Digital Commons Network^™

Tinyvgg-Based Real-Time Degradation Classification For Adverse Driving Scenes Using A Newly Collected Iraqi Driving Dataset, Yousif N. Abbas, Matheel E. Abdulmunim, Nada H. Ali, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Environmental conditions such as low-light at night, fog scattering, glare artifacts, rain streaks, and rain smear distortions are significant issues of camera-based perception in Autonomous Vehicles (AVs). These degradations alter the statistics of the scene, mask structure, introduce non-uniform noise, and adversely affect downstream vision processes, including detection and tracking. To overcome this shortcoming, this paper presents a lightweight TinyVGG-based degradation classification system that runs in real time. The network extracts discriminative spatial features with hierarchical convolutional encoding and projects them to a lower-dimensional semantic representation with fully connected layers and a multi-class predictor based on SoftMax. In addition, a …

Improving Approach Of Evolutionary Strategies For Clustering Technique Enhancement, Duaa Mahde Saleh, Hasanen S. Abdullah, Ahmad Zamsuri

Journal of Soft Computing and Computer Applications

The existence of the information has been the essential aspect of the whole society. Information is concentrated in all forms to be effectively utilized. Clustering — an unsupervised learning technique. It is based on data similarity that gives rise to issues in collection, challenges and instability in data structure. It proposes an advanced evolutionary method by combining two approaches. Firstly, it adopts the evolutionary approach and integrates the advantages between two methods to design one. Among them are Differential Evolution (DE) and Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP), and Evolutionary Programming (EP) and Particle Swarm Optimization …

Comparative Study On Throughput Optimization In Nfv: Traditional Dissemination Techniques Vs. Swarm Intelligence Approaches, Sanaa Salih Alwan, Asia Ali Salman, Wulfrano Arturo Luna Ramírez

Journal of Soft Computing and Computer Applications

Network Functions Virtualization (NFV) modernizes networks by replacing hardware with software, creating a more flexible network architecture and offering flexibility in dynamic network environments. This foundational technology is essential for creating the networks of the future, including the Internet of Things (IoT) and cellular services. NFV does provide flexibility, but it struggles to maintain system throughput during high traffic loads while achieving high resource utilization efficiency and dynamic packet routing. The problem lies in the fact that traditional request distribution mechanisms, such as flooding and gossip, fail to operate efficiently in complex network topologies (scale-free networks), leading to: (a) random …

A Comprehensive Review Of The A* Algorithm: Evolution, Applications, And Future Trends In Path Planning, Saleel H. Abood, Hussein M. H. Al-Khafaji, Mohanned M. H. Al-Khafaji

Journal of Soft Computing and Computer Applications

Despite being a fundamental problem to autonomous robotics and intelligent navigation systems, path planning is still a challenge. The A^* algorithm is often used among search-based techniques for optimal search performance, as it's a tradeoff of computation. The above techniques have been developed for various applications as many versions of A^* Dynamic A^* (D^*), D^* Lite, Hybrid A^*, and Anytime A^* are suggested to deal with dynamic environments, real-time constraints, and kinematic restrictions. This paper comprehensively and structurally reviews the A^* algorithm and its major extensions, encompassing historical development, methodological …

Skin Lesion Classification Using Cnn Model And Augmented Dataset, Mohammed Nawzad Mohammed-Ramzi, Aso M. Aladdin

Journal of Soft Computing and Computer Applications

Skin cancer is a deadly disease. Skin lesion classification is a critical challenge due to its prevalent and deadly nature. Skin lesions are difficult for dermatologists to detect using eye examination, which is time-consuming and variable. A deep learning model of skin lesions classification has been proposed using a Convolutional Neural Network (CNN) trained on the HAM10000 dataset of 10,015 dermatoscopies. To improve resilience and address the dataset's extreme class imbalance, data augmentation techniques such as geometric transformations, brightness/contrast adjustments, blurring, noise addition, histogram equalization, color space alterations, and elastic deformations are used. With a carefully balanced 10% test set, …

Direct Minimization Of Hartree-Fock-Roothaan Energy Functionals Using Derivative-Free Optimization Algorithms: A Case Study On The Use Of Noninteger Slater-Type Orbitals, Ali̇ Bağci

Turkish Journal of Physics

This study presents an evaluation of derivative-free optimization algorithms for the direct minimization of Hartree−Fock−Roothaan energy functionals involving nonlinear orbital parameters and noninteger-order quantum numbers. The analysis focuses on atomic calculations employing noninteger Slater-type orbitals. Analytic derivatives of the energy functional are not readily available for these orbitals. Four methods are investigated under identical numerical conditions: Powell’s conjugate−direction method, the Nelder−Mead simplex algorithm, coordinate-based pattern search, and a model-based algorithm utilizing radial basis functions for surrogate-model construction. Performance analysis is first carried out using the Powell singular function, a well-established test case exhibiting challenging properties, including Hessian singularity at the …

