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Deep Vision: Iris Segmentation for Diagnostic Insights in Iridology 1

Deep Vision: Iris Segmentation for Diagnostic Insights in Ir...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Avhad, Vedika Bakal, Jagdish W. Dept. of Computer Engineering Maharashtra India

ISBN: (纸本)9798350353648

Ocular diseases, particularly diabetic retinopathy, are increasingly prevalent and necessitate precise detection methods. The proposed method, 'Deep Vision: Iris Segmentation for Diagnostic Insights in Iridology,' leverages deep learning for enhanced prediction of diabetes-related ocular conditions. Data from the diabetes-iridology dataset is pre-processed with Contrast Limited Adaptive Histogram Equalization (CLAHE) [1]. The Attenuation-based U-Net model [2] ensures precise iris segmentation. For improved classification, DenseNet [3], EfficientNet [4], and Capsule Networks (CapsNet) [5] are evaluated. The Python-based implementation achieves an impressive accuracy rate of 95.87% [6], demonstrating a substantial improvement over conventional method. This approach not only advances diagnostic performance but also sets a new benchmark for precision in ocular disease prediction. © 2024 IEEE.

关键词： accuracy rate Attenuation-based U-Net benchmark Capsule Networks (CapsNet) Contrast Limited Adaptive Histogram Equalization (CLAHE) deep learning DenseNet diabetic retinopathy diagnostic performance EfficientNet iris segmentation Ocular diseases Python

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Classification Flower Images Based on Deep Learning and Machine Learning 8

Classification Flower Images Based on Deep Learning and Mach...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Mustfa, Asia Kamal Abdulateef, Salwa Khalid Hameed, Qabas Amer Ahmed, Mohamed Aktham Al-Qaysi, Zaydoon Computer Science and Mathematics College Tikrit University Dept. Computer Science Tikrit Iraq

ISBN: (纸本)9798350354423

Image classification has grown increasingly popular due to the growing significance of machine learning and deep learning. Flower images may sometimes exhibit resemblances in terms of hue, form, and visual characteristics. The problem lies in the classification of flowers. This work employs a hybrid approach that integrates deep learning and machine learning techniques to classify 17 discrete flower species. In order to do this, we utilised the ResNet_50, PCA, and SVM architecture to classify several species from the 'Oxford-17' dataset. With this goal in mind, we have made efforts to improve our model in order to get more accuracy compared to similar methods. Prior to inputting our images into our pretrained model, we resized them, and subsequently fine-tuned the model. The dataset was partitioned into two distinct sets: a training set and a testing set. We attained a precision rate of 95.58% while utilising the 'Oxford-17' dataset. Our approach outperformed previous machine learning and deep learning-based methods on this dataset. © 2024 IEEE.

关键词： Adversarial machine learning

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Afroj, Maharin Jahan, Israt Islam, Md Polash Saifur Rahman, Md. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Stockholm University Dept. of Computer and Systems Sciences Stockholm Sweden

ISBN: (纸本)9798331529765

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN's promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources. © 2024 IEEE.

关键词： Fruits

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State of the art: Security Testing of Machine Learning Development Systems 14

State of the art: Security Testing of Machine Learning Devel...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Das, Saikat Krishnamurthy, Bhargavi Das, Raktim Ranjan Shiva, Sajjan G Utah Valley University Dept. of Computer Science Orem United States Siddaganga Institute of Technology Dept. of Computer Science Karnataka Tumakuru India Stamford University Bangladesh Dept. of Computer Science Dhaka Bangladesh University of Memphis Dept. of Computer Science Memphis United States

ISBN: (纸本)9798350360134

In recent days, machine learning (ML) systems have become integral to nearly all mainstream applications. Understanding the underlying logic that contributes to the desired behavior in ML systems can be challenging. Humans play a crucial role in providing the necessary data samples for training ML models to ensure accurate predictions. The complexity of ML testing arises from various factors such as data dependency, dynamic model behavior, the absence of a test oracle, a vast input space, and the lack of a specific testing life cycle tailored for ML. Testing ML systems is not a straightforward process, as it not only involves verifying the code and their corresponding data but also is further complicated by the dynamic nature of these systems. A typical ML model undergoes seven stages, including eliciting business needs/requirements, gathering data, selecting a model, training the model, testing the model, deploying the model, and monitoring the model. Throughout the ML Development Life Cycle (MLDLC), each stage introduces various security risks. This paper explores the current state of the literature on security attacks and defense approaches concerning data, model, and prediction output of ML models. The paper also addresses potential security attacks at each stage of the MLDLC and the corresponding security measures to mitigate those attacks. © 2024 IEEE.

关键词： Life cycle

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FOOTBALL DATA ANALYSIS: THE PREDICTIVE POWER OF EXPECTED GOALS 25

FOOTBALL DATA ANALYSIS: THE PREDICTIVE POWER OF EXPECTED GOA...

