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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Ronson Danthy K Pratham Pai Vidya Rao Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

The banking system is a key component of financial transactions and economic expansion in the modern world. The introduction of internet banking, however, has created some brand-new difficulties, particularly in providing safe user authentication. This study investigates the flaws in conventional authentication techniques, including passwords and SMS-based One-Time Passwords (OTP), and suggests a creative fix. By using Time-Based One-Time Password (TOTP) created by an authenticator app and encrypting the user credentials with this as key, the proposed solution improves security. By addressing problems including poor password habits, SMS theft, OTP delivery delays, phishing, and social engineering attacks, this method strengthens the security of online banking. The approach provides a thorough framework for building this secure authentication system by detailing TOTP generation and encryption. This system successfully defends against popular attacks such as replay attacks, brute force attacks, rainbow tables, packet sniffing, and random guessing.

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Automated Detection of Language Disorders in Children Using NLP and Machine Learning

Automated Detection of Language Disorders in Children Using ...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Malathi, P. Legapriyadharshini, N. Nair, Shruthi S Sujatha, M.P. Sadaieswaran, R. Thirumalaikumari, T. Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Department of Computer Science and Engineering Chennai India Tagore College of Arts and Science Department of Computer Applications Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350309140

The purpose of the study is to address the essential need for a more efficient and objective technique for detecting language disorders in children, overcoming the constraints of standard manual evaluation systems. Existing systems, which utilize subjective evaluations and manual scoring, frequently result in lengthy processes and possible errors. To solve these issues, the proposed system automates the identification process by combining Natural Language Processing (NLP) and Machine Learning (ML). The system achieves a breakthrough in accuracy by normalizing text and extracting a comprehensive collection of linguistic features using advanced NLP techniques. The implementation involves difficult data preprocessing, extensive feature extraction, and the application of a cutting-edge Support Vector Machine (SVM) classifier. The proposed system achieves a 15% increase in accuracy, a 12% increase in precision, a 10% increase in recall, and an 11% increase in F1 score over existing systems. The confusion matrices show improved prediction skills, while accuracy across varied linguistic samples demonstrates the system's adaptability. Finally, the proposed system not only speeds up the detection of language disorders but also provides the groundwork for deeper insights into linguistic development, with revolutionary potential in educational and therapeutic settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Design and Analysis of Deep Learning based Water Potability Prediction

Design and Analysis of Deep Learning based Water Potability ...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Arumugam, M. Vijayakumar, P. Kannan, A.C. Velmurugan, J. Arun, G. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - Pg Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi638057 India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300857

According to the United Nations, having access to clean water for consumption is a fundamental human right. General Assembly in 2010. The importance of safe drinking water cannot be overstated. Unsafe drinking water access is a significant public health concern, especially in poor nations. In many regions of the world, waterborne illnesses including cholera, typhoid fever, and dysentery are important causes of illness and death. These diseases are especially dangerous for children, who are more vulnerable to their effects. In addition, a lack of access to clean drinking water can result in other health issues like stunted growth and malnutrition. Access to safe drinking-water is also a key component of effective policy for health protection at all levels, whether it is at the national, regional, or local level. Policies aimed at improving access to safe drinking-water can include measures such as water quality testing, water treatment, and the provision of clean water sources. Investments in water supply and sanitation can have a positive economic impact, especially in areas with inadequate access to clean drinking water. The costs of implementing the treatments may be outweighed by the reductions in unfavorable health impacts and medical expenses that may result from these investments. In conclusion, having access to clean drinking water is crucial for encouraging wellness, reducing waterborne illnesses, and raising standard of living in general. The input to the DNN could be the various water quality parameters, such as pH, hardness, chloride content, and total dissolved solids, while the output would be a binary classification indicating whether the water is potable or not. The DNN would need to be trained on a large and diverse dataset of water samples to accurately classify new samples. © 2023 IEEE.

关键词： Potable water

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Cover patches: A general feature extraction strategy for spoofing detection

Cover patches: A general feature extraction strategy for spo...

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作者： Cai, Guorong Su, Songzhi Leng, Chengcai Wu, Jipeng Wu, Yundong Li, Shaozi Computer Science and Engineering College Jimei University Xiamen361021 China Department of Intelligence Science and Technology Xiamen University Xiamen360005 China Department of Mathematics Northwest University Xi'an City710127 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China

Face anti-spoofing has attracted many attentions in security applications, such as mobile payment and entrance guard. Until now, face anti-spoofing technique is still a challenging task. Mainstream image-based spoofing algorithms usually use global motion or texture information to distinguish whether an input face is live or fake. However, the performance of these methods are sensitive in light changes, or images acquired from different sensors. The main reason is that spoofed face image always has slight different texture in local areas, such as landmark or salient region of face. To this end, this paper proposes a novel multi-patches feature extraction strategy to detect spoofing. First, a set of patches with specific combination scheme is selected to cover the face image. Second, features such as hand-crafted Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), or deep features are extracted from these patches. Third, all features are combined as the global descriptor of the face image, then fed into an SVM classifier to verify the anti-spoofing detection. Experimental results show that the proposed strategy can effectively enhance the performance, concerning with the accuracy of spoofed face detection in four widely used anti-spoofing databases. © 2018 John Wiley & Sons, Ltd.

