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A Classification of Face Recognition System Using Faster Region-Based Convolutional Neural Network 2

A Classification of Face Recognition System Using Faster Reg...

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

作者： Maheswari, M. Alabdeli, Haider Aruna, T.M. Reddy, R. Archana Revathi, R. School of Computing Srm Institue of Science and Technology Department of Computational Intelligence Kattankulathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India School of Computer Science and Engineering Sr University Warangal India Karpagam Academy of Higher Education Department of Computer Science Echanari Coimbatore India

ISBN: (纸本)9798350373110

In recent times, face recognition is a kind of biometric authentication which is widely used for security purpose in many areas like image processing, pattern recognition. However, recognizing the human face was very difficult as the poses in the input images vary, various conditions of light, brightness, expressions, ages. To overcome from it, a proposed method was introduced in this paper known as Faster Region-based Convolutional Neural Network (RCNN). The main advantage of using the proposed method is it will minimize the training process time as it consisting of two fully connected layers namely classifier and regression layer. Initially, the proposed method was implemented on ORL database consisting of 40 classes, then the images in the database undergoes image resizing and enhancing in the preprocessing stage. Where, Image resizing id done to reduce images size so that every image is same in size that will minimize the processing time and image enhancing was done to adjust the brightness of the image. In further, for extracting the features from preprocessed images, CNN is used, so that extracted features are given to the classification stage to detect the face of human or not. In detecting the faces, proposed Faster RCNN achieved high accuracy of 98.64% when compared with other existing methods like IFC, EFRA and VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Modeling and Analysis of Campus Placements Using machine learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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Diabetes Prediction Using machine learning: A Detailed Insight 1

引用

1st International Conference on Computational sciences and Sustainable Technologies, ICCSST 2023

作者： Thakur, Gour Sundar Mitra Dutta, Subhayu Das, Bratajit Department of Artificial Intelligence and Machine Learning Dr. B. C. Roy Engineering College West Bengal Durgapur India Department of Computer Science and Engineering Dr. B. C. Roy Engineering College West Bengal Durgapur India

ISBN: (数字)9783031509933

ISBN: (纸本)9783031509926

Diabetes often referred to as Diabetes mellitus is a general, continuing and deadly syndrome occurring all over the world. It is characterized by hyperglycemia which occurs due to abnormal insulin secretion which results in an irregular rise of glucose level in the blood. It is affecting numerous people all over the world. Diabetes remained untreated over a long period of time, may include complications like premature heart disease and stroke, blindness, limb amputations and kidney failure, making early detection of diabetes mellitus important. Now a days in healthcare, machine learning is used to draw insights from large medical data sets to improve the quality of patient care, improve patient outcomes, enhance operational efficiency and accelerate medical research. In this paper, we have applied different ML algorithms like Logistic Regression, Gaussian Naive Bayes, K-nearest neighbors, Support Vector machine, Decision Tree, Random Forest, Gradient Boost, AdaBoost and Multi Layered Perceptron using artificial Neural Network on reduced PIMA Indian Diabetes dataset and provided a detailed performance comparison of the algorithms. From this article readers are expected to gain a detailed insight of different symptoms of diabetes along with their applicability in different ML algorithms for diabetes onset prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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One-Shot Vision, AI for Effortless Identification of Individuals with and Without Glasses 5

One-Shot Vision, AI for Effortless Identification of Individ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Tejesh, Gangadhar Karchi, Rashmi Hatture, Sanjeevakumar Sankanagoudra, Asharani Saini, Rashmi Nagarjuna College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India Nagarjuna College of Engineering and Technology Department of Information Science and Engineering Karnataka Bengaluru India G. B. Pant Institute of Engineering and Technology Department of Computer Science Uttarakhand Pauri Garhwal246194 India

ISBN: (纸本)9798350376685

Facial recognition is a challenging task, especially in the wild where there exist a substantial amount of variation in lighting, pose, and expression. One-shot knowledge acquisition is a form of machine learning that allows a model to learn to recognize a new class Upon observing just a single instance of that class. the proposed work of a one-shot learning strategy for facial recognition system that is specifically designed to detect persons with or without spectacles. The proposed work approach has the capability to achieve high accuracy on a limited dataset of labeled images, and possesses the capacity to adjust to unfamiliar images that were not encountered in prior training stages. In this work, One-shot learning aims to recognize individuals based on a limited number of examples with and without specs, making it suitable for scenarios where acquiring a large dataset is challenging. © 2024 IEEE.

