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共 9443 条记录，以下是41-50 订阅

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Real-Time Defogging using Dark Channel Prior for Enhanced Visibility 3

Real-Time Defogging using Dark Channel Prior for Enhanced Vi...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Mushtaq, Zohaib Rehman, Obaid Ur Ali, Usman Latif, Muhammad A. Dept. of Electrical Electronics and Computer systems University of Sargodha Sargodha Pakistan Dept. of Computer and Information System Cleveland State University OH44115 United States

ISBN: (纸本)9798331504861

The presence of fog is a notable obstacle for capturing images, particularly in the early morning hours. The diminished sight impedes the capacity to get distinct and comprehensive information from the surroundings. In order to tackle this issue, a rapid and live vision-based dehazing method is suggested. The objective of the technology is to dissipate the fog and enhance visual acuity. It does this by implementing four steps: using an exponential moving average filter to diminish the impact of noise and enhance clarity, preprocessing the filtered frames, detecting fog with the Gaussian blur technique, and applying the dark channel prior approach to achieve optimum contrast and remove fog. Comprehensive testing were carried out on live recordings to showcase the system's ability to significantly improve sight in different foggy situations. This technique demonstrates the superior performance in terms of following parameters like, an accuracy, precision, recall, and F1 score compared to previous investigations. Our methodology achieved the greatest accuracy when compared to state-of-the-art transfer learning convolutional neural networks. ©2024 IEEE.

关键词： Convolutional neural networks

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Augmented Karuta: Interactive Playing Cards on the Floor for Learning Local Culture 7

Augmented Karuta: Interactive Playing Cards on the Floor for...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Umezu, Nobuyuki Bando, Hideki Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798331521578

This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as 'Karuta'. Designed to engage users and stimulate interest in local cultures, history, and traditions, the system utilizes three projectors. Two projectors project 46 'grabbing' cards onto the floor screen of 4.1 x 3.7 (m), while the third displays a corresponding 'reading' card on a wall screen. Each matching pair of cards shares a poem or sentence that begins with one of the Japanese phonograms. During a session, users actively engage by stepping onto the projected 'grabbing' card that matches the 'reading' card displayed on the wall, effectively blending physical movement with cognitive skills to find the correct match. By integrating a two-dimensional LiDAR sensor, the system tracks the user's position on the interactive floor. This tracking is vital for the proposed system to immediately respond to the user's actions, ensuring an immersive and seamless interaction with the digital karutas on the floor. We conducted evaluation experiments at two museums, gathering positive feedback from 57 participants. Future work will focus on quantitatively evaluating the system's impact on learning effectiveness. © 2025 IEEE.

关键词： Tropics

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Contrastive Self-Supervised Learning for Cocoa Disease Classification 10

Contrastive Self-Supervised Learning for Cocoa Disease Class...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Khalid, Usman Kaya, Buket Kaya, Mehmet Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Electronics and Automation Elaziǧ Turkey

ISBN: (纸本)9798350363104

Self-supervised learning has shown significant improvement within several disciplines including the health sectors and areas where acquisition of annotated data is difficult. This study focuses on the application of self-supervised learning in tackling cocoa disease detection and classification challenges by taking cocoa swollen shoot virus disease (CSSVD) and anthracnose. Self-supervised learning has proven to offer resilience against both OOD and adversarial attacks and as an alternative to the cost involved in the annotation process of the vast amount of data. This work leverages a contrastive self-supervised learning pretext model trained on cocoa leaves and pods, a remarkable performance was shown when we fine-tuned and pre-trained on a downstream task. Several works are being done on the subject of self-supervised learning and fully-supervised learning. In order to get the best out of these two techniques, we propose a hybrid approach which will combine both SLL and fully supervised learning. Thus in any deep learning project regardless of the amount of annotated dataset around, a pretext model should be developed from the dataset which can then be fined-tuned using the entire dataset at hand. Three models were developed and the best come in as a result of fine-tuning the learnt features using the entire dataset. After performing the downstream task (classification of cocoa diseases) a validation accuracy of 87%, 72% and 69% for the fine-tune, fully-supervised and pre-trained respectively were obtained. This approach holds promise for enhancing the productivity in crop management, particularly in regions like Ghana and Ivory Coast where their GDP relies on cocoa production © 2024 IEEE.

