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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Santhiya S Lalitha Krishnasamy Sharmila C Jayadharshini P Dharshan P P Haripreetha S Department of Artificial Intelligence Kongu Engineering College Erode India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of CSE SRM Institute of Science and Technology Tiruchirappalli India Artificial Intelligence and Data Science Kongu Engineering College Erode India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

In tropical regions, coconut palms are an essential part of the economy and daily life, providing food, oil, and many other products. However, their health and productivity can be affected by various diseases. Early and precise disease identification is essential for the management and prevention of many diseases. Leaflet diseases in coconut plants are those that impact individual leaflets, or leaf segments, on the fronds of coconut palms. Drying leaflets in coconut tree leaves can result from a variety of factors, including biotic (living organisms) and abiotic (environmental or non-living) impacts. In coconut trees, chlorosis, or the yellowing of the leaves, is a prevalent issue that can be caused by some factors. The CNN model is trained on a sizable set of high-resolution images of both healthy and diseased coconut leaves. Each disease's unique characteristics and patterns are automatically acquired with the aid of the deep learning architecture, enabling accurate categorization. Using an additional set of annotated photos of coconut tree leaves, the CNN model is assessed with remarkable accuracy in identifying illnesses. Comparing the system to traditional manual diagnosis procedures demonstrates the technology's efficacy and potential for early disease identification. Using this strategy raises the algorithm's accuracy to 95%

关键词： Deep learning Productivity Accuracy Prevention and mitigation Oils Vegetation Prediction algorithms Organisms Convolutional neural networks Diseases

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Machine Learning based Human Stress Detection through Sleep Patterns 5

Machine Learning based Human Stress Detection through Sleep ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Santhosh Krishna, B.V. Mai, Kiran Aswini, S. Biju, J. Mohanraj, S. Kaliappan, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Bangalore Technological Institute Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technology Information Science and Engineering Erode Tamilnadu Sathyamangalam India KGiSL institute of Technology Artificial Intelligence and Data Science Tamilnadu Coimbatore India Lovely Professional University Division of Research and Development Punjab India

ISBN: (纸本)9798331504403

Stress is a critical factor in overall wellbeing, and understanding its patterns during sleep can provide valuable insights into improving sleep quality and managing stress-related health issues. Stress has become an increasingly prevalent issue in modern society, impacting both mental and physical well-being. The ability to accurately detect stress levels in individuals is crucial for timely intervention and management. Sleep patterns serve as a valuable indicator of stress, as disruptions in sleep quality and duration often correlate with heightened stress levels. Leveraging advancements in machine learning techniques, this study proposes a novel linaer regression based approach to detect human stress by analyzing sleep patterns extracted from wearable devices. The proposed system consists of several key components. Firstly, data collection involves the continuous monitoring of sleep parameters such as sleep stages, heart rate variability, and movement using wearable sensors. Next, feature extraction techniques are applied to transform raw sleep data into informative features that capture relevant aspects of sleep patterns indicative of stress. © 2024 IEEE.

关键词： Sleep research

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Machine Learning Approach for Detecting Liver Tumours in CT images using the Gray Level Co-Occurrence Metrix 1

Machine Learning Approach for Detecting Liver Tumours in CT ...

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1st IEEE International Conference on Applied intelligence and Sustainable Computing, ICAISC 2023

作者： Aruna, S. Saranya, A. Guru Pandi, D. Kavya, S.P. Pareek, Piyush Kumar Computational Intelligence SRMIST Chennai India SRMIST Computational Intelligence Department Chennai India Panimalar Engineering College Department of ECE Chennai India KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Arasur India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Over 2.4 % of deaths in India each year are caused by liver diseases. Due to its mild early signs, liver disease is also challenging to diagnose. Frequently, the signs only become obvious when it is too late. Even more challenging than segmenting the liver is segmenting the tumour from the liver. Imaging procedures like computed tomography, magnetic resonance imaging, and ultrasound are utilised to separate the liver and liver tumour once a sample of liver tissue has been removed. This research suggests a machine learning method from CT images-based automatic assistance system for stage categorization. Then, the features are extracted from the CT images using the Gray-Level Co-Occurrence Matrix (GLCM) method. Finally, it is suggested that the computed tomography (CT) pictures of livers containing tumours be categorised using a Random Forest technique. Using the described method, liver tumour images are classified as benign or malignant. The real value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16% based on the experimental investigation. Using the described method, liver tumour images are classified as benign or malignant. These modifications improve the system's ability to recognise the tumour from the CT pictures. © 2023 IEEE.

