Computer Sciences Commons^™

A Review Of Chinese Sentiment Analysis: Subjects, Methods, And Trends, Zhaoxia Wang, Donghao Huang, Jingfeng Cui, Xinyue Zhang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis has emerged as a prominent research domain within the realm of natural language processing, garnering increasing attention and a growing body of literature. While numerous literature reviews have examined sentiment analysis techniques, methods, topics and applications, there remains a gap in the literature concerning thematic trends and research methodologies in sentiment analysis, particularly in the context of Chinese text. This study addresses this gap by presenting a comprehensive survey dedicated to the progression of research subjects, methods and trends in sentiment analysis of Chinese text. Employing a framework that combines keyword co-occurrence analysis with a sophisticated community detection …

Charge Your Clients: Payable Secure Computation And Its Applications, Cong Zhang, Liqiang Peng, Weiran Liu, Shuaishuai Li, Meng Hao, Lei Zhang, Dongdai Lin

Research Collection School Of Computing and Information Systems

The online realm has witnessed a surge in the buying and selling of data, prompting the emergence of dedicated data marketplaces. These platforms cater to servers (sellers), enabling them to set prices for access to their data, and clients (buyers), who can subsequently purchase these data, thereby streamlining and facilitating such transactions. However, the current data market is primarily confronted with the following issues. Firstly, they fail to protect client privacy, presupposing that clients submit their queries in plaintext. Secondly, these models are susceptible to being impacted by malicious client behavior, for example, enabling clients to potentially engage in arbitrage …

Symp25s: A Multi-View Feature Construction And Multi-Encoder-Decoder Transformer Architecture For Time Series Classification, Zihan Li, Wei Ding, Inal Mashukov, Ping Chen, Scott Crouter

Paul English Applied Artificial Intelligence (AI) Institute Publications

Time series data plays a significant role in many research fields since it can record and disclose the dynamic trends of a phenomenon with a sequence of ordered data points. Time series data is dynamic, of variable length, and often contains complex patterns, which makes its analysis challenging especially when the amount of data is limited. In this paper, we propose a multi-view feature construction approach that can generate multiple feature sets of different resolutions from a single dataset and produce a fixed-length representation of variable-length time series data. Furthermore, we propose a multi- encoder-decoder Transformer (MEDT) architecture to effectively …

Weakly-Supervised Semantic Segmentation With Image-Level Labels: From Traditional Models To Foundation Models, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

The rapid development of deep learning has driven significant progress in image semantic segmentation—a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time consuming, and labor intensive. Weakly supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling. It utilizes only partial or incomplete annotations and provides a cost-effective alternative to fully supervised semantic segmentation. In this article, our focus is on the WSSS with image-level labels, which is the most challenging form of WSSS. Our work has two parts. First, we conduct …

Agentic Ai-Enhanced Virtual Reality For Adaptive Immersive Learning Environments, Indra Kishor, Udit Mamodiya, Mohammed Almaayah, Amer Alqutaish, Rami Shehab, Theyazn H. H. Aldhyani

Mesopotamian Journal of Computer Science

Immersive learning using Virtual Reality (VR) has gained prominence for delivering experiential, engaging education. However, most VR learning environments lack real-time adaptability, personalization, and cognitive responsiveness. This study presents an Agentic AI-enabled VR framework that autonomously adjusts pedagogical content, interaction style, and challenge level based on learner behavior, emotions, and performance feedback. The proposed system integrates a reinforcement learning-based agent with a virtual reality module to form an intelligent tutor capable of independent decision-making. A neuro-symbolic model processes multi-modal feedback (gesture, speech, gaze, performance) to determine context-aware pedagogical strategies. The system employs a self-evolving curriculum logic that adapts in real …

Lightweight Deep Reinforcement Learning Model For Energy-Efficient Resource Allocation In Edge Computing, Ghassan A. Abed, Mohanad A. Al-Askari

Mesopotamian Journal of Computer Science

A lightweight digital twin model for a single 6G cell operating in the D-band (140 GHz) with a 1 GHz bandwidth is presented in this work with the goal of assessing the cell's capacity, coverage, and terminal time in order to support extended reality (XR) applications. With a tangent dispersion of 3 dB and a path exponent of n = 2.2, the model is based on the free-space loss equation as per ITU-R Recommendation P.525. The instantaneous capacity is determined using the Shannon-Hartley theorem. Three XR sessions are created every minute using a Poisson method, and their durations are determined …

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.

