Digital Commons Network^™

Cooperative Guidance For Multigroup Flight Vehicles Against Multiple Targets With Separated Impact Time, Guofei Li, Shituo Li, Yilun Huangfu, Yueyang Hua, Yunjie Wu, Zongyu Zuo

Journal of System Simulation

Abstract: To cope with cooperative guidance against multiple targets, a distributed cooperative guidance for multigroup flight vehicles to strike multiple targets with separated impact time is proposed. The collaborative variables for multigroup flight vehicles with separated impact time are given, and the guidance law in the line of sight (LOS) is proposed. The guidance laws on the normal and lateral directions of the LOS are proposed to make the LOS deflection angle rate and LOS the inclination angle rate converge rapidly, which ensures that each vehicle is able to strike the target. The finite-time convergence of the proposed guidance laws …

Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei

Journal of System Simulation

Abstract: In order to solve the problem that UAV aerial images have a large number of small target samples but little extractable feature information, which is not conducive to improving the accuracy of aerial target detection, an improved small target detection algorithm for aerial photography based on YOLOv8s is proposed. The algorithm applies deformable convolution to the feature extraction module of the backbone network to adaptively capture the details of the target at different locations and scales. The feature information at different scales of the backbone network is extracted and enhanced by the feature collection module in the multilevel information …

Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao

Journal of System Simulation

Abstract: To enhance the natural human-machine interaction in simulation roadheader environment, a vision-based simulation roadheader operation system is proposed. The visual motion capture unit is based on the MediaPipe framework, which captures hand gestures through cameras and creates a correspondence between the physical world and virtual space. An improved Kalman filter algorithm is proposed by setting a weighted centroid to address the issue of unreasonable jumps in hand keypoint data during large-scale movements. The operator's gestures are discerned and the corresponding commands are conveyed. The results show that the improved method has significant advantages over the control group in terms …

Diversifying Cybersecurity: Evaluation Of An Internet Of Things (Iot)-Based Cybersecurity Training Course Designed To Bridge The Diversity Gap, Maureen Namukasa, Bhoomin B. Chauhan, Carlie Swords, Curtice Gough, Weronika Dymanus, Catherine Diresta, John Vitali, Vivek Sharma, T J. Oconnor, Meredith Carroll

Aeronautics Faculty Publications

This study aimed to evaluate the effectiveness of an eight-module Cybersecurity course at increasing the learning outcomes of middle and high school students with little to no experience, including underrepresented minorities (URMs) in Cybersecurity. Twice we administered and evaluated the Cybersecurity course, which included hands-on IoT-based activities, utilizing collaborative learning, scaffolding, and representation-based learning strategies. Using a quasi-experimental, within-subjects, repeated measures design, each participant experienced a pretest, the course, and a post-test to evaluate the impact on learners’ self-efficacy, interest, and knowledge. The results revealed that (1) at pre-test, female (p = .001) and in one course administration minority …

Texture Classification Through Deep Residual Networks And Feature Interpretability, Ankit Kumar

Master’s Dissertations

Texture classification plays a critical role in various real-world and industrial applications such as material recognition in manufacturing, medical image diagnostics, surface defect detection, and agricultural monitoring. The ability to distinguish textures reliably enables automation and enhances the precision of intelligent systems. Traditional methods like Local Binary Patterns (LBP), Gabor filters, and wavelet-based descriptors have been used extensively for texture analysis. While these techniques are effective under controlled conditions, they suffer from limited robustness to changes in illumination, scale, and viewpoint. Moreover, handcrafted features often fail to capture the intricate texture structures present in real-world surfaces. The KTH-TIPS2a dataset introduces …

A Systematic Evaluation Of Threaded Internode Communication In Hpc, William Pepper Marts

Computer Science ETDs

High Performance Computing (HPC) applications increasingly rely on both process and thread-level parallelism to maximize performance across complex, multi-node systems. However, conventional bulk synchronous communication strategies often leave both compute and network resources underutilized due to synchronization delays. This dissertation systematically evaluates the potential of fine-grained, threaded inter-node communication as a strategy for reducing these inefficiencies. To this end, I design and develop two tools: the MiniMod modular application framework and the Configurable Messaging Benchmark (CMB), which together enable empirical, reproducible assessment of communication performance across varying application behaviors, threading models, and communication granularities. Through experiments across multiple systems and …

