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Advanced Machine Learning Models For Accurate Kidney Cancer Classification Using Ct Images, Dhuha Abdalredha Kadhim, Mazin Abed Mohammed Jan 2025

Advanced Machine Learning Models For Accurate Kidney Cancer Classification Using Ct Images, Dhuha Abdalredha Kadhim, Mazin Abed Mohammed

Mesopotamian Journal of Big Data

Kidney cancer, particularly renal cell carcinoma (RCC), poses significant challenges in early and accurate diagnosis due to the complexity of tumor characteristics in computerized tomography (CT) images. Traditional diagnostic approaches often struggle with variability in data and lack the precision required for effective clinical decision-making. This study aims to develop and evaluate machine learning (ML) models for the accurate classification of kidney cancer using CT images, focusing on improving diagnostic precision and addressing potential challenges of overfitting and dataset heterogeneity. Two ML models, Support Vector Machines (SVM) and Multi-Layer Perceptrons (MLP), were employed for classification. Key attribute extraction techniques, including …

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Overview Of The Ciciot2023 Dataset For Internet Of Things Intrusion Detection Systems, Wisam Ali Hussein Salman, Chan Huah Yong Jan 2025

Overview Of The Ciciot2023 Dataset For Internet Of Things Intrusion Detection Systems, Wisam Ali Hussein Salman, Chan Huah Yong

Mesopotamian Journal of Big Data

The rapid expansion of the use of the Internet of Things (IoT) has encouraged many attackers to exploit the vulnerabilities in these networks to violate data privacy or disrupt service; they are easy targets due to the diversity of devices within the network, which has led to the loss of unified security standards. intrusion detection system (IDS) play a pivotal role in securing IoT networks by monitoring inbound and outbound traffic to these networks and issuing a security alarm when there is an attack; moreover, they respond directly to these security threats to prevent them from harming the network and …

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Coupling A Dual Tree - Complex Wavelet Transform With K-Means To Clustering The Epileptic Seizures In Eeg Signals, Raid Lafta, Rasha Hallem Razzaq Jan 2025

Coupling A Dual Tree - Complex Wavelet Transform With K-Means To Clustering The Epileptic Seizures In Eeg Signals, Raid Lafta, Rasha Hallem Razzaq

Mesopotamian Journal of Big Data

Electroencephalography (EEG) signals are routinely recorded in clinical settings for the diagnosis of epilepsy, that is, brain electrical disorders, by a neurologist. Nevertheless, both the reliability and safety of GB analysis of EEG parameters are not satisfactory. Therefore, identifying the effectiveness of EEG for diagnosis is a considerable challenge for hospitals. This study was conducted to improve the efficiency of detecting epileptic seizures in EEG signals by combining three methodologies: the double tree complex wavelet transform (DT-CWT), K means clustering, and the ChaCha20 encryption algorithm. EEG segments are initially partitioned into regular segments, each of which is further partitioned into …

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A Comparative Study Of Rfid System Performance In Large-Scale Network Planning Facility, Ali Abdulqader Al Qisi, Azli Bin Nawawi, Adel Muhsin Elewe Jan 2025

A Comparative Study Of Rfid System Performance In Large-Scale Network Planning Facility, Ali Abdulqader Al Qisi, Azli Bin Nawawi, Adel Muhsin Elewe

Mesopotamian Journal of Big Data

Big data in manufacturing fields present several challenges leads to reduce profitability and missed opportunity for innovation. One of the used strategies is the use of radio frequency identification system.  considered a business strategy to increase productivity, speed up decision-making, and enhance production monitoring and control while preserving the structure and integrity of current manufacturing systems. The present research compares five artificial inelegant algorithms based on RFID system in facility layout design to investigate the fitness of each algorithm in manufacturing big data processing. The objective functions have been used are the minimum number of required readers, minimum readers overlap, …

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Intelligent Techniques For Autism Spectrum Disorder Diagnosis: A Review, Ekraam Jabier, Ali Fadhil Marhoon, Ammar A. Aldair Jan 2025

Intelligent Techniques For Autism Spectrum Disorder Diagnosis: A Review, Ekraam Jabier, Ali Fadhil Marhoon, Ammar A. Aldair

