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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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Segmentation Approaches of Parasite Eggs in Microscopic Images: A Survey

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SN computer science 2024年第4期5卷 401页

作者： Ray, Kaushik Saharia, Sarat Sarma, Nityananda Department of Computer Science and Engineering Tezpur University Assam Napam Tezpur 784028 India

The image segmentation is an important stage in automatic detection, identification and quantification of various type of parasite eggs in microscopic images. Different types of parasites, such as protozoa, helminths (worms) and ectoparasites and their eggs, can be observed under a microscope in human and animal faeces, blood or urine samples. Microscopic images often contain multiple kinds of parasite eggs as well as other sample impurities or debris. The size, colour, and texture of parasite eggs may also vary in the images depending on the sample collection and image acquisition process, making the segmentation process challenging. Over the years, various segmentation methods such as thresholding, edge detection, wathershed, etc. have been utilised by the researchers in this field. However, to the best of our knowledge, no survey works on the segmentation approaches for parasite eggs in various types of microscopic images have been reported yet. As a result, we explored a number of research reports and prepared a detail review of various segmentation techniques employed in previous works on automatic detection and identification of parasite eggs in different types of microscopic images. The aim of the study is to assist future researchers in identify the suitable strategies for effectively segmenting parasite egg images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Detection Fecal Identification Microscopic image Non-egg Parasite egg Segmentation

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EUR/USD Forex Market Prices Prediction Using MLP 16

EUR/USD Forex Market Prices Prediction Using MLP

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16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Diab, Raed Nohra, Alain Atwi, Ahmad Haidar, Gaby Abou Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Department of Computer and Communications Engineering United States

ISBN: (纸本)9798350333510

Neural networks is one of the most developed concepts in artificial intelligence owing to their ability to solve complex computational tasks, and its efficiency in finding solutions. There is a wide range of applications that adopt this technique, one of which is in the financial investment domain. This paper presents an approach to predict foreign exchange market prices using a neural network. It considers MLP techniques using sequential models with dense layer methods. Tests were conducted on one of the forex major pair data sets, EURUSD. The work is divided into 5 parts: prediction for the next minute, hour, day, week, and month resulting in 5 different models. Accordingly, a best-case accuracy of 99% for MLP algorithms is demonstrated. © 2023 IEEE.

关键词： Investments

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Supporting lifelong learning: Comparing an in-situ and a post hoc approach to support everyday question-asking

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Educational Technology and Society 2024年第2期27卷 256-269页

作者： Rani, Neha Chu, Sharon L. Williamson, Yvette G. Department of Computer and Information Science and Engineering University of Florida Gainesville FL United States

Much of lifelong learning is driven by our curiosity to ask ourselves questions about the things around us in everyday life. Unfortunately, we often fail to pursue these questions to acquire new knowledge, resulting in missed opportunities for lifelong learning. We investigated two approaches to technological support for lifelong learning from question-asking in everyday life: an in-situ approach – reflecting and learning in the situatedness of the moment when a question is asked, and a post hoc approach – self-reflecting and learning after the question-asking moment when one is available to reflect. The in-situ approach may enable people to tap into their embodied experience to gain understanding, while a post hoc approach may allow people to allocate greater cognitive and material resources to explore and understand. We implemented two systems embodying each of the two approaches. A study was conducted to compare the use of the two learning support systems in an everyday virtual environment. Results showed that the post hoc approach produces more curiosity questions and reflection than the in-situ approach. We discuss the implications of our results for the design of systems to support lifelong learning. © (2023), (International Forum of Educational Technology and Society). All Rights Reserved.

关键词： Context Lifelong learning Question-asking Reflection Situated Learning

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Diagnosis of compressive strength of cement based material using deep learning technique

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Materials Today: Proceedings 2022年 69卷 1402-1407页

作者： Rajendran, Sasikumar Matheshwaran, P. Deepa, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Although the widespread usage of mortars in construction over the last few decades, there is no published method for estimating their strength based on the mix parameters of the mortars themselves. This is owing to the fact that the mortar's compressive forte is very nonlinear in response to the mixture's components. Researchers in this research examined how artificial intelligence might be used to forecast compressive strength for cement-based mortars. In previous studies, artificial networks have been used to map the mechanical possessions of cement-based materials. Overfitting and the difficulty to explain decisions are some of the key drawbacks of this machine learning technique, which have been documented in the literature. This study therefore analyses if deep learning may be used to make predictions. Proposed methodology may accurately estimate mortar's strength when compared to experimental results, according to the results obtained from this study. As expected, machine learning predicted mortar compressive strength more accurately than provided model, however it was discovered during verification. To solve the problem of mortar compressive strength, the built deep learning model has been presented as the best predicting procedure. © 2022

关键词： Mortar

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Fatima, N. Sabiyath Singh, Ramendra Swathi, Akuthota Choudhary, Amar Ahmed, Azhar Sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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Smart Agriculture: Enhancing Crop and Weed Detection Using MobileNetV2 for Autonomous Farming Systems 3

