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Contrastive Self-Supervised Learning for Cocoa Disease Classification 10

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

引用

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

引用

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

引用

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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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

引用

24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset 2

Implementation of Pruned and Quantized Semantic Segmentation...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts. © 2024 IEEE.

关键词： Semantic Segmentation

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies 1

An Approach of Fog Computing and Edge Computing for Computin...

引用

1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Yadav, Devansh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350365016

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems. © 2024 IEEE.

关键词： Fog computing

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

引用

2024 International Conference on computer, electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Gupta, Mridul Yadav, Devansh Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350388534

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life. © 2024 IEEE.

关键词： Contrastive Learning

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Design and Characterization of a Ka-Band Parasitic Patch Antenna for 5G Applications 9

Design and Characterization of a Ka-Band Parasitic Patch Ant...

引用

9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Kouser, Kahkashan Hannan, Mohammad Shafi, Faizan Aknan, Mohammad Dept. of Computer Science & Engineering Bihar Motihari India Rajiv Gandhi Institute of Petroleum Technology Dept. of Electrical and Electronics Engineering Uttar Pradesh Amethi India Indian Institute of Technology Dept. of Electrical Electronics and Communication Engineering Dharwad India

ISBN: (纸本)9798350377972

He design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable for compact wireless *** design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable

关键词： Slot antennas

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Joint Power and Channel Allocation in Fading Channels Using Deep Reinforcement Learning 2

Joint Power and Channel Allocation in Fading Channels Using ...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798331519056

This paper investigates joint power and channel allocation under fading communication channels using Deep Reinforcement Learning (DRL). To this end, we design a DRL to allocate dynamic resources with different network condition properly and achieve the spectral efficiency improvement while reducing latency to meet lower bound in cost function as well keeping target fairness. Results show that our DRL-based approach can achieve better spectrum efficiency and latency than traditional algorithms while at the same time, sustaining a higher value of fairness index. In addition, it is observed that the DRL method also offers both adaptability (with fast adaptation to network changes) and robustness in performance but at a higher computational expense. Our results illustrate that the DRL-based technique may serve as a strong, scalable candidate in future wireless systems up to several orders of magnitude better than existing resource allocation schemes. © 2024 IEEE.

关键词： Deep reinforcement learning

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Performance Evaluation of Triple-Branch Diversity Receivers in Composite Gamma-Shadowed Rician Fading Channels with AMC and Asymmetric SNR Conditions 1

Performance Evaluation of Triple-Branch Diversity Receivers ...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

ISBN: (纸本)9798350365016

This paper provides an in-dept. analysis of the performance enhancement resulting from AMC in triple-branch diversity receivers under composite Gamma shadowed Rician fading channels with uneven SNR conditions. This paper was able to show the level of enhancement in average SNR resulting through simulations of AMC on signal quality in several receiver branches, which clearly shows AMC's ability in alleviating fading and shadowing based degradations. The analysis revealed that AMC adequately alleviates degradations by changing modulation schemes and coding rates based on the current quality of the channel. This reinforces AMC's ability to improve the link's reliability and likelihood to optimize the efficiency of the communication channel. This makes AMC a desirable design and optimization feature for wireless communication in highly fading and shadowed environments, which makes this innovation a potential fundamental technology in the realization of robust, resilient, and adaptive communication systems. © 2024 IEEE.

关键词： Signal to noise ratio

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