Mesopotamian Journal of Big Data

Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose prevalence has increased drastically around the world due to the shortcomings of the traditional method of diagnosis, which has been shown to be unsustainable since it is usually time-consuming, expensive, and subjective to clinical interpretation. These difficulties make finding more scalable, efficient, and objective methods of diagnosis incredibly expedient. This review explores the impacts of intelligent technologies such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and Internet of Things (IoT) sensor-supported systems and the revolution they are bringing into the diagnosis of ASD. This work reviews the recent …

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Utilizing Graph Theory Algorithms For The Modeling And Analysis Of Covid-19 Infection Dynamics, Ioannis Adamopoulos, Antonios Valamontes, Niki Syrou, Jovanna Adamopoulou, Antonios Bardavouras Jan 2025

Utilizing Graph Theory Algorithms For The Modeling And Analysis Of Covid-19 Infection Dynamics, Ioannis Adamopoulos, Antonios Valamontes, Niki Syrou, Jovanna Adamopoulou, Antonios Bardavouras

Mesopotamian Journal of Artificial Intelligence in Healthcare

The COVID-19 outbreak has shown how urgently good models are needed to understand and project the dissemination of infectious diseases. Graph theory's excellent basis for presenting and analyzing complex networks helps one to grasp COVID-19 transmission dynamics. This work investigates the simulation and evaluation of the COVID-19 spread using multiple graph theory approaches. We utilize algorithms, including centrality assessments, community detection, and epidemic spreading models, to identify significant transmission channels and viable intervention sites; we also study the usage of network-building strategies to show relationships between individuals and communities. By incorporating real-world data with graph-based models, we demonstrate how these …

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Parkinson's Disease Detection Using Deep Learning Approach Based On Wearable Sensor-Based Daily Monitoring, Bahaulddin N. Adday, Khalid Shaker, Ihsan Salman, Hothefa Shaker Jan 2025

Parkinson's Disease Detection Using Deep Learning Approach Based On Wearable Sensor-Based Daily Monitoring, Bahaulddin N. Adday, Khalid Shaker, Ihsan Salman, Hothefa Shaker

Mesopotamian Journal of Artificial Intelligence in Healthcare

Parkinson's disease (PD) is a movement disorder characterized by motor dysfunction commonly bradyphemia, tremor, rigidity, akinesia, or slowness of movement. Noting that motor states can fluctuate in PD the primary aim of this current paper was to differentiate multiple states using wearable sensors in the patients and detection of this PD based on deep learning (CNN). Methodology: In this paper, the researchers recorded the signals of the accelerometer and gyroscope fixed on the wrist of PD in their regular daily functioning after using this dataset collection. The deep learning architecture developed was to optimize a CNN for analyzing the sensor …

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Heartbeat Sound Classification Using Mel-Spectrogram And Cnn Optimized By Frilled Lizard Algorithm For Cardiovascular Disease Detection, Ahmed T. Alhasani, Zainab H. Albakaa, Shahad A. Alabidi, Osamah Qasim Abd Zaid Gburi, Ammar Kadi, Irina Potoroko Jan 2025

Heartbeat Sound Classification Using Mel-Spectrogram And Cnn Optimized By Frilled Lizard Algorithm For Cardiovascular Disease Detection, Ahmed T. Alhasani, Zainab H. Albakaa, Shahad A. Alabidi, Osamah Qasim Abd Zaid Gburi, Ammar Kadi, Irina Potoroko

Mesopotamian Journal of Artificial Intelligence in Healthcare

Cardiovascular disease (CVD) continues to be the predominant cause of mortality globally, underscoring the critical necessity for prompt and precise diagnostic techniques.  This paper introduces an innovative machine learning framework for categorizing heartbeat sounds into four classifications—normal, murmur, additional heart sound, and artifact—utilizing audio recordings from the PhysioNet/CinC Challenge 2016 dataset.  The methodology employs Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, converting raw heart sound data into comprehensive time-frequency representations.  A Convolutional Neural Network (CNN) is utilized for classification, with its hyperparameters refined by the recently developed Frilled Lizard Optimization (FLO) method, a bio-inspired metaheuristic that emulates the …