Topic Shift Detection And Triggering In Natural Dialogue Systems: A Lightweight Approach, Rohith Perumandla

College of Computing and Digital Media Dissertations

This research address a key challenge in dialogue system: enabling the proactive, human-like shifting using lightweight approaching using MobileBERT (~25M) model was proposed and fine-tuned for topic shift detection, augmented with liguistic featuers for for topic trigger detection. Despite its smaller size (~25M parameters), the MobileBERT-based system achieved competitive results (F1 = 74.16%,) compared to the much larger XLNet model (~110M parameters, F1 = 79.95%), while offering greater efficiency. The topic trigger module, combining MobileBERT with linguistic features, further demonstrated effective performance (F1 = 71.61%).

Implications Of Neural Compression To Scientific Images, João Phillipe Cardenuto, Joshua Krinsky, Lucas Nogueira, Aparna Bharati, Daniel Moreira

Computer Science: Faculty Publications and Other Works

While neural compression has the potential to revolutionize image compression, recent studies have emphasized its ability to introduce subtle artifacts that could alter the image content. Concerned about the impact of such modifications on scientific images, this work explores the potential effects of neural compression on these images, focusing on two critical aspects: semantic understanding and forensic integrity. We use scientific image datasets to assess the performance of neural compression techniques on Visual Question Answering (VQA) and copy-move forgery detection tasks. Our findings indicate that the subtle changes introduced by neural ] compression do not significantly degrade the performance of …

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Revitalization Of Endangered Languages With Ai, Ivory Yang

Dartmouth College Master’s Theses

The preservation and revitalization of endangered languages, particularly those with minimal digital presence, presents significant challenges for computational linguistics. This thesis addresses these challenges by proposing novel methods for language identification and data generation, focusing on underrepresented Indigenous languages, specifically Nüshu, Native American and Native Alaskan languages.

In the first study, a COLING 2025 paper, we present NüshuRescue, an AI-driven framework designed to facilitate the preservation of Nüshu, an endangered script used exclusively by Yao women in China. Using minimal seed data, we demonstrate how GPT-4-Turbo can generate new translations, expanding a publicly available Nüshu-Chinese corpus, achieving 48.69% accuracy in …

Cutting-Edge Methods For Analyzing Student Behavior In Educational Settings: A Review, Shatha Talib Rashid, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

The ability to predict students' performance in educational settings like schools and universities is crucial. A key objective of this effort is to increase academic outcomes and prevent dropout rates, among other benefits. Automating student activities, encouraged by information collected from any technology-based learning tool, has an important role in the process here. Those big quantities of information ought to be completely studied theoretically and processed for gaining worthy insights concerning a student's background as well as interacting with scientific missions, facilitating the development of advanced ways and algorithms to predict students' performance. The current study reviews several contemporary mechanisms …

Blockchain-Based Physical Election Votes Digitally Secure Transfer, Mohanad A. Mohammed, Hala B. Abdul Wahab

Journal of Soft Computing and Computer Applications

Responsibility for maintaining election transparency over time and ensuring democratic values intact is held by the Iraqi Independent High Electoral Commission (IHEC). However, transferring physical election votes from election centers is a critical duty, where many challenges appear regarding accountability and security measures. This study proposes a system that utilizes blockchain technology to solve any challenges or difficulties and ensure an effective and improved election process by providing its highest trustworthiness and legitimacy and ensuring a decentralized security process. This system offers unique blockchain characteristics such as immutability, decentralization, and transparency, providing an extra level of security to the data …

Modern Face Recgognition Systems: A Review Of Methods And Empirical Findings, Zahraa Naji Razoqi, Raheem Ogla, Abdul Monem S. Rahma

Journal of Soft Computing and Computer Applications

The face recognition system is a biometric technique that replaces traditional passwords and personal identification. This research is dedicated to presenting a study of some facial recognition systems. Since it is unlikely to replicate and is more stable over time, the domain of facial feature extraction has proven to be more effective in attaining exact facial recognition, which is important, especially in intelligent security surveillance systems. Face recognition systems encounter several challenges, primarily related to pose variations, illumination conditions, and occlusions such as hair, glasses, and so on. To address these challenges, enhance performance, and boost the accuracy and speed …

