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A Unified Deep Learning Approach for Integrating Retinal Image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Fabrication of an IoT based Air Purifier using HEPA Filter 11

Design and Fabrication of an IoT based Air Purifier using HE...

引用

11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks, IEMECON 2023

作者： Choudhary, Arpita Saini, Lakhan Ahmad, Afaq Banerjee, Hriday Gazi, Firoj University of Engineering and Management Jaipur Dept. of Computer Science Jaipur India UEM Jaipur Dept. of Computer Science Jaipur India University of Alberta Dept. of Computer Science Edmonton Canada

ISBN: (纸本)9781665475129

Air pollution has become one of the major problems for human health across the globe. Indoor air pollution poses more risk than outdoor air pollution, since the human body is more exposed to the inside. It refers to the contamination of indoor air that causes harmful health problems. To purify the polluted air, the air purifier is a main necessity. In this Internet of Things (IoT) project, we have created an air purifier that purifies the indoor air by passing it through the High Efficiency Particulate Air (HEPA) filter and Ultraviolet light. The microcontroller used is the NodeMCU based on the ESP8266 WiFi enabled chip. Two MQ135 air quality sensors used to monitor the change and calculate the efficiency of the purified air. IoT customization eases the manual work of changing the purifier manually every time by providing a platform that operates on the mobile phone via Wi-Fi where the user can operate it accordingly. It provides an equivalent grade of services at a very moderate cost that is affordable even for the average person and can provide real time analytics. The proposed system is capable for providing hands-on practical operations, which enables more exciting possibilities in IoT based automation systems. © 2023 IEEE.

关键词： Internet of things

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Automatic Story Text Generation Using Recurrent Neural Network Algorithm 13

Automatic Story Text Generation Using Recurrent Neural Netwo...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Damayanti, Shalsahbilla Nazhiifah Prasetiadi, Agi Dewi, Atika Ratna Telkom University Dept. of Informatics Purwokerto Indonesia Telkom University Dept. of Data Science Purwokerto Indonesia

ISBN: (纸本)9798350368086

In the process of writing stories it takes time to bestow feelings and pour imagination into stories, making the writers run out of ideas with stories to be written which makes the writing process inhibiting. Previous research has demonstrated that algorithms such as Natural Language Generation (NLG), Generative Adversarial Networks (GAN), Generate and Rank Methodology, and Recurrent Neural Networks can generate sentences based on input words. However, the datasets used and the results obtained have been limited to the English language. Therefore, this research will focus on developing a model for generating novel text using the Recurrent Neural Network algorithm, specifically with GRU and LSTM architectures, and employing a dataset in Indonesian. This research aims to implement deep learning in creating automatic story text based on word input using RNN with GRU and LSTM architectures. There are several stages of research, namely data collection, data preprocessing, modeling, data testing and evaluation of relevant levels between words. data taken from the Z-Library site with a character length of 13.535.514, the comparison of train and testing data used is 8:2. The results of the GRU architecture training have the best model precision with a bias loss of 0.7371. Based on the calculation of the average relevant word and standard deviation, the output text generated by the LSTM architectural model obtained values of 3.9 and 1.7288 respectively. © 2024 IEEE.

关键词： Generative adversarial networks

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Integrating Explainable AI with Infrared Imaging and Deep Learning for Breast Cancer Detection 21

Integrating Explainable AI with Infrared Imaging and Deep Le...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Kaushik, R.K. Sivaselvan, B.S. Kamakoti, V.K. IIITDM Kancheepuram Dept. of Computer Science Chennai India IIT Madras Dept. of Computer Science Chennai India

ISBN: (纸本)9798350358230

The interplay between medical imaging and artificial intelligence has set the stage for groundbreaking advancements in early disease detection. In the critical domain of breast cancer diagnosis, accurate, swift, and interpretable models are undeniably paramount. This study presents a pioneering approach to breast cancer detection using infrared breast imagery underpinned by the principles of explainable AI. Our methodology's core lies in a novel Gaussian pyramid-driven denoising autoencoder meticulously tailored for infrared breast images. This sophisticated denoising technique enhances the quality of input infrared breast images. It paves the way for more accurate feature extraction, a cornerstone in the diagnostic process. Capitalizing on this refined input, our research introduces an ensemble classifier, blending the deep learning capabilities of DenseNet201 with the feature-rich outputs of the auto-encoder. This synergistic amalgamation sets a new benchmark in precision for breast cancer detection in infrared images. However, accuracy without understanding remains an incomplete victory. Addressing this, our study integrates an attention-guided Grad-CAM mechanism, shedding light on the model's decision-making process. This tool accentuates pivotal regions in imagery through saliency-driven heatmaps, offering clinicians an intuitive window into the AI's verdict. Our research not only pushes the boundaries of precision in breast cancer detection but also champions the cause of transparency and understanding in AI-driven diagnostics, ensuring that technology and human expertise move forward hand in hand. © 2023 IEEE.