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Analysis Of Climate Change In Chelyabinsk And Kurgan: Effects Of Temperature And Precipitation From 1990 To 2020 Based On Cru Data, Irina Potoroko, Ammar Kadi, Ali Subhi Alhumaima Jan 2025

Analysis Of Climate Change In Chelyabinsk And Kurgan: Effects Of Temperature And Precipitation From 1990 To 2020 Based On Cru Data, Irina Potoroko, Ammar Kadi, Ali Subhi Alhumaima

Mesopotamian Journal of Computer Science

This study analyzes climate patterns in the Kurgan and Chelyabinsk regions of Russia using high-resolution data from the Climate Research Unit (CRU) between 1990 and 2020. The research focuses on how temperature and precipitation have evolved over time and their impacts on local ecosystems, agriculture, and water resources. Using MATLAB for visualization, temperature and precipitation maps were created for January and July across five time periods to understand the spatial and temporal variations in these regions. The analysis revealed a noticeable increase in temperature, with warmer winters and hotter summers in both regions. Precipitation patterns showed a shift, with a …

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The Next Frontier In Computer Science, Trends And Research Opportunities, Mostafa Abdulghafoor Mohammed, Z. T. Al-Qaysi, Tahsien Al-Quraishi Jan 2025

The Next Frontier In Computer Science, Trends And Research Opportunities, Mostafa Abdulghafoor Mohammed, Z. T. Al-Qaysi, Tahsien Al-Quraishi

Mesopotamian Journal of Computer Science

No abstract provided.

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A Deep Learning Approach For Identifying Malicious Activities In The Industrial Internet Of Things, Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani Jan 2025

A Deep Learning Approach For Identifying Malicious Activities In The Industrial Internet Of Things, Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani

Mesopotamian Journal of Computer Science

Data-driven decision-making, real-time connectivity, and automation have transformed industrial operations with the Industrial Internet of Things. However, the integration also introduces substantial cybersecurity vulnerabilities, making IIoT networks a prime target for malicious activities. Cyber threats are evolving and becoming more sophisticated, which makes traditional security mechanisms inadequate. An approach using deep learning to detect malicious activities in IIoT environments is examined. It is investigated whether Deep Feed Forward neural networks, autoencoders, and convolutional neural networks are effective at detecting anomalies and mitigating cyber threats. NSL-KDD and UNSW-NB15 benchmark datasets are used to evaluate the proposed model's accuracy, precision, and detection …

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Α Novel Ai-Based Modeling With Bias Classification Hybrid Risk Evaluation System For Confidence Enhanced Network Meta-Analysis Of Occupational Hazards And Burnout Risk Among Public Health Inspectors, Ioannis Adamopoulos Jan 2025

Α Novel Ai-Based Modeling With Bias Classification Hybrid Risk Evaluation System For Confidence Enhanced Network Meta-Analysis Of Occupational Hazards And Burnout Risk Among Public Health Inspectors, Ioannis Adamopoulos

Mesopotamian Journal of Artificial Intelligence in Healthcare

Public Health Inspectors (PHIs) serve a critical role in enforcing health and safety regulations, particularly under the growing pressures of climate change. With rising exposure to occupational hazards such as heat waves, air pollution, and vector-borne diseases, PHIs now face escalating stress and burnout. Geographical variability, limited resources, and institutional gaps in training and support further shape their complex risk profile. Despite growing concern, systematic, confidence-based evaluations of these occupational risks—especially tailored to PHIs—remain rare. This study addresses that gap using a Confidence in Network Meta-Analysis (CINeMA)-enhanced framework to assess domain-specific occupational risk profiles of PHIs working in climate-stressed environments. …

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Woa-Covid-19: Whale Optimization Algorithm For Selection Of Multi-Examination Features Based On Covid-19 Infections, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Zaid Abdi Alkareem Alyasseri, Dawood Zahi Khutar, Osama Ahmad Alomari Jan 2025

