on the echocardiograph data for prediction of LVH event and has shown improvement in the performance. © 2024 IEEE.

关键词： Prediction models

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Prediction of Lung Infections with Deep Learning Techniques: A Systematic Review 3

Prediction of Lung Infections with Deep Learning Techniques:...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Thangamuthu, Vaishnavi Kamaraj, K. Hezekiah, James Deva Koresh KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350343632

This research study systematically examines the application of deep learning techniques in predicting lung infections including cancer, pneumonia, tuberculosis, Covid 19 and so on. The analysis includes a range of deep learning models, including convolutional neural networks (CNNs) and various architectures like ResNet, VGG, AlexNet, and others. The review assesses the efficacy of these techniques in forecasting lung infections on their computational complexity and accuracy. This study also diversified by utilizing both chest CT images and X-ray images on the predictions. The insights derived from this systematic review contribute to a better understanding of the potential and limitations of employing deep learning in the early detection of lung infections. © 2023 IEEE.

关键词： 2019-nCoV AlexNet CNN GoogleNet LeNet ResNet SARS-CoV-) VGG

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A Novel Hybrid Approach to Smart Door Locking System based on Real Time Data Transmission 14

A Novel Hybrid Approach to Smart Door Locking System based o...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pradeepkumar, G. Rohith Bhat, C. Senthilvadivu, K. Latha Jothi, V. Sharanyanivasini, J.S. Kumar, Neelam Sanjeev Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Simats Institute of Computer Science and Engineering Saveetha School of Engineering Chennai India Kongu Engineering College Department of Artificial Intelligence Tamil Nadu Perundurai India Velalar College of Engineering and Technology Department of Computer Science and Engineering India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

A wireless remote-control lock system is used for pedestrian entrances to both homes and businesses. To engage and disengage a door latch component, this locking mechanism is powered by electricity and consists of a striker plate assembly with a movable striker plate element A radio frequency receiver and a circuit that responds to signals from an operator-controlled radio emitter power the striker assembly. The striker assembly has the ability to unlock the door either temporarily or permanently. The circuit in the control unit is made up of a self-latching relay, a selection switch, and a second relay. The door will stay open for up to 3.5 seconds, or until the emitter transmits a new signal to the receiver. Because everyone is accustomed to using physical keys to lock and unlock doors, this is the most natural and prevalent method. The physical key is a well-known and dependable mechanism, but it has limitations. It is impossible to have multiple distinct keys for the same lock. Several of the locks work with multiple key types. © 2023 IEEE.

关键词： Signal receivers

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Fake Account Detection on Social Media using Random Forest Classifier 7

Fake Account Detection on Social Media using Random Forest C...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Nikhitha, Kancharla Venkata Bhavya, Karnati Nandini, D. Usha Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

As the growth of online social networks increases, everyone is associated and linked with social media. A massive amount of personal data is being attacked and stolen by cyber attackers. These fraudulent profiles also spread negative publicity, false news, and various malicious programs. One of the best ways is to detect these fake accounts, not only the best way but also it is the first step to stopping the negative news and false rumours. There are many efficient ways and techniques to detect these fake accounts. However, these techniques are based on or depend on the account features. The main aim of this research is to examine three machine learning algorithms i.e., random forest, logistic regression, and decision tree. The level of efficiency in the three mentioned methodologies are compared and the highest accuracy has been achieved for random forest classifier. © 2023 IEEE.

关键词： Social networking (online)

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Breast Cancer Detection and comparative analysis of Convolutional Neural Networks and VGG-16 3

Breast Cancer Detection and comparative analysis of Convolut...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Maniraj, S.P. Gupta, Ayush Mullamuri, Venkata Yaswanth Soumya, Prateek SRM Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350338607

Most common machine learning algorithms were designed to tackle particular problems in seclusion. Based on the business requirements, a machine learning algorithm was employed for a particular task and these algorithms were applied to train models separately on specific feature sets. Most of the machine learning models are trained on the assumption that the test and training sets have similar features while sharing the same underlying distributions. Convolutional Neural Networks (CNNs) have demonstrated remarkable potential for breast cancer detection using histopathology images. Moving forward with the latest research, we present VGG-16 architecture for breast cancer detection, which involves the use of pooling and convolutional layers to extract high-level accuracy from histopathology images. The proposed network consists of sixteen layers, each of which performs convolutional operations on the image to detect local features and pooling operations with the aim of simplifying the feature maps' dimensionality. The test results illustrate that the VGG-16 architecture secures superior classification accuracy when compared with the previous cutting-edge models. © 2023 IEEE.

关键词： Image classification

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