关键词： Self-supervised learning

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E-Mail Spam Detection Using BERT and LSTM

E-Mail Spam Detection Using BERT and LSTM

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Erkus, Celal Kaya, Buket Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Electronics and Automation Elazig Turkey

ISBN: (纸本)9798350369106

The quick advancement of today's digital world and our daily reliance on the internet have a big impact on our lives. Especially in these days when automation is very easy, fraud and fraudulent activities against users have become quite common. One of the most common examples of these activities is fraud attempts through spam e-mails. Spam e-mails aim to hijack people's information, spread malware and defraud people by playing with their perceptions. Various spam detection methods and algorithms have been developed to prevent these problems. In this study, two models are proposed to detect spam e-mails using natural language processing (NLP) and deep learning methods: LSTM (Long Short Term Memory) and BERT (Bidirectional Encoder Representations from Transformers). As a result of the studies on the Enron e-mail dataset, the BERT model showed 98.93% accuracy and 98.87% f-score performance. The LSTM model showed 97.47% accuracy and f-score performance. With these results, it is seen that the BERT model detects spam with higher accuracy despite the complexity in language structures. © 2024 IEEE.

关键词： Deep learning

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A Deep Learning Model for More Descriptive Image Captioning

A Deep Learning Model for More Descriptive Image Captioning

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Doner, Tunahan Kaya, Buket Firat University Dept. of Computer Engineering Elazig Turkey Firat University Dept. of Electronics and Automation Elazig Turkey

ISBN: (纸本)9798350369106

Describing images with natural and fluent sentences is recognized as a challenging problem in computer vision and natural language processing. The limited number of studies in this area, especially in the Turkish language, and the fact that the existing datasets usually contain generalized descriptions made this research necessary. In this study, a rich dataset in Turkish language was created and this dataset was developed by optimizing both the natural structure of the language and the sentence lengths. An accuracy rate of 36% was achieved in the training process and 31% in the testing process. As a result, the generated dataset aims to contribute to Turkish image recognition models to produce more natural and detailed sentences. © 2024 IEEE.

关键词： Modeling languages

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Enhanced Brain Tumor Segmentation: A Study on the Efficacy of Multi-Model Ensembling Utilizing Diverse Backbones 26

Enhanced Brain Tumor Segmentation: A Study on the Efficacy o...

引用

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

作者： Islam, Md. Hedayatul Hossain, Md. Rakib Akter, Farzana Srizon, Azmain Yakin Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Islam, Md. Minhazul Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Gliomas account for approximately 80% of all malignant brain tumors and are associated with the most unfavourable survival rates amongst all brain tumor types. Segmenting these tumors is integral to evaluating their dimensions and morphology, formulating tailored treatment plans, and estimating patients' life expectancy. Various approaches for brain tumor segmentation have been proposed, yet the model described herein stands out, offering promising results in this vital task. A comprehensive examination of three eminent convolutional neural network (CNN) models, namely Resnext50, Resnet50, and InceptionV3, was conducted, each characterised by its own unique structural framework. The enhancement of the segmentation approach's effectiveness was a focal point, leading to the exploration of ensemble modeling. This approach leverages the collective strengths of the aforementioned models, facilitated by softmax averaging. This collaborative initiative culminated in the creation of the Ensemble model, which outshoned its individual counterparts in performance metrics. Furthermore, the Ensemble model is poised to instigate significant advancements in brain tumor diagnostic procedures. The ensemble methodology is instrumental in refining the precision and automation of brain tumor diagnoses. Deep learning, as evidenced by the research, is a transformative element in medical image analysis, elevating the accuracy and efficiency of brain tumor segmentation. Consequently, this refined approach is set to become an invaluable asset for medical professionals in enhancing the accuracy of diagnoses and the efficacy of treatments for patients afflicted with brain tumors. © 2023 IEEE.

关键词： Backbone Architectures Brain Tumor Segmentation Deep Learning Ensemble Learning Evaluation Metrics Medical Image Analysis MRI Images U-Net

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Leveraging AI-Driven Strategies to Mitigate Employee Turnover in Commercial Industries 26

Leveraging AI-Driven Strategies to Mitigate Employee Turnove...