关键词： Machine learning

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Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2309-2321页

作者： Raghupathy, Bala Krishnan Vairavasundaram, Subramaniyaswamy Ganesan, Manikandan Namachivayam, Rajesh Kumar Kotecha, Ketan Herencsar, Norbert SASTRA Deemed University Department of CSE India Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India SASTRA Deemed University School of Computing India Symbiosis Centre for Applied Artificial Intelligence Pune India Brno University of Technology Faculty of Electrical Engineering and Communication Department of Telecommunications Technicka 3082/12 Brno61600 Czech Republic

Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain. © 2024 IEEE.

关键词： Convolutional neural networks

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Federated Learning for SMS Spam Detection: A Privacy-Focused Approach 15

Federated Learning for SMS Spam Detection: A Privacy-Focused...

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

作者： Vats, Satvik Shastri, Suryakant Mehta, Shiva Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Iimt College of Engineering Artificial intelligence and Data Science Department Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

Herein proposes a new classification technique of SMS spam within a federated learning system, based on which the process occurs on the user's device. The objective is to realize the perfect balance of the two;on the one hand, it should be proactive with spam detection, and on the other hand, it should have the highest respect for privacy protection. Our research demonstrates that this method has significantly boosted classification accuracy without privacy issues by equating an appropriate evaluation technique for extensive testing and assessment. The statistical analysis shows that our federated learning algorithm significantly reduces loss;according to the statistical analysis results, this value increases from 0.267 to 0.389 from the first to the tenth epoch. Also, the system reveals that this mechanics remains excellent and keeps showing values of more than 90%, and the third epoch shows nothing but a high accuracy value of 98.3%. In effect, we analyze the cornerstone of our knowledge based on the method of the critical priorities potentially tightened privacy protection for federated learning. The federated learning solution offers a transparent approach that enables users to access essential services directly on their devices, and then the trained machine learning models are merged securely. This study underscores the competency to differentiate spam from anything useful in electronic communication and considers it a significant issue as regards privacy protection. The outcome gives a sense of hope in the idea of federated learning to have specific SMS spam. It creates a good gap between effectiveness and privacy protection, which is especially important considering that mobile communication covers a vast territory today. © 2024 IEEE.

关键词： Federated Learning Machine Learning Mobile Communication On-Device Learning Privacy Preservation SMS Spam Detection

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Early Prediction and Grading of Diabetic Retinopathy using Fusion Based Learning System 3

Early Prediction and Grading of Diabetic Retinopathy using F...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Dhanalakshmi, V. Baskar, K. Ayyadurai, Sathiya Suchithra, B. Kongunadu College of Engineering and Technology Department of Cse Thottiyam Trichy India Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Thottiam Trichy India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Karur India Sri Eshwar College of Engineering Department of Cse Coimbatore India

ISBN: (纸本)9798350361186

Diabetic retinopathy (DR), a visual impairment caused by long-term diabetes mellitus, is a type of diabetes that destroys the retinal blood vessels. Determining and quantifying this form of diabetes from retinal pictures takes time and experience. The classification of DR necessitates the grading of multiple aspects. Finding the severity is a process that ophthalmologists must perform with great care and attention, and it may be error-prone. Convolutional neural networks (CNNs) with a transfer learning technique have been proposed to automatically diagnose diabetic retinopathy by gauging its severity. The enhanced version of VGG16 performed in a very admirable way. The rate of training and validation accuracy increased steadily with each subsequent frame. Overall, a VGG-16 model trained on the DIARETDB1 dataset achieved strong performance of 0.89 quadratic weighted kappa on the task of detecting diabetic retinopathy from fundus images. This suggests the approach is effective for this medical imaging application. © 2024 IEEE.

关键词： Grading

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Brain Tumor Automated Detection System Based on Hybrid Deep Learning Networks Using MRI Images

Brain Tumor Automated Detection System Based on Hybrid Deep ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Salama, Gerges M. Ashraf, Shady Bayoumi, Esraa Salah Elwan, Mohammed Abd-Ellah, Mahmoud Khaled Minia University Faculty of Engineering Department of Electrical Engineering Minia6111 Egypt Egyptian Academy for Engineering and Advanced Technology Electrical Engineering Department Egypt College of Computing and Information Technology Arab Academy for Science Smart Village GizaB 2401 Egypt Egyptian Russian university Faculty of Artificial Intelligence Cairo11829 Egypt

ISBN: (纸本)9798350351408

technology undeniably has a significant impact on human life, particularly in advancing various aspects of healthcare. artificial intelligence (AI) has greatly contributed to the early and precise detection of tumors, especially in the realm of medical image processing, with a specific focus on brain tumor detection using magnetic resonance imaging (MRI). However, manually analyzing numerous MRI images is a challenging task in clinical settings. Hence, the utilization of a computer-aided diagnostic (CAD) system is imperative for the timely identification of normal and abnormal brain tissues. This study proposes a hybrid detection architecture to determine the presence of brain tumors. The system incorporates five different deep convolutional neural networks (CNNs) for feature extraction, each comprising five models that identify the optimal five layers for feature extraction before employing Support Vector Machine (SVM) for classification. The system undergoes training and evaluation to ascertain the most effective detection outcomes. The study utilizes two datasets to assess the system's performance and functionality. © 2024 IEEE.

关键词： Support vector machines

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Cardiac Affliction Detection Using Improved Grasshopper Optimized CNN

Cardiac Affliction Detection Using Improved Grasshopper Opti...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Shamna, B. Maheswaran, C.P. Noorul Islam Centre for Higher Education Department of Computer Science and Engineering Tamil Nadu Nagercoil India Sri Krishna College of Technology Department of Artificial Intelligence and Data Science Coimbatore641 042 India

ISBN: (纸本)9798350333244

The most significant cause of death globally is cardiovascular disease. Whenever an accurate diagnosis is obtained at the earliest possible stage, cardiovascular problems are avoided. Electrocardiogram (ECG) tests are typically used to screen cardiac conditions, and facilitates healthcare process. Due to the dependence and direction of trained medical professionals, traditional methods of evaluating ECG images require more work and time. As a result, automated methods become necessary and image processing is then used to the medical industry. In this research, a neural network-based, effective computational method for determining heart disease is developed. In general, the input ECG images are first pre-processed using Wiener filter, which calculates the mean and variance and then conducts enhanced smoothing in low-variation circumstances. The K-means clustering method is adopted to perform segmentation on pre-processed image in precise manner. The Grey Level Co-occurrence Matrix (GLCM), which chooses the predominant features is utilized to enable accurate diagnosis of conditions in ECG images, then extracts the pertinent features. Finally, for accurate classification, an Improved Grasshopper Optimized Convolutional Neural Network (IGO- CNN) is used. The obtained outcomes demonstrate that the suggested work assists in the establishment of exact outputs and successfully recognizes the presence of heart disease in ECG images. © 2023 IEEE.

关键词： Electrocardiograms

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Serverless Blockchain-based AI-Powered Financial Transaction Management System on Cloud 3

Serverless Blockchain-based AI-Powered Financial Transaction...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Sri Sathya, K.B. Nithyasri, A. Amirtha, E. Fowjiya, S. KPR Institute of Engineering and Technology Computer Science and Engineering Coimbatore India M. Kumarasamy College of Engineering Artificial Intelligence and Data Science Karur India Computer Science and Technology Tiruchengode India

ISBN: (纸本)9781665456647

The Finance Manager Application is used to automate the existing finance tracking system and to offer the user to have a more engaging and aesthetic user experience with the bill tracking feature. The existing systems are used to record several financial transactions and events. They are not giving an overview of the amount spent and in which category the user has transacted more amount and hence it does not support expense management. Mainly the existing finance tracking systems are not using AI-based authentication which is more convenient for the user. Our web application is proposed to give transaction charts and graphs that give more clarity of the income, expenses, and transfers. In addition, we can analyse the transactions in category-wise, and we can track the history. Along with form-data authentication, we are providing OAuth and Zero-effort authentication that is more user friendly. Our SPA provides the facility to add multiple transactions at a time and it is to show the balance in the wallet on the future according to the planned transactions. Also, it can automate the data entry for recurrent transactions. © 2022 IEEE.

关键词： Finance

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Remote Sensing data-Based Satellite Image Analysis in Water Quality Detection for Public Health data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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