Multi-Frequency Associative Memory For Continual Graph Learning Through Nested Optimization, Shuvam Kundu

Master’s Dissertations

Graph Neural Networks struggle to learn new tasks without forgetting old ones a problem known as catastrophic forgetting. In graph domains, this is compounded by structural shift, where newly added edges corrupt the learned representations of historical nodes even when model weights remain unchanged. We present CAM-Titans, a continual graph learning framework built around a two-buffer associative memory to address both parametric and structural forgetting. Our architecture operates across three timescales of adaptation: a slow base memory updated via ordinary gradient descent, an intermediate task buffer re-encoded after every task using the delta-rule, and a transient in-context state for rapid …

Pinnlab: An Interactive Dashboard For Teaching Data-Driven Parameter Estimation In Differential Equations Using Physics-Informed Neural Networks, Mohan J. Parthasarathy, Padmanabhan Seshaiyer

CODEE Journal

Undergraduate instruction in ordinary differential equations (ODEs) is typically organized around the forward problem: finding solution trajectories when the governing equation and its parameters are known. In scientific practice, however, inverse problems are often more relevant, requiring unknown parameters to be inferred from noisy observations while assessing whether a proposed model is consistent with the data. We introduce PINNLab, an open-source MATLAB dashboard designed to help undergraduate students explore inverse modeling through physics-informed neural networks (PINNs). PINNLab presents PINNs as a complementary data-driven framework that connects differential equations, optimization, empirical data, and scientific machine learning. The instructional sequence is organized …

Learning To See Lesions, Not Skin Tone: Counterfactual Multimodal Learning For Fair, Trustworthy, And Text-Free Dermatology Ai, Shivam Jangid

Master’s Dissertations

Recent advances in deep learning have significantly improved the performance of automated skin lesion classification systems, enabling accurate detection of various dermatological conditions from medical images. Despite these achievements, concerns regarding fairness and generalization remain a major challenge for the deployment of such systems in real-world clinical settings. A key factor contributing to these challenges is the presence of bias in training datasets, particularly with respect to skin-tone representation. Most publicly available skin lesion datasets contain a disproportionate number of samples from individuals with lighter skin tones. As a result, deep learning models trained on these datasets often learn representations …

Adaptive Spectral Trust Gate For Physics- Constrained Operator Learning, Soham Chakraborty

Master’s Dissertations

Physics-informed machine learning improves the plausibility, data-efficiency and generalization of surrogate models by injecting prior physical knowledge into the learning process. The current approaches can be broadly divided into two main categories: soft constraints, which add a physics residual to the training loss but guarantee nothing at inference time, and hard constraints, which project the model output onto the constraint set exactly but apply the projection uniformly to every part of the signal — including parts that are dominated by noise, discretization error, or model mismatch, where the idealized physics is not actually trustworthy. This dissertation proposes the Adaptive Spectral …

Deep Reinforcement Learning With Directed Asymmetry And Kolmogorov-Arnold Networks For Dismantling Interdependent Multiplex Networks, Soumyajit Dev

Master’s Dissertations

Identifying the minimum-cost node-removal sequence that fragments a complex network - the network dismantling problem is NP-hard and central to infrastructure resilience. In interdependent multiplex networks, this difficulty is compounded by cascading cross-layer failures. While deep reinforcement learning (DRL) agents utilizing graph neural network (GNN) encoders achieve near-optimal dismantling, current state-of-the-art architectures suffer from two critical limitations. Topologically, existing agents strictly assume undirected edges, rendering them inapplicable to directed systems - such as supply chains or gene regulatory cascades - where failure propagation is fundamentally asymmetric. To resolve this, we propose Disassembling Directed Interdependent Networks (DDIN). DDIN introduces an asymmetric …

New Studies In Lattice-Based Cryptography, Quantum Algorithms, And Privacy-Preserving Computation, Hansraj Jangir

Electronic Theses and Dissertations 2020 - Present

Classical public-key cryptographic schemes primarily rely on the presumed hardness of the integer factorization and discrete logarithm problems. However, Shor’s algorithm demonstrates that both problems can be solved efficiently on a sufficiently powerful quantum computer. This breakthrough motivated the search for quantum-resistant hard assumptions. The assumptions based on lattices are one of the primary candidates in post-quantum cryptography due to their strong theoretical foundations and well-established hardness against both classical and quantum attacks. In this thesis, we explore several directions of lattice based cryptology and quantum algorithms.

