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共 42 条记录，以下是1-10 订阅

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Efficient Computational Cost Reduction in KNN through Maximum Entropy Clustering 6

Efficient Computational Cost Reduction in KNN through Maximu...

引用

6th International Conference on computing and Informatics, ICCI 2024

作者： AbdelAzim, Hazem Tharwat, Mohamed Mohammed, Ammar School of Computing and Digital Tech ESLSCA University Egypt

ISBN: (纸本)9798350373875

The K Nearest Neighbor (KNN) is a widely-used classification algorithm recognized for its simplicity and applicability across diverse domains in classification tasks. Despite its straightforward nature, KNN encounters a notable challenge regarding computational costs per classification instance. This paper introduces an algorithm to address this challenge by incorporating entropy-based clustering into the KNN algorithm. Unlike traditional distance-based clustering methods, the proposed algorithm involves partitioning data into disjoint clusters and minimizing entropy within the neighbors. Representative points are then extracted from these clusters to compute nearest neighbors. Experimental resnlts on various UCI datasets reveal a significant reduction in computational costs achieved by utilizing a smaller set of representative samples to represent data instances. Furthermore, the statistical t-test analysis of the accuracy results indicates that KNN performs similarly when utilizing entropy-based representatives. © 2024 IEEE.

关键词： Cost reduction

来源：评论

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Detecting Diabetes Misinformation: Leveraging Medical Expertise and NLP Models for Effective Detection 2

Detecting Diabetes Misinformation: Leveraging Medical Expert...

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Aziz, Ebraam Hani Ibrahim, Ahmed Abdelmalek Samir Said, Abdelrahman Mohamed Hossam, Magi Waheed, Mohamed Tharwat School of computing and digital tech Eslsca Univeristy Giza Egypt

ISBN: (纸本)9798350362633

The proliferation of health-related misinformation on social media, particularly regarding diabetes, poses a substantial threat to public health decision-making and awareness. This study addresses the challenge of identifying false information about diabetes on Arabic social media platforms. A comprehensive dataset was collected directly from these platforms, with the verification of data authenticity conducted by medical professionals who accurately labeled the information. We subsequently evaluated various machine learning models for their effectiveness in detecting misinformation, achieving notable accuracy. Furthermore, the study incorporated an advanced Large Language Model (LLM), specifically GPT-4, to assess its proficiency in identifying fake news relative to the evaluations by our medical team. The findings highlight the efficacy of integrating human expertise with artificial intelligence to enhance the detection and mitigation of misleading health information. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

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Retail Enhancement Using Computer Vision Models 2

Retail Enhancement Using Computer Vision Models

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Zgorni, Ghassan Qussay, Adham Elmasry, Salma Ayman, Renada Habib, Sama Amr Hossam, Magi School of Computing and Digital Technology ESLSCA University Cairo Egypt

ISBN: (纸本)9798350362633

In the realm of retail, particularly in supermarkets and hypermarkets, monitoring, product management, and stocking traditionally rely on human effort and manual labor. However, this approach often proves to be time-consuming, labor-intensive, and prone to errors. To address these challenges, this study proposes the implementation of computer vision and object detection technologies in the retail industry. By implementing computer vision models, the study aims to automate the process of detecting retail items on shelves, with a specific focus on a manually curated drinks dataset. To accomplish this objective, this paper performs a comparative analysis of two prominent object detection models, namely YOLO versions 8 and 9, and DETR, within the context of retail supermarket scenarios. A novel dataset comprising 2000 real-life images manually collected from retail shelves is created to assess the performance of these frameworks in accurately detecting and localizing products. The experimental findings indicate that YOLOv9 a chieved a mean average precision (mAP) score of 0.6, surpassing both DETR and YOLOv8 in terms of detection accuracy and localization capabilities. © 2024 IEEE.