关键词： Image enhancement

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Deep Skeleton Modeling and Hybrid Hand-crafted Cues over Physical Exercises 3

Deep Skeleton Modeling and Hybrid Hand-crafted Cues over Phy...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Akhter, Israr Javeed, Madiha Jalal, Ahmad Bahria University Dept. of Computer Science Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

Human activities have always been complex and most important concern for researchers especially when it comes to physical exercises. Multiple methods have been proposed for physical exercise recognition using different sensors where the conventional approaches focused on either videos or motion-based sensors. Whereas, the combination of both types of data can improve the physical exercise recognition particularly for complex motion patterns. For that reason, a hybrid handcrafted cues-based method has been proposed in this paper. data has been collected from the multi-modality-based datasets that are publicly available. Next, three different filters have been used to sift the noise from multiple sensors-based data. Then, an overlapping windowing technique along with human silhouette extraction has been utilized to pre-process the filtered data. Further, the hybrid hand-crafted cues have been extracted using linear prediction cepstral coefficients, Gaussian markov random field, and saliency maps. Finally, the cues have been reduced using multi-layer sequential forward selection methodology and the physical exercise activities have been classified using a deep belief network. © 2023 IEEE.

关键词： Deep learning

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application 25

Investigating Principle Component Analysis Impact on the Per...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Almatarneh, Sattam Bulkrock, Olla Alazaidah, Raed Almahasneh, Hossam Samara, Ghassan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates Zarqa University Data Science & Artificial Intelligence Dept. Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798331540012

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion. © 2024 IEEE.

关键词： Dimensionality reduction

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Simplifying Digital Streaming: An Innovative Cross-Platform Streaming Solution 15

Simplifying Digital Streaming: An Innovative Cross-Platform ...

引用

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

作者： Muduli, Debendra Sahoo, Saroj Kumar Mishra, Jayanta Kumar Pradhan, Debasish Sharma, Santosh Kumar C.V Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Centurion University of Technology and Management Dept. of Computer Science Engineering India GITA Autonomous College Dept. of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

Our research aims to investigate how reinforcement learning methods can be applied to develop a movie recommendation system. We frame the issue of interactive recommendation as a contextual multi-armed bandit, where the system learns user preferences by suggesting new movies and receiving feedback. Through reinforcement learning, we address challenges such as balancing exploitation and exploration and tackling the coldstart problem. Additionally, we incorporate the novelty factor of movies into our model. We evaluate both content-based and collaborative filtering approaches which achieved accuracy 72.33(%), both of which demonstrate effective recommendation outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Implementing HR Management System with an AI Powered ChatBot 3

Implementing HR Management System with an AI Powered ChatBot

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susitra, D. Pratheesh, G. Karthiga, J.D. Dhanalakshmi, K. Shamreen Ahamed, B. Sylvia Grace, J. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user-friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management. © 2025 IEEE.

关键词： AI powered chatbot for Small-scale startups using cache memory attendance management face recognition HR Management System leave management payroll management

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Dimensional Hidden Layers and their Impact on Neural Network Performance 4

Dimensional Hidden Layers and their Impact on Neural Network...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Vijay Kiran, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350354218

Increasing the dimensionality of the ANN model architecture from 1D to 2D, 3D and Investigating the impact of a novel approach on this ANN model by replacing the neuron in a hidden layer with another hidden layer containing a neuron. 1D contains 3 hidden layers with 64 neurons each,2D also contains 3 main-hidden layers but each hidden layer contains 2 more sub-hidden layers inside with 32 neurons, similarly in 3D there are 3 main-hidden layers inside each there are 2 more sub-hidden layers inside those hidden layers there are 2 more inner hidden layers containing 16 neurons. One of the approach is with making an dense connection and the other is to go sequential 2D model has performed 10% better than the 1D whereas the 3d model has an better performance of 11-12% in an dense connection, In sequential 2D model has performed 15% better than the 1D whereas the 3d model achieved an accuracy similarly to that of the 1D model but the visualization show's that the model has used only few neuron to give the same results. This help in explore more complex relationships and achieve competitive accuracy despite higher model complexity. All models are trained under identical conditions, highlighting the potential of increased dimensionality to optimize model performance efficiently. © 2024 IEEE.

关键词： Neurons

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Cooperative Thinking: Analyzing a new framework for software engineering education (Extended abstract) 35

Cooperative Thinking: Analyzing a new framework for software...

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35th IEEE International Conference on Software Engineering Education and Training, CSEE and T 2023

作者： Ciancarini, Paolo Russo, Daniel Missiroli, Marcello University of Bologna Dept. of Computer Science Bologna Italy University of Aalborg Dept. of Computer Science Aalborg Denmark

ISBN: (纸本)9798350322026

关键词： Software engineering

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