Approach For Detecting Face Morphing Attacks Using Convolution Neural Network, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi

Mesopotamian Journal of Computer Science

The facial morphing method combines at least two images of the face to get a singular altered facial image that exposes the vulnerabilities of face recognition systems (FRS). The extensive implementation of face recognition algorithms, particularly in Automatic Border Control (ABC) systems, has raised apprehensions over potential threats, as modified passports present significant risks to national security. In this paper, a new face morphing attack detection approach has been proposed using two different datasets (StyleGAN and AMSL) for testing and validation. A new model for face morphing attack detection based on a special Convolutional Neural Networks (CNNs) architecture has been …

Securing The Internet Of Wetland Things (Iowt) Using Machine And Deep Learning Methods: A Survey, Guma Ali, Wamusi Robert, Maad M. Mijwil, Malik Sallam, Jenan Ayad

Mesopotamian Journal of Computer Science

Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The …

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 …

Anila: Adaptive Neuro-Inspired Learning Algorithm For Efficient Machine Learning, Ai Optimization, And Healthcare Enhancement, Ismael Khaleel, Wijdan Noaman Marzoog, Ghada Al-Kateb

Mesopotamian Journal of Computer Science

The Adaptive Neuro-Inspired Learning Algorithm (ANILA) offers a breakthrough in the realm of machine learning by drawing inspiration from the biological processes of the human brain. Developed to address limitations in conventional models such as CNNs and RNNs, ANILA enhances real-time responsiveness, energy efficiency, and system adaptability. By emulating neurobiological behaviors particularly sparse coding and synaptic plasticity ANILA allows systems to process data dynamically, adjust to novel inputs without retraining, and scale effectively across environments like IoT and healthcare diagnostics. Performance evaluations highlight significant reductions in latency, increases in energy efficiency (up to 92%), and exceptional adaptability to changing data …

Real-Time Sdn–Iot Integrated Framework For Intelligent Emergency Vehicle Prioritization In Smart Cities, Sura F. Ismail

Mesopotamian Journal of Computer Science

Urban traffic control has become increasingly complex with rising vehicle density, particularly in smart cities. Timely arrival of emergency vehicles is critical, yet existing systems relying on manual transmitters and sirens offer limited range and effectiveness. This paper proposes a real-time intelligent traffic management framework integrating Software-Defined Networking (SDN), Internet of Things (IoT) technologies, and the Edge of Things (EoT)—a paradigm combining edge computing with IoT to enable low-latency processing at the network edge. The framework connects the SUMO traffic simulator and Veins vehicular network framework via TraCI, with the RYU SDN controller dynamically adjusting traffic signals and vehicle routes …

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 …

Synthesizing Deception: Countering Large Language Model-Generated Phishing Campaigns Through Adaptive Semantic Anomaly Detection, Bekim Fetaji, Debabrata Samanta

Mesopotamian Journal of Computer Science

The paper fills in a gap in the literature that demonstrates an insufficient number of sturdy detection schemes that can recognize the small semantic aberrations inherent in LLM-generated deceptive text. Our proposed co-design hybrid model is Semantic Anomaly Detection with Isolation Forest (SADI) model that combines the synergistic mixture of a fine-tuned transformer-based LLM for deep semantic feature extraction with Isolation Forest algorithm that detects anomalies efficiently. This study introduces SADI, an adaptive semantic-anomaly detector for large-language-model phishing emails. Using a corpus of 10 000 messages, SADI attains an F1 score of 0.981 (95 % CI 0.978–0.984) and processes a …

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 …

Development And Construction Of New Scanning Antennas With High Remote Sensing Capability For Wireless Communication Systems In The Millimeter Wavelength Range, Nagham Habeeb Shakir, Sarah R. Hashim, Ahmed Sileh Gifal, Ahmed Dheyaa Radhi, Alaa G.K. Alshami, Rusul Mansoor Alamri, Hussein Mohammed Ali

Mesopotamian Journal of Computer Science

In this paper, geometric methods and wave optics were used to calculate the basic profiles and characteristics of the lens antennas.  The planar reflector arrays were assembled using an iterative method with multiple forward and inverse Fourier transform calculations.  Three-dimensional electromagnetic modeling was performed in CST Microwave Studio software to evaluate the technical parameters of the designed antennas. Measurements of the characteristics of fabricated prototypes of far-field scanning antennas were performed using a custom-designed experimental setup. This study focuses on the analysis and development of scanning antennas for use in millimeter-wave wireless communication systems. The researchers aim to develop a …

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 …

Haze-Image-Dataset: A Large-Scale Benchmark For Image Dehazing In Variable Fog And Low-Light Conditions, Mustafa J. Shahbaz, Ali A.D. Al-Zuky

Mesopotamian Journal of Computer Science

To enhance image dehazing and visual recognition in real-world conditions, we introduce HAZE-IMAGE-DATASET, a large-scale dataset comprising nearly 42,000 images. It is constructed from 1,532 clean images sourced globally and captured using a Samsung smartphone, covering diverse natural and urban scenes. The dataset includes synthetic and real haze variations. Synthetic haze was generated using MATLAB-based atmospheric scattering models with depth maps for 10 fog levels. Colored haze was created using alpha blending (α = 0.4) in six colors: red, green, blue, yellow, white, and black. Low-light conditions were simulated via uniform darkening at 10 levels. Also, 616 real haze images …

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 …

Enhanced Iot Cyber-Attack Detection Using Grey Wolf Optimized Feature Selection And Adaptive Smote, Sura Abed Sarab Hussien, Mustafa S. Ibrahim Alsumaidaie, Nada Hussein M. Ali

Mesopotamian Journal of Computer Science

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrate that GWO reduces features from 32 …

Heuristic Approaches For Coordination Of Heterogeneous Robotic Systems In Harvesting Automation With Size Constraints, Hyeseon Lee

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of path planning algorithms for the coordination of heterogeneous robotic systems while considering size constraints. The objective is to generate practical and efficient solutions for real-world applications. The use of heterogeneous collaborative robots is beneficial in many applications, such as transportation operations in warehouses or manufacturing environments, surveillance, and monitoring, and task allocation and path planning are critical techniques that need to be addressed to deploy in real-world applications. This research focuses on automating lavender harvesting, where robots with varying capabilities must collaboratively navigate complex field layouts to efficiently complete harvesting tasks.