关键词： Local binary pattern

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Pattern Recognition Based Skin Lesion Stage Analysis Using IoT

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SN computer science 2024年第5期5卷 473页

作者： Kollu, Venkatagurunatham Naidu Sharma, Girish Kumar Kautish, Sandeep Manikandan, R. Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Applications Bhai Parmanand DSEU Shakarpur Campus-II Shakarpur Delhi 110092 India Dean-Academics COER University Roorkee India Professor & amp School of Computing SASTRA Deemed University Thanjavur India

Skin diseases are considered the emerging cause of high mortality throughout the world. To reduce the rate of death, that arise due to skin is minimized with early detection and cure. The lesions in the skin need to be evaluated significantly to increase the detection rate in the early stage. Nowadayscomputer-Aided Diagnosis (CAD) is considered as the effective technique to detect lesions in the skin with the use of pattern recognition technique. Through CAD system skin lesions are classified into different classes. In other hand, medical environment uses the Internet of Things (IoT) for the monitoring and detection of diseases in patients. Hence, this paper constructed a framework of Dragon Pattern Optimization Stacked Classifier (DPOSC) automated model for the early detection of skin lesions. The DPOSC automated model is interconnected with the IoT devices implanted in the patient body for the classification of lesions as malignant, benign and normal in the early stage. The DPOSC model perform the image pre-processing with PCA (Principal component analysis) followed by the segmentation. The pattern recognition approach is developed with estimation of the features in the skin images. The technique uses quantitative analysis with the coding process to extract the pattern in the skin lesion and utilized for the automated detection system. The modifier dragon optimization model is implemented for the extraction and optimization of features in the skin lesion images. Upon the extracted features classification is performed with the stacked classification model. The DPOSC model effectively detect the lesions in the skin images and linked with the IoT environment for the detection of skin cancer in the early diagnosis. The developed automated integrated with IoT is emerging innovative technique for the detection of cancer in early stages. The accuracy of the improved DPOSCis 0.5% superior to SVM and NN, 2.4% more improved than GA-NN, PSO-NN and DA-NN. The precision of the

关键词： Classification Dragon optimization Features Internet of Things (IoT) Pattern recognition Skin lesions

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Exploring Design Optimisation Techniques of a Radio Telescope Using Fixed Costing Constraints 2

Exploring Design Optimisation Techniques of a Radio Telescop...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Iyer, Aditya Arun Prabhu, Gautham Manuru Gupta, Tanay Deshmukh, Shrey Rivankar, Rushit Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Cost optimization is a common problem encountered in the design of telescopes. This paper comprehensively discusses various radio telescope designs worldwide, focusing on their design and utilities. It contextualizes the Pulsar data challenge and subsequently discusses and develops a mathematical model for designing radio telescopes. The model assumes a fixed-cost budget. This paper expands current ideas of modeling the system using figure-of-merit equations and optimizing them based on a fixed budget to obtain optimal and affordable radio telescope designs. © Published under licence by IOP Publishing Ltd.

关键词： Radio telescopes

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Dimensionality Reduction with DLMNN Technique for Handling Secure Medical data in Healthcare-IoT Model 3

Dimensionality Reduction with DLMNN Technique for Handling S...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Lalithambigai, M. Kalpana, V. Sasi Kumar, A. Uthayakumar, J. Santhosh, J. Mahaveerakannan, R. Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore641042 India Vel Tech University India Institute of Engineering and Technology Srinivas University Department of Cloud Technology and Data Science Surathkal Karnataka State Mukka574146 India Dhanalakshmi Srinivasan College of Engineering Department of Ai and Ds Coimbatore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Security and maintaining the integrity of sensitive patient information have emerged as major obstacles for healthcare service applications in the wake of IoT's meteoric rise in the healthcare industry. The diagnostic information included in medical photographs is of particular importance, and this research presents a hybrid security strategy to protect against such breaches. Chronic diseases such as Heart Disease (HD), cancer, and chronic respiratory sickness are the main causes of death worldwide. Those who have a heart attack suddenly have a very poor chance of surviving. As a result, this paper proposes a heart disease monitoring scheme, wherein a Deep Learning Modified Neural Network (DLMNN) centred on the Internet of Things is used to aid in the diagnosis and treatment of HD. There are three stages to implementing this strategy: First, there is encryption;second, a combination of information gain (IG) and principle component analysis (PCA) to reduce dimensionality;and third, a classification system. To begin, the patient is fitted with a wearable Internet of Things (IoT) sensor device, which simultaneously uploads its sensor data to a remote server. The sensor data is encrypted using a hybrid encryption scheme that combines the Advanced Encryption Standard (AES) and the Rivest, Shamir, and Adleman (RSA) algorithms. A DLMNN classifier is then used to classify the data after it has been decrypted. There are two types of classification outcomes. It provides information on the patient's heart and notifies the doctor in case of abnormalities. Estimated study results demonstrate that DLMNN is an improvement over previously used algorithms for HD diagnosis. The proposed AES-RSA used to support safe data transfer also yields the highest level of security, i.e. 94.32%, and does so in the quickest time for encryption and decryption. © 2023 IEEE.

关键词： Principal component analysis

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Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap Visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

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

作者： Velmurugan, L. Maheswari, R.K. Janani, K. Agalya, K. Poornima, M. Gayathri, K.C. Vit Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore India M.A.M College of Enginnering and Technology Artificial Intelligence and Data Science Tamilnadu Trichy India Manipal institute of Technology Manipal Academy of Higher Education Department of Data science and computer applications Manipal India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India Oasys Institute of Technology Department of Information Technology Tamil Nadu Trichy India Kelambakkam Chennai India

ISBN: (纸本)9798331543617

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet' s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices. © 2024 IEEE.

关键词： Brain mapping

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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management 15

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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

作者： Lina, Jia Banda, Shahab S. Paib, Hao-Ting Rawal, Bharat S. International Graduate School of Artificial Intelligence Yunlin County Douliu City Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science and Digital Technologies University GgramblingLA United States

ISBN: (纸本)9798350370249

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL's reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent's decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market. ©2024 IEEE.

关键词： Deep reinforcement learning

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