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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Naeem, Javaria Gül, Ömer Melih Parlak, Ismail Burak Karpouzis, Kostas Kadry, Seifedine Nimer Salman, Yücel Batu Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Computer Engineering Galatasaray University İstanbul Turkey Panteion Uninversity of Social and Political Sciences Kallithea Greece Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates Department of Software Engineering Bahcesehir University İstanbul Turkey

ISBN: (纸本)9783031644948

The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Electronically Steerable Mobile Optical-Wireless Mesh-Network

Electronically Steerable Mobile Optical-Wireless Mesh-Networ...

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Annual IEEE Systems Conference

作者： Suman Bhunia Yicheng Qian Mahmudur Khan Department of Computer Science and Software Engineering Miami University Oxford Ohio USA Department of Electrical & Computer Engineering and Computer Science York College of Pennsylvania USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

Free Space Optical (FSO) Communication provides better communication coverage and higher data rate. However, FSO requires neighboring nodes to schedule their transceiver towards each other for communication, which makes multi-hop communication challenging. In this paper, we present an FSO mesh network system that enables data transfer among multiple nodes. Each node is equipped with a multi-transceiver FSO communication module and a low-bitrate long-range (LoRa) omnidirectional radio frequency (RF) communication module. Scheduling remains a significant challenge for wireless communication using highly directional transceivers such as FSO. Hence, we propose a distributed coordination and communication method that utilizes a supplementary omnidirectional LoRa channel for distributed coordination among the nodes in the mesh network. In the proposed scheme, a per-packet beam scheduling is proposed where the assisting low bandwidth omnidirectional channel helps the higher data rate directional channel to coordinate and face the line of sight. Our proposed method enables communication via both single-hop and multi-hop FSO links. We also present the implementation of a proof-of-concept prototype of an RF-FSO communication module comprising multiple FSO transceivers and a LoRa transceiver using commercial off-the-shelf devices (COTS). We demonstrate the effectiveness of our proposed FSO mesh network system through real test-bed experiments using the developed prototype.

关键词： Wireless communication Radio frequency Mesh networks Schedules Free-space optical communication Protocols Wireless mesh networks

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Predictive Modeling of House Prices;A Regression Approach 18

Predictive Modeling of House Prices;A Regression Approach

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Saeed, Natasha Saeed, Ahsan Bashir, Mariam Caceres-Najarro, Lismer Andres National University of Sciences and Technology Department of Computer & Software Engineering Islamabad Pakistan National Excellence Institute Department of Computer Science Islamabad Pakistan Korea Institute of Energy Technology Naju-si Korea Republic of

ISBN: (纸本)9798350328172

Housing prices have always been an important issue among the business community. The sudden increase in property prices has an impact on economic cycles as well as our means of living. However, understanding several features that affect residential real estate prices is still challenging. Traditional techniques have been unable to choose essential features for precise predictions, which frequently leads to reduced prediction accuracy. In this research work, we utilized machine learning techniques to increase prediction accuracy and get data-driven insights into property evaluation. This study compares Random Forest Regressor and the Decision Tree Regressor using an extensive dataset of Bangalore that includes nine property features. The findings show that our models achieved an optimal accuracy level and provide insights into the variances in model performance, assisting policymakers in choosing the most suitable approaches to get accurate house prices across various market circumstances. © 2023 IEEE.

关键词： Decision trees

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Application of Nonlinear Gaussian Process Regression Models for CO2 Emissions Prediction 15

Application of Nonlinear Gaussian Process Regression Models ...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Ahmed, Ahmed Abdelhadi Badri, Yousif Shamseldin, Asaad Department of Computer Science and Software Engineering Al Ain United Arab Emirates University of Auckland Faculty of Engineering Department of Mechanical and Mechatronics Engineering Auckland New Zealand University of Auckland Faculty of Engineering Department of Civil and Environmental Engineering Auckland New Zealand