Smart Agriculture: Enhancing Crop and Weed Detection Using M...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Uma, P. Pranav, S. Ma, Sutharson Vignesh, V. Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331523893

Agriculture is vital to food security and economic stability, but weeds reduce crop yields by competing for sunlight, water, and nutrients. Manual weeding and herbicides are often ineffective and environmentally harmful. This study presents an autonomous weeding robot that utilizes MobileNetV2 for real-time crop and weed detection, leveraging its lightweight architecture for efficient performance on devices like the NVIDIA Jetson Nano. The system achieves 85% accuracy, a 0.72 IoU score, and 25 FPS processing speed, with field tests showing detection accuracy of up to 90% in ideal conditions. By reducing herbicide use and improving resource efficiency, this approach supports sustainable agriculture, with future advancements such as multispectral imaging and pest detection further enhancing its role in precision agriculture. © 2025 IEEE.

关键词： Weed control

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Cotton Percentage Prediction from Fabric Images Using Transfer Learning 26

Cotton Percentage Prediction from Fabric Images Using Transf...

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

作者： Islam, Niful Sutradhar, Debopom Shatabda, Swakkhar Rahman, Chowdhury Mofizur United International University Department of Computer Science and Engineering Bangladesh Bangladesh BRAC University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

The textile industry is a prominent and influential sector worldwide, contributing significantly to the economy. This industry has long relied on manual inspections for determining the percentage of cotton in fabric samples, a critical factor for quality control and compliance. However, this conventional approach takes a significant amount of time and is subject to human error. A quicker and more accurate alternative is required due to the rising demand for objective quality assurance and speedier manufacturing cycles. In this article, we propose a computer vision based solution to this challenge. We leverage convolutional neural networks (CNNs), to analyze microscopic images of fabrics and precisely calculate the percentage of cotton. To prove our model's effectiveness, we also made a comparative analysis with several CNN models. The proposed solution achieves a mean absolute error of 3.56 and a mean squared error of 48.78 in detecting the percentage of cotton from microscopic images. The proposed automated method has the potential to affect the industry by removing the subjectivity of manual inspection and saving critical time and money leading to the goal of sustainability. © 2023 IEEE.

关键词： Convolutional Neural Networks Cotton Percentage Fabric Image Regression Transfer Learning VGG

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ICE-VDOP: an integrated clustering and ensemble machine learning methods for an enhanced vector-borne disease outbreak prediction using climatic variables

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2077-2088页

作者： Jayabalan, Divya Elango, Sivasankar Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli 620015 India

Dengue is prone to cause frequent disease outbreaks. An early outbreak prediction based on the climatic variables is essential to control the disease’s spread and avoid possible outbreaks. Existing non-seasonal outbreak classification uses a partition-based clustering method for outlier removal;however, a standard outlier detection with improved prediction performance is needed to detect outbreaks. We develop ICE-VDOP model, an Integrated Clustering and Ensemble machine-learning model for Vector-borne Disease Outbreak Prediction to classify outbreaks based on non-seasonal climatic characteristics for Bangkok and Bangladesh datasets. The work begins with identifying the clustering approach that generates a refined dataset ensuring the data points’ inherent structure is preserved and does not alter the association of climatic variables and Dengue Incidence. The partitioning-based clustering methods modify the direction of association between climatic variables and Dengue Incidence after outlier removal as it removes the essential data points. The comparative results shows that BIRCH (balanced iterative reducing and clustering hierarchy), a hierarchical clustering technique for outlier removal captures finer non-seasonal climate variables (temperature, rainfall and relative humidity) and gradient boosting (GB) achieves a prediction accuracy of 97% for Bangkok and 98% for the Bangladesh datasets. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Binary classification BIRCH Dengue Gradient boosting Outlier detection Vector-borne disease outbreak prediction

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Machine Learning Algorithms and Internet of Things for Healthcare: A Survey

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IEEE Internet of Things Magazine 2022年第2期5卷 133-139页

作者： Abualsaud, Khalid Computer Science and Engineering Department Qatar University Qatar

The Internet of Things (IoT) has recently attracted a lot of attention in numerous applications. In particular, IoT technologies have shown significant improvements and cost reductions when used in healthcare systems. However, there are still many challenges in applying IoT technologies to healthcare systems, such as processing and analyzing huge amounts of health data. This data needs to be transferred to the cloud, securely stored, and properly analyzed for medical decisions. Furthermore, it is important to automate these decisions using machine learning (ML). In this article, we review the ML schemes that provide intelligence integrated with IoT used in healthcare systems. Afterward, we discuss existing types of ML algorithms and provide a number of challenges for ML issues in IoT-based healthcare applications. Our discussions are based on investigating current solutions with future research directions. © 2018 IEEE.

关键词： Internet of things

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