关键词： Sales

来源：评论

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Multi-Hop Arabic LLM Reasoning in Complex QA 6

Multi-Hop Arabic LLM Reasoning in Complex QA

引用

6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Abdelazim, Hazem Begemy, Tony Galal, Ahmed Sedki, Hala Mohamed, Ali School of Computing and Digital Technologies ESLSCA University Giza 11841 Egypt

The introduction of Large Language Models (LLMs), and generative AI has significantly transformed the field of natural language processing. These models have exhibited profound reasoning capabilities, marking considerable progress across diverse general knowledge reasoning tasks. Consequently, the deployment of LLMs in domain-specific contexts has become a prime objective for governments and corporations eager to leverage the generative AI revolution. However, the Arabic language has notably lagged in attention and development compared to other languages in this arena. This research endeavors to delve into various facets of Arabic closed-domain question and answering systems that emulate the reasoning requirements of private enterprise data. Our study focuses on the practical deployment of Arabic LLMs in targeted applications, specifically utilizing the ACQAD (Arabic Complex Question Answering Dataset), which exhibits multi-hop reasoning. Different strategies are experimented using Long Context Window (LCW) and Retrieval Augmented Generation (RAG). Results showed that decomposing complex questions using Chain-of-Thought reasoning considerably improved the performance from 75% to 92% using LCW, but at much higher token cost compared to RAG. Trade-of between cost and performance showed that 80% accuracy can be attained using only 30% of the cost using RAG Sentence - level embeddings. Microsoft E5 embedding model is used and OpenAI GPT4-turbo LLM which proved superior reasoning performance compared to other Arabic LLMs. © 2024 Elsevier B.V.. All rights reserved.

关键词： Question answering

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Data Augmentation for Sentiment Analysis Enhancement: A Comparative Study 6

Data Augmentation for Sentiment Analysis Enhancement: A Comp...

引用

6th International Conference on computing and Informatics, ICCI 2024

作者： Yasser, Farida Hatem, Souzan Ayman, Laila Mohamed, Loujain Mohammed, Ammar School of Computing and Digital Technology ESLSCA University Egypt Cairo Egypt

ISBN: (纸本)9798350373875

For many years, researchers in the field of natural language processing have been exploring sentiment analysis, a method for understanding human feelings and thoughts expressed in text. Sentiment analysis works by first analyzing the sentiment of individual words or phrases, using methods like dictionaries, machine learning, or natural language processing. In machine learning, however, the reliance on training data quality and quantity poses challenges, including data scarcity and imbalanced label distribution. One possible way to increase the distribution of textual data is utilizing data augmentation methods in which the train data samples are artificially transformed to another data with similar context. One effective approach to generating a more extensive and varied textual samples involves utilizing the capabilities of large language ***, this paper introduces a comparative study on evaluating the impact of various augmentation methods namely, Random Deletion, Synonym Replacement, GPT3.5 generation, and Character Swapping. We compare the performance across six deep learning models, namely CNN-LSTM, BI-LSTM, BERT, TCN, Ensemble CNN Bidirectional GRU, and Deep neural network. The experimental results reveal that BERT exhibits significant accuracy improvements across different augmentation methods, showcasing gains of 14% in Random Deletion, 12.9% in Synonym Replacement, and 6.5% in Character Swapping. © 2024 IEEE.

关键词： Sentiment analysis

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Hybrid Approach for COVID-19 Severity Identification. 3

Hybrid Approach for COVID-19 Severity Identification.

引用

3rd International Mobile, Intelligent, and Ubiquitous computing Conference, MIUCC 2023

作者： Mahfouz, Magi Samuel, George Mohammed, Ammar Msa University School of Computing and Digital Tech Eslsca University Faculty of Cs Egypt Cairo University Department of Cs Egypt

ISBN: (纸本)9798350306231

Research effort have been dedicated to the utilization of deep learning techniques for the detection of COVID-19 and various forms of pneumonia. Although these efforts have produced commendable results in terms of performance, a prevalent focus has been on classifying different pneumonia types, with limited attention paid to assessing infection severity. In this context, our study offers a multifaceted contribution. Firstly, we propose a hybrid framework that combines a Convolutional Neural Network (CNN) with a fuzzy system to identify infection severity. The CNN-based model comprises two stacked layers of CNN models designed to distinguish between normal cases and various pneumonia types. Concurrently, the fuzzy system processes numerical laboratory test data and the CNN model's classification outcomes to determine the degree of infection severity. Experimental findings from experiments conducted on a meticulously curated dataset using diverse CNN models showcase that the VGG16 model achieved an impressive 92%. specificity and 88% accuracy in distinguishing between viral and bacterial pneumonia. Furthermore, our fuzzy model underwent evaluation with twelve actual medical cases, revealing its capacity for accurate assessment of recommended severity levels among patients. This approach facilitates healthcare specialists in quantifying the severity of viral pneumonia through a scoring system, enhancing the diagnostic process. © 2023 IEEE.