ISBN: (纸本)9798350382396

The growing concern over climate change led to a massive corpus of literature addressing the impact of various factors in terms of carbon dioxide (CO2) emissions levels. However, the majority of existing literature employs linear models to a singular factor, neglecting the relation of these factors and their impact in conjunction on CO2 emissions levels. To answer this question, this study conducted an intensive literature review to identify proper factors to deduce parameters that influence CO2 emissions levels. These parameters, derived from data spanning from 1965 to 2021 in the United Arab Emirates, underwent thorough analysis using Dominance Analysis to establish their significance in relation to CO2 emissions levels. Subsequently, we employed nonparametric nonlinear Gaussian Process Regression (GPR) models with diverse kernels to assess their performance against traditional parametric linear models. The results showed that nonparametric nonlinear GPR models provide the most accurate predictions for CO2 emissions, with the GPR-Matern kernel outperforming all other models, achieving an R-squared value of 98.1%. Our findings highlight the importance of adopting a nonlinear approach for predicting and forecasting CO2 emissions levels when considering the interplay of driving factors. © 2023 IEEE.

关键词： Energy utilization

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and Data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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Positive Sample Augmentation with Graph Mixup for DDI Prediction 1

Positive Sample Augmentation with Graph Mixup for DDI Predic...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Yu, Hailong Ma, Xinyu Chu, Xu Wang, Yasha Wu, Rengyu Zhou, Qiang School of Computer Science Peking University Beijing China Tsinghua University Department of Computer Science and Technology Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Shenzhen Center for Prehospital Care Shenzhen China

ISBN: (纸本)9798350358780

Unexpected drug-drug interactions (DDIs) may occur when drugs are taken at the same time. Harmful DDIs can reduce drug efficacy and even increase unintended toxicity, putting patients treated with different drugs at risk. Previous works on DDI prediction mainly rely on hand-engineered domain knowledge, which is laborious to obtain and cannot handle the problem of sparse positive samples in DDI prediction. Even though some works attempt to handle data sparsity from the perspective of data distribution to improve the generalization ability of the model, they are limited to negative samples distribution. To address these limitations, we propose a novel method, Graph Mixup for Positive Sample Augmentation (GMPSA), to effectively combine positive drug molecule graphs and extend the training distribution to alleviate the problem of sparse positive samples. To model the graph signals and graph structures jointly, we map source graphs to the fused gromov-wasserstein metric space, then minimizes the weighted sum of transportation distances between the distributions of the source graphs and the objective graph in this metric space. By doing so, interpolated positive samples improve the the generalization error of the model. Experiments results on a real-word dataset show that our method effectively improve the perfomermance in DDI prediction on a state-of-the-art architecture. Further studies also verify the robustness and generalizability of GMPSA under different GNN backbones and parameter settings. © 2023 IEEE.

关键词： Forecasting

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Enhancing Extreme Programming (XP) Adoption through SAMAM: A Scalable Agile Maturity Assessment Model Based on Industry Best Practices

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IEEE Access 2025年 13卷 97647-97680页

作者： Abrar, Muhammad Faisal Alferaidi, Ali Almurayziq, Tariq S. Saqib, Muhammad Khan, Wilayat Khan, Zawar Alsaffar, Mohammad University of Ha’il College of Computer Science and Engineering 55476 Saudi Arabia University of Engineering & Technology Department of Software Engineering Peshawar 25000 Pakistan