On the construction side, we propose two compact encryption schemes based on the …

A System For The Prediction Of Election Results Using Vader And Hybridized Machine Learning Model, Abraham E. Evwiekpaefe, Khadijah Kabir, Georgina N. Obunadike

Tanzania Journal of Science

Integrating different classifiers along with sentiment lexicons like Vader, can enhance the performance of sentiment analysis systems. However, such a hybrid model remains underexplored, particularly in the context of regional elections in developing countries like Nigeria. The aim of this research is to develop a hybrid model that combines three machine learning classifiers and Vader lexicon to possibly achieve a higher accuracy. A case study of the 2023 governorship election in Kogi, Bayelsa and Imo State, Nigeria was examined. Twitter API library was utilized to extracted public and personal tweets using hashtags and keywords related to the target data from …

Robust Real-Time Uav Target Tracking With Onboard Vision-Based Yaw Control, Rylan Malarchick, Jose Castelblanco, Enrique Amaya, Carmen Dimario, Graysen Brinkman, Chirag Kumar, Kiwon Yoon, Sajid Berhane

Beyond: Undergraduate Research Journal

Autonomous tracking of agile unmanned aerial vehicles (UAVs) presents significant challenges for real-time perception and control systems. This work presents AIRHOUND (Autonomous Intelligent Rotorcraft for Hostile Object Unified Navigation and Detection), a UAV platform implementing vision-based yaw tracking through a modular ROS2 software architecture. The system employs YOLOv8 object detection optimized with NVIDIA TensorRT for embedded deployment on an NVIDIA Jetson Orin companion computer. Detected targets are processed through a geometric tracking module that converts pixel coordinates to angular yaw errors using pinhole camera intrinsics, with a proportional controller generating rate-limited yaw commands. These commands are streamed to a PX4 …

Effective Training For International Students, G W. Thomas

IGC Proceedings (1977-2023)

The observations made in this paper are based upon contacts with international students, both in the U.S. and in many other countries. Two of the most serious problems in increasing world food production in the developing world are (1) lack of trained people, and (2) technology transfer. A third constraint, inadequate research, is also critical but not of the magnitude of the other two, since there is a great amount of research information available that is not being adequately ut:ilized. Training needs fall into two general categories in which deficiencies are obvious: (1) graduate-level programs, particularly in the applied fields …

The Dynamics Of Educational Change: A Complex Systems Perspective, Preethi Nanjundan, Lijo Thomas, Abith K. Sunil

Northeast Journal of Complex Systems (NEJCS)

The perception of university teachers toward educational reforms plays an important role in determining the success of changes introduced in the education sector. This study focuses on teachers’ attitudes toward change, their emotional responses, and their overall views on educational reforms. Across the world, many educational reforms have failed to achieve their expected outcomes in improving teaching practices and student learning. As education systems are highly complex, the approach toward implementing reforms has also changed over time. Some reforms are introduced gradually, while others involve major innovations within the system. Complexity theory provides useful insights and tools that help educators …

A Switch-Point-Aware Contrastive Approach To Sentiment Analysis Of Hinglish Code-Mixed Text, Prasant Kumar Sahoo

Master’s Dissertations

With the increasing use of social media in non-English-speaking regions, especially in India, people often use Romanized Hindi and English together in their online communication. In a single sentence, they frequently mix Romanized Hindi and English, creating code-mixed text. However, most multilingual transformer models are pre-trained primarily on monolingual data. As a result, NLP systems face challenges when processing code-mixed text, as a single word may be fragmented into meaningless subword pieces, making it difficult for the model to capture its semantic meaning accurately. In this dissertation, we propose a parameter efficient neural architecture consisting of three main components to …

Extending Unibreak: Semantic Retrieval And Harmful-Intent Direction Suppression For Token-Level Llm Jailbreaking, Sanket Saha

Master’s Dissertations

Token-level adversarial perturbations remain one of the most efficient known attacks against the safety alignment of instruction-tuned large language models (LLMs). Among recent works, the UniBreak framework (You et al., 2026) stands out for unifying gradient-based optimization with an evolutionary perturbation repository. However, its repository relies solely on accumulated success frequency without utilizing query content, and its fitness function implicitly assumes that suppressing refusal tokens is sufficient to elicit harmful responses. In this dissertation, we extend UniBreak along both axes and re-evaluates the framework under stricter generalization and judgment protocols. Specifically, we introduce a semantic perturbation repository that replaces frequency-only …