Woa-Covid-19: Whale Optimization Algorithm For Selection Of Multi-Examination Features Based On Covid-19 Infections, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Zaid Abdi Alkareem Alyasseri, Dawood Zahi Khutar, Osama Ahmad Alomari

Mesopotamian Journal of Computer Science

Since its emergence in late 2019, COVID-19 (Coronavirus Disease 2019) has become one of the most critical global health threats, claiming millions of lives and placing many more at serious risk. The complexity of diagnosing COVID-19 lies in the wide range of clinical and examination features involved, prompting researchers to explore various advanced diagnostic methods. However, one of the main challenges is identifying the most relevant features that can streamline and improve diagnostic accuracy. In this study, we propose a feature selection approach based on the Whale Optimization Algorithm (WOA) to identify key examination indicators associated with COVID-19. We used …

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Self-Attention Enhanced Dual Bigru For Arabic Fake News Detection, Baqer M. Merzah, Jafar Razmara, Jaber Karimpour Jan 2025

Self-Attention Enhanced Dual Bigru For Arabic Fake News Detection, Baqer M. Merzah, Jafar Razmara, Jaber Karimpour

Mesopotamian Journal of Computer Science

The rapid proliferation of social media platforms has greatly amplified the dissemination of fake news, representing significant obstacles to public trust and evidence-based decision-making, particularly for the Arabic-speaking population. Meeting the challenge of Arabic fake news detection is a problem compounded by the complex morphological nature of the language, as well as limited resources. This study presents a hybrid deep learning framework that integrates two Bidirectional Gated Recurrent Units (BiGRUs) along with an attention mechanism for efficiently detecting misinformation in Arabic news. The method leverages FastText word embeddings for disambiguating the intricate semantics of the Arabic language. The model is …

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Diabetes At A Glance: Assessing Ai Strategies For Early Diabetes Detection And Intervention Via A Mobile App, Ayad Hameed Mousa, Ibrahim Oday Alrubaye, Mayameen S. Kadhim, Ahmed Dheyaa Radhi, Mudatheer M. Al-Slivani, Rusul Mansoor Al-Amri, Liaw Geok Pheng Jan 2025

Diabetes At A Glance: Assessing Ai Strategies For Early Diabetes Detection And Intervention Via A Mobile App, Ayad Hameed Mousa, Ibrahim Oday Alrubaye, Mayameen S. Kadhim, Ahmed Dheyaa Radhi, Mudatheer M. Al-Slivani, Rusul Mansoor Al-Amri, Liaw Geok Pheng

Mesopotamian Journal of Computer Science

Diabetes is a widespread disease worldwide that does not differentiate between children and adults. It also affects the elderly and pregnant women. However, early detection of the disease facilitates its control to avoid the effects resulting from delayed diagnosis. With the emergence of artificial intelligence represented by machine learning techniques and its use in most sectors, accordingly, the adoption of machine learning techniques to help in disease prediction has become a necessity. This study proposes a machine learning algorithm-based approach for diabetes prediction. This study uses three datasets, two of which are private and the other includes the Pima Indians …

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Enhancing Routing Efficiency In Vanets By Leveraging 5g To Mitigate Congestion, Yusor Rafid Bahar Al-Mayouf, Omar Adil Mahdi, Heba Hussain Hadi, Suleman Khan, Mazin Abed Mohammed Jan 2025

Enhancing Routing Efficiency In Vanets By Leveraging 5g To Mitigate Congestion, Yusor Rafid Bahar Al-Mayouf, Omar Adil Mahdi, Heba Hussain Hadi, Suleman Khan, Mazin Abed Mohammed

Mesopotamian Journal of Computer Science

Transportation networks impact millions of people daily. Their efficiency immediately affects travel time, safety, and environmental sustainability. Unfortunately, various issues hinder the expected performance and efficiency of these networks. Traffic congestion is an up-to-date issue in the urban environment. Fuel consumption is high because travel time has increased, which has a passive environmental impact. Extensive research has been conducted to progress the intelligent transportation systems installed on communication networks and information to treat this congestion. However, there is a significant amount of affront residue in combining real-time data, estimation analytics, and 5G abilities effectively. This paper offers a novel routing …