The adoption of Extreme Programming (XP), a widely recognized Agile methodology, faces numerous barriers that hinder its successful implementation in software development organizations. This research aims to develop a novel Scalable Agile Maturity Assessment Model (SAMAM) to address these barriers and facilitate the effective adoption of XP. The model is designed by leveraging established frameworks, including the Capability Maturity Model Integration (CMMI), software Outsourcing Vendor Readiness Model (SOVRM), and software Process Improvement Implementation Management Model (SPIIMM). Unlike traditional models that rely on predefined Key Process Areas (KPAs), SAMAM adopts 14 critical barriers (CBs) identified through a Systematic Literature Review (SLR) and corresponding practices as the foundation for its maturity levels. The study was conducted in four phases. First, an SLR was performed to identify 14 critical barriers to XP adoption and their respective mitigation practices. In the second phase, a survey questionnaire was administered within the software industry to validate the SLR findings and extract additional industry-relevant practices. The third phase involved the development of SAMAM, structured into five maturity levels using the identified barriers and practices instead of traditional KPAs. In the final phase, industrial case studies were conducted to evaluate the model’s effectiveness in real-world settings using the Motorola Assessment Tool. The findings demonstrate that SAMAM provides a comprehensive and scalable approach to assess and improve XP adoption maturity by systematically addressing critical adoption barriers. The model supports organizations in overcoming XP adoption challenges and achieving higher process maturity. The evaluation through case studies confirmed the practical applicability and effectiveness of the proposed model, contributing to the body of knowledge on agile methodologies and advancing XP adoption in the software developm

关键词： Agile maturity assessment mode Extreme Programming (XP) Adoption Scalable agile adoption (SAMAM) software process improvement

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Emotionally Challenging Games Can Satisfy Older Adults' Psychological Needs: From Empirical Study to Design Guidelines 25

Emotionally Challenging Games Can Satisfy Older Adults' Psyc...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Zhou, Min Peng, Xiaolan Liu, Binjie Denisova, Alena Barathi, Soumya C. Li, Zhuying Xie, Xurong Huang, Jin Tian, Feng Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science University of York York United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798400713941

Older adults often struggle to meet their psychological needs due to retirement and living alone. Recent studies suggest that games featuring emotional challenge (EC) can help fulfill basic psychological needs such as autonomy, competence, and relatedness by facilitating emotional exploration. However, it remains unclear whether older adults can benefit from EC games, whether they find this genre enjoyable, and how these games should be designed to better meet their needs. This work explores older adults' experiences and perceptions of playing EC games through two studies. The first study involved playing Detroit: Become Human, revealing that older adults derived multifaceted psychological experiences from playing the game. The second study involved a custom-designed game scenario tailored to older adults, demonstrating that meaningful choices significantly influenced autonomy need satisfaction. Based on these findings, we offer five design guidelines for developing EC games that satisfy psychological needs of older adults. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Interactive computer graphics

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Detecting AI-Generated Text: A Bi-GRU with Linguistic Features Approach

Detecting AI-Generated Text: A Bi-GRU with Linguistic Featur...

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IEEE Education engineering (EDUCON)

作者： Abdelhadi Hireche Saja Al-Dabet Mohammed Mediani Abdelkader Nasreddine Belkacem Department of Computer and Network Engineering United Arab Emirates University Computer Science & Software Engineering Department United Arab Emirates University

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

The advances in artificial intelligence (AI) technology can transform education. However, the growing infusion of AI technologies into academic environments raises important ethical issues that are essential for safeguarding academic integrity and quality. This paper proposes a detection framework that utilizes linguistic features and a Bidirectional Gated Recurrent Unit (Bi-GRU) model to identify AI-generated texts. The framework extracts perplexity values, readability measures, syntactic complexity metrics, and lexical diversity indicators, which are fed into a Bi-GRU classifier. Trained on an extended version of the DAIGT dataset and evaluated on the Deepfake dataset, the model achieved an accuracy of 98 % with F1 score of 97 % when tested on the DAIGT dataset. It also achieved an accuracy of $\mathbf{7 2 \%}$ and an F1 score of $\mathbf{7 9 \%}$ on the Deepfake dataset, outperforming state-of-the-art methodologies in these datasets. These findings highlight the potential of combining linguistic feature analysis with deep learning to develop efficient, interpretable, and domain-adaptive systems for AI text detection, addressing the critical need for automating authenticity and maintaining integrity in content generation.

关键词： Deep learning Deepfakes Accuracy Diversity reception Text detection Linguistics Syntactics Feature extraction Complexity theory Artificial intelligence

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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