Dynamic Governance Of Transboundary Fisheries Through Deep Learning And High-Frequency Vessel Tracking Data For Climate-Resilient Management: An Application To The East China Sea, Qin Lin, Yaqing Shu, Manel Grifoll, Hongxiang Feng

Journal of Marine Science and Technology–Taiwan

Transboundary fisheries governance is increasingly challenged by jurisdictional conflicts, regulatory asymmetries, and climate-induced stock shifts in fish stocks, especially in contested regions like the East China Sea (ECS). This study capitalizes on China’s high-frequency BeiDou Navigation Satellite System (BDS) data (June 2021–May 2022) to pioneer a CNN-BiLSTM deep learning model to dynamically identify fishing hotspots and evaluate policy effectiveness. By integrating vessel trajectories, environmental variables, and regulatory events, we examine spatiotemporal patterns of fishing effort relative to seasonal bans, extreme weather, and quota systems. Key findings reveal: (1) post-moratorium (September–December) concentration of fishing intensity, peaking in November; (2) significant weather-driven …

Face-To-Face Versus Distance Education In Competence-Based Maritime Education And Training, Clara Borén, Marcella Castells-Sanabra, Manel Grifoll

Journal of Marine Science and Technology–Taiwan

The COVID-19 pandemic created significant challenges for higher education, with universities being shut down and face-to-face teaching and assessment shifting to an online format. This presented an opportunity to focus on the continuity of learning through distance education, especially in the maritime context, due to the on-board training requirements, and even though five years have passed, this shift has reshaped the education landscape, proving that remote learning has come to stay.

At the Barcelona School of Nautical Studies, we have developed a weather routing software in the framework of distance education and teaching innovation. We conducted a teaching trial alternatively …

(Re)Claiming Homeplace: Dance As Embodied Resistance & Healing For Health & Spatial Justice, Preeya G. Kannan

University Honors Theses

This thesis argues that dance functions as both an upstream public health intervention and a form of radical spatial resistance; creating healing, agency, and belonging among historically marginalized communities. Drawing on bell hooks' concept of homeplace, Jasbir Puar's assemblage theory, Nancy Krieger's eco-social theory, and Katherine McKittrick's Black geographies, I position the body as both an ecological and political landscape shaped by histories of colonialism, racial capitalism, displacement, and resilience.

Through this community-based participatory research study, Homeplace, I explore dance as a form of social prescription that centers liberation rather than pathology. Movement is often reduced in public health discourse …

Assessing Spatial Distribution Of Plausible Earthquake-Triggered Landslides Relative To Ca. 1000 Ybp Earthquake On The Gales Creek Fault, Jacqueline Rheingans

University Honors Theses

Modern coseismic landslides cluster near the surface trace of the fault that ruptured, suggesting that clustering of paleolandslides may record information about paleoearthquakes. This study documents the pattern of prehistoric landslides near the Gales Creek Fault (GCF), northwest Oregon, to assess whether it reflects an independently known paleoearthquake. The GCF is located northwest of Portland through the Cascadia fore-arc basin where landslides are often triggered by intense rainfall during the wet season or potentially by seismic shaking on the megathrust or on crustal faults. In order to understand how a seismic event on the GCF will impact landslide distribution I …

Custom Sbc Gps Tracking And Geocaching Carputer Software Development And Implementation, Joshua A. Davis

University Honors Theses

This thesis argues that hardware-integrated capstone projects develop software engineering skills that traditional coursework cannot replicate. A team of eight developers built a GPS tracking system on a Raspberry Pi 4 over two academic terms, integrating real-time position streaming, the APRS amateur radio protocol for network-independent location sharing, and PostGIS spatial queries for "new road" detection. The system implements SmartBeaconing for adaptive GPS data reduction, achieving approximately 80% storage savings while preserving route fidelity. The project exposed challenges absent from classroom assignments: hardware debugging without stack traces, cross-layer integration failures, and coordination overhead when deploying to unfamiliar architecture; demonstrating that …

Effects Of Changing Fire Regimes On Black Huckleberry Habitat Suitability, Emily Labarge

University Honors Theses

Black huckleberries are a significant cultural, ecological, and economic species in the Pacific Northwest. Historically, black huckleberries were tended with cultural fire by Indigenous peoples, and now also hold importance as a resource in the non-timber forest product economy. Research has begun on the impacts of climate change on suitable black huckleberry habitat and on changing fire regimes, but not in relation to each other. To address this gap, I examined the area of overlap between suitable black huckleberry habitat and fire burn probability into mid-century climate projections. I determined the current ownership entities with management jurisdiction in areas with …

Synthetic Data Generation For Healthcare Machine Learning: A Case Study In Vital Signs And Diagnostic Predictions, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung

Journal of Informatics and Web Engineering

Healthcare Machine Learning (ML) applications face significant challenges in accessing high-quality training data due to stringent privacy regulations, institutional data silos, and concerns over patient confidentiality. This paper explores synthetic data generation as a viable and privacy-preserving alternative to real patient data for developing ML models in healthcare settings. We present techniques for generating realistic vital signs data including body temperature, blood pressure, heart rate, respiratory rate, and oxygen saturation according to appropriate statistical distributions. In addition, we demonstrate how synthetic datasets generated can be used to train diagnostic prediction models. The generated datasets were applied to multiple diagnostic prediction …

Ai-Powered File Security System With Facial Biometrics, Qr Code, And Otp Verification, Xinyi Loi, Ming Man Chan, Yuk Yii Chan

Journal of Informatics and Web Engineering

Commonly used file-sharing systems suffer from impermissible security risks such as impersonation, credential theft, and QR code exploitation, which can expose sensitive information. This paper describes and evaluates an AI-driven secure file-sharing system designed to alleviate these risks through a multi-layered authentication strategy. The complete system involves AES-128 encryption, unique QR code referencing, live facial recognition, and One-Time Password (OTP) confirmation. In our proposed workflow, the sender encrypts a reference to the file and links it to facial data of the recipient, if applicable, and embeds this information into a QR code, that is dynamically generated during the send operation. …

An Integrated Yolov8 Based System For Real Time Vehicle Tracking And Identification Using Cctv Footage, Riya Jain, Piyush Kumar, Ashwani Kumar Dubey, Angela Amphawan, Neo Tse Kian

Journal of Informatics and Web Engineering

Vehicle identification and tracking play a critical role in security and surveillance, supporting the efforts of law enforcing authorities to prevent crime, monitor traffic, and enforce the vehicle rules and regulations. This research presents a proof-of-concept system that employs YOLOv8 for real time vehicle detection and car type classification using live or recorded CCTV footage. YOLOv8 was chosen after rigorous comparison, it was chosen for its ease of use and efficient image training capabilities, making it well suited for rapid prototyping that the concept needed. The system is designed to extract key vehicle features, including colour, model of the vehicle, …

Enhanced Trust-Based Security Mechanism To Prevent Temporal Dos Vulnerabilities In Ipv6 Link-Local Networks, Iznan Husainy Hasbullah, Lokman Mohd Fadzil, Selvakumar Manickam, Supriyanto Praptodiyono, Mohamad Khairi Ishak

Journal of Informatics and Web Engineering

Many computer networks in operation today currently use both IPv4 and IPv6 stacks. On the other hand, there is a transition towards IPv6-only networks as a result of the limited availability of IPv4 addresses. The primary protocol for link-local IPv6 communication is the Neighbor Discovery Protocol (NDP). Regrettably, its insecure design and basic scope-based security mechanisms make the local network susceptible to insider threats. The Internet Engineering Task Force’s recommended security mechanism for NDP, which is Secure Neighbor Discovery (SEND), is well documented but complex and unsuitable for resource-constrained devices and networks. Trust-ND was positioned as an alternative to SEND …

Machine Learning For Employee Attrition Prediction: A Comparative Study, Kai Jia Low, Sook Ling Lew, Sri Winarno

Journal of Informatics and Web Engineering

Employee turnover remains a long-standing issue that can lead to significant economic losses and a decline in workplace productivity. Traditional methods for predicting natural attrition, such as surveys and rumour assessment, usually fail to capture the complexity and multi-factorial nature of natural attrition. To address this limitation, this paper proposes to develop an advanced prediction model using Machine Learning (ML) algorithms to enhance the accuracy and reliability of export prediction. These methods are applicable to a full set of data covering parameters such as demographics, workplace performance and survey scores. The objective of this model is to provide a data-driven …

Design Of A Cnn–Nlp Based Visual Chatbot For E-Commerce Systems, Sidra Kubra, Salma Jahan Nisha, Nbhan D. Salih, Amalina Ibrahim, Wan Noorshahida Mohd Isa

Journal of Informatics and Web Engineering

E-commerce platforms have limitations to assist interactive and highly personalized experiences using conventional text or voice-based chatbots, particularly in context-aware areas where product discovery and visual identification are necessary. Hence, we propose to integrate an image as a visual-based chatbot. We present a conceptual framework that combines Convolutional Neural Networks (CNN) for image recognition with Natural Language Processing (NLP) for dynamic, context-aware search in a chatbot. The CNN model enables the recognition of products from user-uploaded images, while the NLP component processes and generates appropriate responses to enhance the shopping experience by providing suggestions. This hybrid system will be developed …