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Roi – Enhancing Detection Of Citrus Disease Based On Yolov10, Raya N. Ismail, Armaneesa Naaman Hasoon, Israa Rafaa Abdulqader, Salwa Khalid Abdulateef Jan 2025

Roi – Enhancing Detection Of Citrus Disease Based On Yolov10, Raya N. Ismail, Armaneesa Naaman Hasoon, Israa Rafaa Abdulqader, Salwa Khalid Abdulateef

Mesopotamian Journal of Computer Science

One of the most important fruit crops in the world is citrus. However, some citrus diseases spread rapidly, which is why early detection at an accurate stage is important for timely intervention. YOLO-based object detection models, such as the latest YOLOv10, where small lesions are difficult to identify among noisy backgrounds, have recently been developed, yet their accuracy tends to degrade. Therefore, we proposed a citrus disease detection model by integrating the region of interest (ROI) for object segmentation with the YOLOv10 model, thus addressing the issues of low detection accuracy and slow inference time. The proposed model was trained …

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A Review Of Image Steganography Based On Metaheuristic Optimization Algorithms, Fatima Abdulhussain Khalil, Ammar Ali Neamah, Hasanen Alyasiri Jan 2025

A Review Of Image Steganography Based On Metaheuristic Optimization Algorithms, Fatima Abdulhussain Khalil, Ammar Ali Neamah, Hasanen Alyasiri

Mesopotamian Journal of Computer Science

Due to the widespread popularity of digital images on the Internet, image-based steganography has become a widely adopted technique for embedding secret information into everyday visual content. In parallel, steganalysis plays a vital role in digital forensics and information security by seeking to uncover hidden content within these images. Although steganographic techniques—particularly those employing adaptive embedding strategies—have made significant progress, many steganalysis approaches still struggle to generalize effectively across different image types and embedding methods. This contrast highlights the need for more intelligent, flexible, and robust analysis frameworks. This review examines steganographic techniques for digital images and the application of …

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2025 Annual Report, Tennessee. Department Of Human Services Jan 2025

2025 Annual Report, Tennessee. Department Of Human Services

Annual Reports

This annual report highlights the accomplishments of the Department of Human Services for the fiscal year.

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Andrew Phang (Gen. Ed.), Pioneer, Polymath And Mentor: The Life And Legacy Of Yong Pung How, Kwan Ho Lau, Bryan Leow Jan 2025

Andrew Phang (Gen. Ed.), Pioneer, Polymath And Mentor: The Life And Legacy Of Yong Pung How, Kwan Ho Lau, Bryan Leow

Research Collection Yong Pung How School Of Law

Many know the story of modern Singapore in broad outline. When the country attained independence in 1965, it was not industrialised and fishing villages still dotted the landscape. By 1990, GDP per capita had risen to around US$13,000, far exceeding its neighbours and on par with Hong Kong and New Zealand. A further three decades on, Singapore today boasts gleaming skyscrapers, well-developed infrastructure and a high degree of trust by residents and investors alike in its government.

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Parallel Ethnic Identity Development Of Mexican-Origin Adolescents And Mothers Under The Influence Of Neighborhood Latinx Concentration And Ethnic-Racial Diversity, Hin Wing Tse, Jinjin Yan, Lester Sim, Minyu Zhang, Wen Wen, Jiaxiu Song, Su Yeong Kim Jan 2025

Parallel Ethnic Identity Development Of Mexican-Origin Adolescents And Mothers Under The Influence Of Neighborhood Latinx Concentration And Ethnic-Racial Diversity, Hin Wing Tse, Jinjin Yan, Lester Sim, Minyu Zhang, Wen Wen, Jiaxiu Song, Su Yeong Kim

Research Collection School of Social Sciences

Co-ethnic or ethnically-racially diverse neighborhoods can serve as safe and supportive places for U.S. immigrant families to explore and develop clarity about their ethnic identity. Although parents undergo concurrent changes in the adaptation process with their children, existing research has predominantly focused on adolescents, with fewer examination on adult parents’ continued ethnic identity development; additionally, researchers also overlook the impact of neighborhood context on ethnic identity in parents. To fill this gap, this registered study used a three-wave longitudinal dataset of 595 Mexican-origin adolescents and their mothers in central Texas. Latent growth models were used to estimate how ethnic identity …