关键词： Zero-shot learning

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An Efficient Proposal for Deep learning-Based Diabetes Prediction 2

An Efficient Proposal for Deep Learning-Based Diabetes Predi...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Baswaraj, D. Raju, Ch V V Narasimha Reddy, Pundru Chandra Shaker Kiran, Ajmeera Shaik, Mohammad Khaja Kumar, D Anil Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad India Marri Laxman Reddy Institute of Technology and Management Department Information Technology Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India St. Ann's College of Engineering and Technology Artificial Intelligence and Machine Learning Andhra Pradesh Chirala India Pace Institute of Technology and Sciences Electronics and Communication Engineering Andhra Pradesh Ongole India

ISBN: (纸本)9798350372892

As a result of its exponential growth in recent years, diabetes now poses a serious danger on a worldwide scale. As of right now, diabetes is one of the foremost causes of death worldwide. Failure to treat diabetes increases the risk of death from cardiovascular disease, cerebrovascular accident, brain stroke, and a host of other complications. In order to minimize further complications, it is crucial to discover diabetes early so that action can be taken to stop the condition from progressing. Predicting diabetes in its early stages may be possible with the use of cutting-edge machine learning techniques that uncover previously unseen patterns. Using the PIMA dataset and a variety of machine learning algorithms, this study lays forth an approach for diabetes prediction. All four of these functional classifiers - Deep learning (DL), artificial Neural Network (ANN), and Naive Bayes (NB) - achieve accuracy rates between 90% and 98%. With a 98.07% accuracy rate on the PIMA dataset, DL outperforms the other three methods when it comes to diabetes onset. In light of this, the suggested system offers healthcare officials a useful tool for prognosis. Incorporating omics data for illness onset prediction considerably improves the accuracy of the DL technique. © 2024 IEEE.

关键词： Adversarial machine learning

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Yashas D M Shivani Kashyap Charan PR Hemanth Kumar GP Abhishek BK Deekshith C Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning BNM Institute of Technology Bengaluru Dept of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field.

关键词： Deep learning Dermatology Medical services Transforms Chatbots Skin Real-time systems Convolutional neural networks artificial intelligence Diseases

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MediGlove: Integrating IoT and machine learning for Personalized Remote Health Care 2

MediGlove: Integrating IoT and Machine Learning for Personal...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Nanda, K.V. Keerthan Kumar, T.G. Achyutha Prasad, N. Raviprakash, M.L. Anand Babu, J. Naveen Kumar, M.R. Sri Siddhartha Institute Of Technology Master Of Computer Applications Tumkur India Siddaganga Institute Of Technology Information Science And Engineering Tumkur India East West Institute Of Technology Computer Science And Engineering Bangalore India Kalpataru Institute Of Technology Artificial Intelligence And Machine Learning Tiptur India Malnad College Of Engineering Information Science And Engineering Hassan India Vidyavardhaka College Of Engineering Information Science And Engineering Mysore India

ISBN: (纸本)9798350381665

Mobile operating systems have advanced significantly in recent decades, with Android emerging as a widely used open-source platform. Simultaneously, the Internet of Things (IoT) has transformed computer equipment by enabling seamless data transfer over networks without human involvement. This study makes use of intelligent gloves equipped with various sensors, as well as IoT and Android applications, to improve healthcare. In this work, we introduce a pioneering framework that combines IoT and Android technology to revolutionize remote health monitoring called IoT-enabled Smart Gloves for Remote Health Monitoring using an Android-based solution approach (MediGlove). MediGlove uses intelligent smart gloves with robust sensors to continuously collect crucial health data. The solution works with IoT devices to send data quickly to a cloud-based server. The cloud server provides real-time health metric analysis with great precision by utilizing cutting-edge algorithms and machine learning techniques. A simple Android software acts as a dynamic health dashboard, allowing users to track their health in real-time. The framework generates real-time alerts for problematic health markers, allowing prompt medical action. Extensive testing validates the system's efficacy and dependability in health monitoring and the seamless integration of smart gloves with Android applications. © 2024 IEEE.