The problem considers …

Developing A Graphql Mesh Federated Api Gateway: Rapid Integration Of New Endpoints Into A Predefined Schema, Noah Kolczynski

Dissertations, Master's Theses and Master's Reports

The Navy’s Undersea Warfare Decision Support System (USW-DSS) uses data from an ever growing number of sensors, accessible through an equally growing number of Application Programming Interfaces (APIs). Due to the lack of standardization among these sensors and APIs, as the system has continued to grow, the challenge of collecting and using these data has become increasingly prevalent. Previous work at Michigan Tech, in collaboration with engineers at ARiA (Applied Research in Acoustics LLC), introduced a GraphQL Mesh federated API gateway. The gateway would enable the combination of diverse API sources into a predefined hierarchical structure. This report follows the …

Uso Da Modelagem Baseada Em Agentes No Estudo De Sistemas Complexos, Eric Araújo

University Faculty Publications and Creative Works

A modelagem baseada em agentes (MBA) é uma metodologia poderosa e acessível para explorar sistemas complexos, onde interações simples entre indivíduos podem gerar comportamentos coletivos emergentes. Este artigo apresenta a MBA de maneira didática e fluida, utilizando a interface NetLogo para exemplificar como a metodologia pode ser aplicada em diversas áreas, como ecologia, saúde pública, economia e sociologia. Com uma abordagem prática, mostramos que não é necessário um conhecimento avançado em computação para começar a usar a MBA, mas que sua versatilidade permite investigar questões complexas do mundo real. Ao final, o leitor será capaz de entender os fundamentos da …

Optimization Of Serum And Salivary Cortisol Interpolation For Time-Dependent Modeling Frameworks In Healthy Adult Males, Nathaniel T. Berry, Travis Anderson, Christopher K. Rhea, Laurie Wideman

Rehabilitation Sciences Faculty Publications

Cortisol is an important marker of hypothalamic-pituitary-adrenal function and follows robust circadian and diurnal rhythms. However, biomarker sampling protocols can be labor-intensive and cost-prohibitive. Objectives: Explore analytical approaches that can handle differing biological sampling frequencies to maximize these data in more detailed and time-dependent analyses. Methods: Healthy adult males [N = 8; 26.1 (±3.1) years; 176.4 (±8.6) cm; 73.1 (±12.0) kg)] completed two 24 h admissions: one at rest and one including a high-intensity exercise session on the cycle ergometer. Serum and salivary cortisol were sampled every 60 and 120 min, respectively. Six alternative sampling profiles were defined by downsampling …

Clinician Perspectives On Virtual Reality Use In Physical Therapy Practice In The United States, Danielle T. Felsberg, Jared T. Mcguirt, Scott E. Ross, Louisa D. Raisbeck, Charlend K. Howard, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

The primary goal of physical rehabilitation is to assess movement impairments and restore function to improve overall quality of life. Virtual reality (VR) may provide the optimal environment to promote these goals due to its motivating and modifiable nature which can be difficult to accomplish through traditional real-world therapeutic methods. Current research of VR for rehabilitation has demonstrated that VR interventions can produce clinically meaningful change in motor outcomes. Despite this, adoption and usage of VR by physical therapy professionals is unclear due to the limited research in this area. Thus, the purpose of this study was to identify the …

Nexus: Network Exploration For Exploiting Unsafe Sequences In Multi-Turn Llm Jailbreaks, Javad Rafiei Asl, Sidhant Narula, Mohammad Ghasemigol, Eduardo Blanco, Daniel Takabi

School of Cybersecurity Faculty Publications

Large Language Models (LLMs) have revolutionized natural language processing, yet remain vulnerable to jailbreak attacks—particularly multi-turn jailbreaks that distribute malicious intent across benign exchanges, thereby bypassing alignment mechanisms. Existing approaches often suffer from limited exploration of the adversarial space, rely on hand-crafted heuristics, or lack systematic query refinement. We propose NEXUS (Network Exploration for eXploiting Unsafe Sequences), a modular framework for constructing, refining, and executing optimized multi-turn attacks. NEXUS comprises: (1) ThoughtNet, which hierarchically expands a harmful intent into a structured semantic network of topics, entities, and query chains; (2) a feedback-driven Simulator that iteratively refines and prunes these chains …

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem

School of Cybersecurity Faculty Publications

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random …

Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach, Ali Parsa, Neda Moghim, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …