DroidPerf: Profiling Memory Objects on Android Devices

Li, Bolun; Zhao, Qin; Jiao, Shuyin; Liu, Xu

doi:10.1145/3570361.3592503

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…Initially adopted by major marketplaces, these models have since evolved and adapted to augment and streamline the product recommendation processes within individual e-commerce platforms. Efficient e-commerce performance is also crucial for real-time processing and responsiveness, which are essential factors in systems that provide recommendations, especially in dynamic and data-intensive environments [5,6].…”

Section: Literature Reviewmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.

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Section: Literature Reviewmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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show abstract

“…While the majority of chatbots have been designed for commercial applications, their utility extends beyond other areas, such as telecommunications, security, tourism promotion or health. DroidPerf, a lightweight Android profiler, develops a communicable bot to perform analysis to uncover memory inefficiencies in Android apps running on the Android Runtime platform (Li et al, 2023). EMMA is a virtual assistant developed for the US Citizenship and Immigration Services that assists individuals with requests related to immigration services, green cards, passports, and any services offered by the department (US Department of Homeland Security, 2024).…”

Section: Background On Chatbots Used For Emotion Processingmentioning

confidence: 99%

CuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence?

Ferrández,

Lavigne-Cerván,

Peral

et al. 2024

PeerJ Computer Science

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In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of “tales with a message” is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.

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“…The popularity of online social networks (OSN) and social media has surged in recent years owing to the easy accessibility of the internet and mobile devices ( Li et al, 2023 ; Khairy, Mahmoud & Abd El-Hafeez, 2021 ). However, these platforms have become increasingly plagued by negative and abusive behavior, detracting from their original purpose as constructive and positive spaces.…”

Section: Introductionmentioning

confidence: 99%

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

El Koshiry,

Eliwa,

Abd El-Hafeez

et al. 2024

PeerJ Computer Science

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This study investigates the effectiveness of various deep learning and classical machine learning techniques in identifying instances of cyberbullying. The study compares the performance of five classical machine learning algorithms and three deep learning models. The data undergoes pre-processing, including text cleaning, tokenization, stemming, and stop word removal. The experiment uses accuracy, precision, recall, and F1 score metrics to evaluate the performance of the algorithms on the dataset. The results show that the proposed technique achieves high accuracy, precision, and F1 score values, with the Focal Loss algorithm achieving the highest accuracy of 99% and the highest precision of 86.72%. However, the recall values were relatively low for most algorithms, indicating that they struggled to identify all relevant data. Additionally, the study proposes a technique using a convolutional neural network with a bidirectional long short-term memory layer, trained on a pre-processed dataset of tweets using GloVe word embeddings and the focal loss function. The model achieved high accuracy, precision, and F1 score values, with the GRU algorithm achieving the highest accuracy of 97.0% and the NB algorithm achieving the highest precision of 96.6%.

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DroidPerf: Profiling Memory Objects on Android Devices

Cited by 11 publications

References 36 publications

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

CuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence?

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

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