关键词： Internet of things

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ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction 4

ML for Sustainable Solutions: Applications in Renewable Ener...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Awachat, Arya Dube, Ananya Chaudhri, Shivnath Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve efficiency within solar and wind energy systems, smart grids, and energy storage technologies is revealed by large data sets and predictive analytics. As ML improves the forecasting of energy, minimizes waste, and allows for better grid integration, its contribution to cutting carbon emissions significantly is immense. In addition, it facilitates climate adaptation by accurately predicting extreme weather occurrences and climate variability. Challenges facing the approach include data paucity, computational cost, and integration with older systems. Interdisciplinary collaboration and newer ML models tackle these challenges well. The study recognizes the significant role of machine learning in creating a sustainable environment and notes its ability to actually reconcile environmental, economic, and social objectives toward a robust and greener future. © 2025 IEEE.

关键词： Clean energy

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Optimal Band Selection and Scale based Feature Selection for Hyper Spectral Image Classification using Hybrid Neural Network 3

Optimal Band Selection and Scale based Feature Selection for...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Jeenath Shafana, N. Jayan, K.T. Divagar Iyyappan, R. Srm Institute of Science and Technology Ramapuram Campus Dept of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Ramapuram Campus Dept of Comp. Sci. and Eng. with Specialization in Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The use of hyper spectral remote sensing to categorize images has been a popular study topic. Non-linear features and high dimensionality are common in Hyper Spectral Image classifications. Band selection can be used to cut computation costs and speed up knowledge discovery. In hyperspectral photographs, mixed pixels often include some ambiguity. This paper suggests a new band selection procedure method to address these issues. Band selection will be a useful tool for lowering the size of hyperspectral data while also assisting us in overcoming dimensionality issues. To automate hyperspectral picture analysis, this article uses a hybrid model. For ground/landcover classification using hyperspectral pictures, this suggested approach employs strategic and competitive theory models. In a classifier-ensemble system, there are also game theory application models for hyperspectral band grouping and pixel classification. © 2022 IEEE.

关键词： Image classification

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Hybrid Fuzzy Assisted RNN Algorithm for Diabetic Retinopathy Identification

Hybrid Fuzzy Assisted RNN Algorithm for Diabetic Retinopathy...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Sivaprakash, P. Gifta Jerith, G. Packiaraj, H. Manikandan, R.A. Thanish Kumar, T. Ashok, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Malla Reddy University School of Engineering Department of Artificial Intelligence & Machine Learning Hyderabad India

ISBN: (纸本)9798350393378

The most common cause of blindness in diabetics is diabetes retinopathy. The importance of early detection in preventing the DR cannot be overstated. However, because eye checkups are so expensive, many people let DR worsen and eventually lead to blindness. To identify persons with DR, the current study employs a color fundus image and a Fuzzy RNN algorithm. Although the purpose of this study is to save money, it could be a game changer for people with DR who cannot afford a medical diagnosis. Prior processing methods include green band extraction, histogram equalization, filtration, optic disc removal with organizational components on architecturally procedures, and illumination changes. The data acquired by GLCM contains contrast, correlation, energy, and homogeneity, and it is utilised to extract characteristics from the preprocessing results. This paper describes a threshold segmentation-based DR detection system. As a result of this study, a fuzzy-RNN methodology for identifying diabetic retinopathy has been developed. Threshold-based categorization is used to determine the diabetic zone. GLCM (Grey Level Co-occurrence Matrix) is the feature extraction technique applied in this case. These characteristics are utilised for categorization. For classification, an RNN classifier is utilised. To simulate the image processing using Phython in diabetic retinopathy is used to validate the final outcome. © 2023 IEEE.

关键词： Recurrent neural networks

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