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Exploring The Impact Of Collaboration Processes On Policy Networks Success: A Case Study Of Food Policy Councils, Beomgeun Cho, In Hae Noh, April M. Roggio, Luis Felipe Luna-Reyes Jan 2025

Exploring The Impact Of Collaboration Processes On Policy Networks Success: A Case Study Of Food Policy Councils, Beomgeun Cho, In Hae Noh, April M. Roggio, Luis Felipe Luna-Reyes

Research Collection School of Social Sciences

Innovative forms of collaborative governance have evolved to address a diversity of wicked problems. Collaboration processes involved in these forms of governance appear to have a paradoxical nature, where the necessary inclusiveness and diversity of actors may also be important obstacles for a successful collaboration. We apply theories of collaborative and network governance, and fuzzy-set Qualitative Comparative Analysis, to explore the impact of collaboration process characteristics (network density, diversity, inclusion and participation) on 12 food policy councils. Our findings suggest that collaborative arrangements where diverse stakeholders have equal and inclusive access to active deliberation constitute one path to effective outcomes.

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Human Rights At Sea: Analyzing States' Responses To Cruise Ships During The Covid-19 Pandemic, Lixin Chen Jan 2025

Human Rights At Sea: Analyzing States' Responses To Cruise Ships During The Covid-19 Pandemic, Lixin Chen

Research Collection Yong Pung How School Of Law

In the early stages of the COVID-19 pandemic, the treatment of cruise ships by coastal states was inconsistent, with some ships being allowed to dock while others were not. To that end, this Note focuses on the obligations that a coastal state owes to the individuals onboard the cruise ships in the context of the COVID-19 pandemic, including the rights to life and health. It further considers whether and how such rights are to be balanced with other countervailing considerations of such states, such as the risk of transmission to the local communities. This author concludes with the view that …

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Interactive Effects Of Intrasexual Competitiveness, Same-Sex Competition, And Physical Attractiveness On Temporal Discounting, Jose C. Yong, Indra Alam Syah Aziz, Hualin Xiao, Norman P. Li Jan 2025

Interactive Effects Of Intrasexual Competitiveness, Same-Sex Competition, And Physical Attractiveness On Temporal Discounting, Jose C. Yong, Indra Alam Syah Aziz, Hualin Xiao, Norman P. Li

Research Collection School of Social Sciences

Studies have shown that men discount the future and prefer immediate-but-smaller over delayed-but-larger rewards when exposed to mating opportunities (e.g., attractive opposite-sex targets) or threats (e.g., same-sex competition) whereas women's discounting in response to similar cues appears mixed, suggesting that mating-motivated discounting is primarily a male phenomenon. Importantly, this line of research has not yet examined the role of individual difference variables as well as how the attractiveness of potential mates and perceptions of competition jointly influence discounting rates. We conducted a novel test of the effect of trait intrasexual competitiveness (ISC) using dating profiles varying on target attractiveness and …

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Is Common Behavior Considered Moral? The Role Of Perceived Others' Motives In Moral Norm Inferences And Motivation About Environmental Behavior, Kimin Eom, Bryan Kwok Cheng Choy Jan 2025

Is Common Behavior Considered Moral? The Role Of Perceived Others' Motives In Moral Norm Inferences And Motivation About Environmental Behavior, Kimin Eom, Bryan Kwok Cheng Choy

Research Collection School of Social Sciences

The present research examines how inferences about moral norms from descriptive norms change by perceptions of others' motives in the context of environmental behavior. When individuals think that many others engage in an environmental behavior (e.g., water and energy conservation) for prosocial (vs. proself) motives, they infer moralization about the behavior in a given context. They infer stronger injunctive norms about the behavior and expect others to experience moral outrage at violation of the moral standard (e.g., wasting water and energy). The moral norm perceptions predict people's motivation to engage in environmental behavior themselves. We further show that expected guilt …