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25th International Conference on Intelligent Games and Simulation, GAME-ON 2024

作者： Roccetti, Marco Berveglieri, Filippo Cappiello, Giuseppe Dept. of Computer Science and Engineering University of Bologna Italy Dept. of Management University of Bologna Italy

ISBN: (纸本)9789492859327

Major football events, such as the World Cup, have popularized the European football (i.e., soccer) beyond its traditional geographical borders, for example with a growing number of Americans who have begun to incorporate European football into their daily sport consumption. Many video game series based on this sport have further contributed to this increasing development. Not only that, but many performance indicators inspired by football computer games have become metrics that are now part of the normal conversation about real football, being used to analyse tactics, teams and players. Among these indicators, of prominent importance is the role of expected goals (xG) that measure the quality of a shot by calculating the likelihood that it will be scored from a particular position during a particular phase of play. Often, someone talks about xG leading to imprecise conclusions and to a distorded point of view on this performance indicator. In this paper, using a non-parametrical Kolmogorov Smirnov hypothesis testing method, we show that there is no statistically significant difference if, in a given football tournament, the final scores are awarded to the participating teams either with real goals or based on xGs, thus demystifying some wrong assumptions about this indicator. Data for this study come from all the matches played during the 2020 UEFA European Football Championship. © 2024 25th International Conference on Intelligent Games and Simulation, GAME-ON 2024. All rights reserved.

关键词： Predictive analytics

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing 26

Movie Genre Prediction Using AI-Generated Data and Natural L...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Tanvir, Sifat Abontee, Ayesha Parpia Rayhan, Md. Abu Rahman, Md. Fuadur Sultana, Tasnim Ahmed, Ajwa Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350359015

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved. © 2023 IEEE.

关键词： Embedding GPT-3.5 Neural Network NLP Text Vectorization

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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...

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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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AnVILMEDFORD: Documenting Cloud-Based Analysis Projects Using the MEDFORD Metadata Language

AnVILMEDFORD: Documenting Cloud-Based Analysis Projects Usin...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Stubbs, Benjamin Shpilker, Polina Sayers, Michael Wallace, Shaun Daniels, Noah M. Couch, Alva Tufts University Department of Computer Science MedfordMA United States University of Rhode Island Dept. of Computer Science and Statistics KingstonRI United States Dept. of Computer Science and Statistics Dept. of Computer Science KingstonRI United States

ISBN: (纸本)9798350362480

This paper introduces AnVILMEDFORD, an R package that aims to enable MEDFORD metadata annotation from within the cloud based AnVIL platform for computational analysis. AnVILMEDFORD helps solve the problem of documentation within the AnVIL environment by providing bidirectional tools that both allow a user to generate MEDFORD documentation from a workspace, and also allow a user to update an AnVIL workspace starting with a MEDFORD meta-data file. AnVILMEDFORD accomplishes this by leveraging the Bioconductor AnVIL package and the AnVIL API to download and process AnVIL metadata, Tree-sitter-medford module along with the AnVIL API to update the workspace in the cloud. An implementation is available at https://***/bjstubbs/AnVILMEDFORD © 2024 IEEE.

关键词： Cloud platforms

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Facial Emotional Recognition Using Convolutional Neural Network 2

Facial Emotional Recognition Using Convolutional Neural Netw...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Raj, Deepak Wassay, Md. Abdul Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798350320923

The face of a person is his identity. Most of his emotions, issues can be derived from his face. Face is the window to the soul which was said by famous French doctor Duchenne de Boulogne. He used several techniques to support his theory like giving shocks of electric impulses to understand how a person reacts to muscular contractions. He also tried to induce some of the expressions of bizarre looking. The ultimate aim is to analyze how much muscles contribute to emotion. He successfully derived lots of human emotions which is hidden. After 200 years this field is still active as it requires more experiments to extract invisible truths of human truths. Lots of Emotion Recognition which is automatic have been seen in the field of Marketing and Advertising. It is also seen in the field of Medical, Law and order...etc. The main fundamental question arrives whether it is good to enter into our personal space. This a question which has lots of dimension. Those who are against this practice is claiming that this is a violation of human rights by which we can use the stored data to harm the society in future. Even though it is a major concern if we can rectify the above the scope in this area is phenomenal. Lots of researchers are still working on this area because of mainly one reason that is its wide application in the area of Medical science. It can be used by a doctor for diagnosing a person with certain psychological disorders, children with Autism, person with Parkinson's disease. It can be widely used to diagnose children with Autism so that particular child can be motivated at early stage leads to his success in future and thus provide a good citizen to the society. In our research we are using several datasets such as FER2013, CK+...etc. These datasets have been given to a model which includes Deep Convolutional Neural Network. Here input image is given to a model using camera. The particular image has to done preprocessing to extract fine features in the model. A

关键词： Emotion Recognition

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Multisport Dynamics Explored Through Advanced Activity Recognition Techniques in Athletic Performance 8

Multisport Dynamics Explored Through Advanced Activity Recog...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. D'Souza, Ria Monterio, Rencita Fernandes, Rinette Veronica Almeida, Riya D. St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

Recognition of human activities has drawn a lot of interest lately in the field of computer vision and machine learning. Group activity recognition is a significant subcategory in which several people participate in a common activity. The primary obstacle in these tasks is learning the relationships between individuals in a scene and understanding their evolution over time. The suggested study offers a new taxonomy to classify state-of-the-art (SOTA) group activity recognition approaches and subcategorizes the current literature. It also critically analyzes these techniques. To understand scenes involving multiple people, models must describe individual actions in context and infer collective activities. Accurately capturing relationships between actors and performing relational reasoning is crucial for comprehending group activities. However, modelling these relationships is challenging due to the limited availability of interaction information, relying only on individual action labels and collective activity labels. Inferring relationships from other aspects is thus essential. Group activity recognition has garnered increased research attention recently due to its importance in video understanding. The difficulties lie not only in recognizing individual actions but also in exploring scene information and collaborative relations among people. This research addresses these challenges by providing a comprehensive review of existing techniques, offering a structured approach to categorize them, and highlighting the importance of relational reasoning in understanding group activities. © 2024 IEEE.

关键词： Active learning

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