关键词： COVID-19

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Clustering techniques for Frame Delay Variations in Mobile Networks 11

Clustering Techniques for Frame Delay Variations in Mobile N...

引用

11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Waheed, Mohamed Tharwat Abdelaziem, Hazem Mahmoud, Ibrahim Mohamed, Ammar School of computing and Digital techn. Eslsca University Vodafone Egypt Giza Egypt School of computing and Digital techn. Eslsca University Giza Egypt Vodafone Egypt Technology Department Giza Egypt

ISBN: (纸本)9798350369328

This study presents a novel solution for addressing mobile network frame delay variations (FDV), a common issue leading to service interruptions. Our methodology departs from traditional approaches by implementing advanced clustering techniques and a unique feature engineering strategy. We concentrate on specific data characteristics, enabling a more accurate identification and categorization of delay patterns. This targeted method allows for efficient grouping and management of similar delays, enhancing network performance. Tested in a live network environment, our approach demonstrates significant improvements in network reliability and offers insights into the root causes of FDV in real time. This research contributes to the field by providing a practical and effective solution for managing FDV in mobile networks. © 2023 IEEE.

关键词： Wireless networks

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Efficient Computational Cost Reduction in KNN through Maximum Entropy Clustering

Efficient Computational Cost Reduction in KNN through Maximu...

引用

computing and Informatics (ICCI), International Conference on

作者： Hazem AbdelAzim Mohamed Tharwat Ammar Mohammed School of Computing and Digital Tech ESLSCA University Egypt

The K Nearest Neighbor (KNN) is a widely-used classification algorithm recognized for its simplicity and applicability across diverse domains in classification tasks. Despite its straightforward nature, KNN encounters a notable challenge regarding computational costs per classification instance. This paper introduces an algorithm to address this challenge by incorporating entropy-based clustering into the KNN algorithm. Unlike traditional distance-based clustering methods, the proposed algorithm involves partitioning data into disjoint clusters and minimizing entropy within the neighbors. Representative points are then extracted from these clusters to compute nearest neighbors. Experimental results on various UCI datasets reveal a significant reduction in computational costs achieved by utilizing a smaller set of representative samples to represent data instances. Furthermore, the statistical t-test analysis of the accuracy results indicates that KNN performs similarly when utilizing entropy-based representatives.

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Detecting Diabetes Misinformation: Leveraging Medical Expertise and NLP Models for Effective Detection

Detecting Diabetes Misinformation: Leveraging Medical Expert...

引用

Intelligent Methods, Systems, and Applications (IMSA)

作者： Ebraam Hani Aziz Ahmed Abdelmalek Samir Ibrahim Abdelrahman Mohamed Said Magi Hossam Mohamed Tharwat Waheed School of computing and digital tech Eslsca Univeristy Giza Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

关键词： Accuracy Social networking (online) Prevention and mitigation Large language models Decision making Machine learning Diabetes

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Enhancing Road Safety: A Comprehensive Driver Behavior Scoring Framework with K-Means Action Segmentation and Deep Learning Behavior Detection 2

Enhancing Road Safety: A Comprehensive Driver Behavior Scori...

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Yasser, Farida Hatem, Souzan Waleed, Hatem Ahmed, Ziad Hossam, Bassmalla Ali, Raghda Essam Atia, Ayman School of Computing and Digital Technology ESLSCA University Cairo Egypt Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt ESLSCA University Cairo Egypt Helwan University HCI-LAB Faculty of Computers Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350362633

The rising concern for road safety due to increasing vehicle numbers requires innovative strategies to address unsafe driving practices. This paper presents a comprehensive Driver Behavior Scoring Framework aimed at improving road safety. It consists of five modules: Video Segmentation, Behavior Classification, Voice and Speech Analysis, Facial Analysis, and Safety Scoring. In Video Segmentation, we propose a novel approach using VGG-16 and k-means for action segmentation. The Behavior Classification module utilizes a CNN-LSTM classifier, which obtained an F1-score of 87.5%. Voice and Speech Analysis use a DistilBERT-based sentiment analysis achieving 93.42% accuracy in sentiment detection and 76.6% accuracy in profanity detection. Facial Analysis detects eye and mouth movements with MAE of 0 and 1.6 respectively, and identifies anger and happiness behaviors with an MAE of 44 using a CNN-based pretrained model. The fifth module introduces a Linear Regression-based safety scoring algorithm trained on survey data, yielding an RMSE of 1.1616. © 2024 IEEE.

关键词： Speech analysis

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