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Umbrella Review Of Meta-Analyses On The Risk Factors, Protective Factors, Consequences And Interventions Of Cyberbullying Victimization, K. T. A. Sandeeshwara Kasturiratna, Andree Hartanto, Crystal H. Y. Chen, Eddie M. W. Tong, Nadyanna M. Majeed Jan 2025

Umbrella Review Of Meta-Analyses On The Risk Factors, Protective Factors, Consequences And Interventions Of Cyberbullying Victimization, K. T. A. Sandeeshwara Kasturiratna, Andree Hartanto, Crystal H. Y. Chen, Eddie M. W. Tong, Nadyanna M. Majeed

Research Collection School of Social Sciences

The increasing prevalence of cyberbullying victimization has become a commonplace issue globally. Although research has explored various predictors and consequences of cyberbullying victimization, most focus on a narrow range of variables or contexts, highlighting the need to comprehensively review and synthesize the wealth of empirical findings. We conducted a systematic review of meta-analyses on cyberbullying victimization, incorporating 56 meta-analyses and 296 effect sizes (sample size range 421–1,136,080, sample size median 53,183; searched via EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus, Web of Science, 13 cyberbullying-related journals, Google Scholar and ProQuest Dissertations and Theses) to address the following critical questions: (1) What …

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Understanding Sociability Through Mandevillean Pride: Comments On Robin Douglass's Mandeville's Fable, Antong Liu Jan 2025

Understanding Sociability Through Mandevillean Pride: Comments On Robin Douglass's Mandeville's Fable, Antong Liu

Research Collection School of Social Sciences

There is ‘a tendency to evaluate philosophical theories of human nature as more accurate or adequate when they are more attractive or flattering, not when they are more truthful,’ claim two contemporary moral psychologists (Flanagan and Hu 2011: 312). While this tendency may not be as widely shared as these scholars expect, the flip side of their claim indeed captures a factual truth: philosophers seldom avoid attacks on their theories and even their personalities when they insiston exposing the dark side of human nature. Indeed, not all philosophers attacked for this reasonlose their popularity, as numerous readers of Machiavelli and …

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Through The Looking Glass: We All See Ideational Objects Here, J.G. Allen Jan 2025

Through The Looking Glass: We All See Ideational Objects Here, J.G. Allen

Research Collection Yong Pung How School Of Law

In this chapter, I explore the property law treatment of cryptoassets—and, presumably, other digital artefacts that are not clearly rights to anything or against anyone. I argue that such artefacts are well described as “ideational objects” and draw together insights from private law theory and social ontology to explore how we should think about complex objects with a social and a technical dimension. I then examine how the law of property can accommodate ideational objects as objects of property rights (including the right of ownership) and dealing such as transfer, and what challenges and implications this poses for the traditional …

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No Consistent Evidence For Between- And Within-Person Associations Between Objective Social Media Screen Time And Body Image Dissatisfaction: Insights From A Daily Diary Study, Adalia Y. H. Goh, Andree Hartanto, K. T. A. Sandeeshwara Kasturiratna, Nadyanna M. Majeed Jan 2025

No Consistent Evidence For Between- And Within-Person Associations Between Objective Social Media Screen Time And Body Image Dissatisfaction: Insights From A Daily Diary Study, Adalia Y. H. Goh, Andree Hartanto, K. T. A. Sandeeshwara Kasturiratna, Nadyanna M. Majeed

Research Collection School of Social Sciences

With the abundance of social media content that promotes unrealistic beauty standards, there are growing concerns about the potential negative impact of social media use on body image satisfaction. While some studies highlight negative associations, others present null effects, pointing to methodological limitations like biased and unreliable self-reported screen time measures and a focus on singular platforms. Addressing these gaps, our study employed a daily diary method to objectively measure social media screen time across six major platforms (Twitter, Reddit, TikTok, YouTube, Instagram, and Facebook), alongside daily body image dissatisfaction among 252 young adults (Mage = 21.67 years, 67.77% female) …

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2025 Program, Georgia Southern University Jan 2025

2025 Program, Georgia Southern University

Georgia Educational Research Association Conference

2025 Schedule

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