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

作者： Hossain, Md. Shakib Hossain, Md. Rakib Hasan, S. M. Mahedy Akter, Farzana Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Islam, Hedayatul Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Employee attrition remains a significant obstacle in the corporate sector, adversely influencing organizational coherence and productivity. This article introduces a well-rounded system, meticulously crafted to curtail employee turnover. An extensive analysis employing a diverse set of machine learning models, namely, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Random Forest, CatBoost, and XGBoost, has been performed to construct an efficacious predictive architecture. Despite the widespread adoption of machine learning in forecasting attrition, the interpretational challenges persist, largely attributable to the dependency on feature sets. This research underscores that the Min-Max Scaler empowers the Random Forest classifier to attain an exceptional 88.24% accuracy utilizing a condensed three-feature subset, underscoring the efficacy of feature reduction amidst expansive datasets. The study employs LIME and SHAP frameworks to delve into the interpretability of models, examining the influence of risk variables meticulously. SHAP illuminates both overarching and specific insights, while LIME focuses on dissecting local black-box outputs. This scholarly contribution extends beyond offering a potent AI resolution to the pressing challenge of employee attrition. It accentuates the paramountcy of model interpretability in deriving insightful HR decisions. Furthermore, the investigation accentuates the capability of AIinfused systems to bolster workforce consistency and efficiency in the corporate sphere. It unfolds actionable intelligence regarding the determinants of attrition, marking a significant stride towards a more informed and strategic approach to employee retention and organizational productivity. © 2023 IEEE.

关键词： Employee Turnover LIME Machine Learning Random Forest SHAP

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Predictive Analytics in Agriculture: Unraveling the Determinants of Crop Yield with Machine Learning 26

Predictive Analytics in Agriculture: Unraveling the Determin...

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

作者： Sakib, Shadman Hossain, Md. Rakib Hasan, S. M. Mahedy Akter, Farzana Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Basak, Anindya Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

In the context of Bangladesh, the precision in forecasting crop yields has been enhanced significantly through the integrative approach of amalgamating historical data, environmental variables, and cutting-edge technology. This integrative methodology underscores the fortification of the nation's agricultural domain, integral to both its economic fabric and the populace's sustenance. This study presents an integrative approach leveraging historical data, environmental variables, and advanced technology to enhance crop yield forecasting. Traditional agricultural methods, while valuable, often lack comprehensive understanding of climatic factors, limiting agricultural refinement. Employing an Advanced computer-Aided Decision Support System, this research meticulously analyzes extensive datasets to uncover intricate correlations between historical yields and environmental nuances. In this study, a novel interpretative machine learning model is formulated, emphasizing data pre-processing and optimal feature transformation protocols tailored to diverse model archetypes. The study delves into the influence of distinct variables on predictive acumen and the impact of individual observational values on forecast outcomes. The study's contributions include a transparent machine learning model elucidating key variables affecting crop yield predictions, paving the way for informed agricultural practices. Future endeavors could focus on leveraging real-time data and expanding datasets to further refine predictive models in this crucial domain. © 2023 IEEE.

关键词： Crop Yield Prediction Feature Importance Machine Learning Parameter Optimization

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Development of IoT-based Scale Sensor to Measure AWD System Water Level in Crop Field 3

Development of IoT-based Scale Sensor to Measure AWD System ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Kabir, Md Shahriar Mahmud Niloy, Khalid Hossen, Md Imon Afrose, Sumaiya Hossain Mofazzol, Md. Ismail American International University-Bangladesh Dept. of Electrical & Electronics Engineering Bangladesh Uttara University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The paper introduces an innovative Internet of Things (IoT)-based scale sensor that measures the water level in the alternate wetting and drying (AWD) system and displays data in the mobile app to enable remote monitoring. The AWD system mainly consists of 30 cm of PVC pipe or bamboo with a diameter of 10-15 cm inserted into the paddy field, with a lower 20 cm buried in the soil and tiny holes at the bottom to allow field water to enter. The water level within the pipe indicates the field's water level, especially the underground water level. The AWD method optimizes water usage and reduces methane emissions by watering periodically and drying rice fields instead of maintaining continuous flooding. The current manual measurement process for water level needs to go to crop fields daily with a scale, which is difficult for continuous and remote monitoring. The developed scale sensor has IoT connectivity that removes the difficulties. The scale sensor works based on conductive rain sensors to precisely detect each centimeter rise or fall of water. The sensor consists of two open conductive layers that create a short circuit when the water level touches them by raising, triggering the microcontroller to register a voltage drop. The scale can measure up to 20 cm as it has 20 conductive layers, which fulfill the requirements. It has a 1 cm resolution, and in the test, it shows 95-99% accuracy. So, the sensor can effectively measure the water level in the AWD system, and further research is required to add an automatic irrigation system with the sensor so that the whole process can be controlled remotely. © 2024 IEEE.

关键词： Comparators (optical)

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