Arson Event Detection Using Yolov9, Ali Abbas Abbod, Matheel E. Abdulmunimb, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Detecting event anomalies is crucial for surveillance systems, as it enables the identification of occurrences in videos, both temporally and spatially. It can identify deviations from patterns without requiring human oversight by learning from past information to distinguish normal behavior and pinpoint irregularities. Early detection of arson fires is critical to mitigating damage, public safety, property, and the environment, as well as saving lives and aiding in law enforcement investigations. The objective of this study is to evaluate a system for detecting events using the You Only Look Once version 9 (YOLOv9) model in surveillance videos with a focus on …

Enhancing Cybersecurity Based On Blockchain Technology: A Systematic Review, Sarah Mohammed Shareef, Rehab Flaih Hassan

Journal of Soft Computing and Computer Applications

Cybersecurity is a crucial component of the security system that guards against unauthorized access to digital transactions. Blockchain is a decentralized ledger used to securely exchange digital currencies and conduct trades and transactions. Blockchain technology has led to significant changes in electronic transactions. The enormous potential is being exploited in many areas such as financial services, real estate, supply chain, and the Internet of Things. Despite being a security system, it has suffered from security threats to sensitive data. Phishing and 51% attacks can circumvent blockchain security, highlighting the need for thorough user education and awareness. Additionally, blockchains based on …

Predicting Earthquake Location Using Convolutional Neural Network-Attention Mechanism Approach, Mohammed A. Jaleel Shaneen, Suhad M. Kadhem

Journal of Soft Computing and Computer Applications

In seismically active areas, earthquake prediction is essential for minimizing potential damages and preserving lives. However, precise forecasts are complicated to achieve because of seismic events’ complex and unpredictable nature. The current study presents an advanced prediction approach to address such issues, combining Convolutional Neural Networks (CNNs) and Attention Mechanism (AM). The primary goal is to improve the accuracy of the earthquake predictions and the generalizability across various mainland Chinese regions. AM layer emphasizes significant features for improving the prediction performance, whereas CNNs are utilized to extract spatial features of seismic data. The efficiency and effectiveness of the proposed approach …

Improving Culinary Informatics Through Meaningful Social Web Engineering, Suraya Nurain Kalid, Fahmi Mikail Fahrid, Sin-Ban Ho

Journal of Informatics and Web Engineering

This paper gives a good reflection about social web engineering aspirations to ardently see the truth to enlighten culinary concentration and wisdom. With the compassionate path to discover food and engage customers to share content with loving kindness spread to every corner. There are so many features realized, which include direct interfaces through intuitive interfaces to lead the daily food streamlining. May one engaged with constructive creation to grow compassions in every culinary living sharing. Contentment would develop in our heart involved in different phases put forward in this paper. The initial phase begins with honesty in planning and design. …

Basic Theory And Implementations Of Quantum Error Correction, Derek Rodriguez

Undergraduate Theses, Capstones, and Recitals

The introduction of quantum computing has presented algorithmic solutions to computationally difficult challenges that are far more efficient than those of classical computers. These algorithms leverage the properties of quantum mechanics to manipulate the quantum properties of subatomic particles, requiring immense precision and stability. Current quantum hardware, however, is too noisy and introduces too many errors for these algorithms to be useful in practice, necessitating the use of error correction algorithms. This field survey seeks to introduce various principles of quantum mechanics relevant to quantum computing and quantum error correction (QEC), detail the implementation and motivations of a basic QEC …

K-Means Clustering Optimization Of Toddler Malnutrition Status Using Elbow Method, Femmi Widyawati, Ahmad Yahya Dawod, Heru Agus Santoso

Journal of Informatics and Web Engineering

The problem of nutritional status is still a major challenge in the health sector in developing countries, including Indonesia. Malnutrition in toddlers can have serious long-term impacts on children's growth and development, including increased risk of disease, impaired cognitive function, and low productivity in the future. To overcome this problem, an in-depth analysis is needed to determine the distribution of nutritional status of toddlers in one of the provincial capitals in Indonesia, which can be used as a basis for planning more effective interventions. This study uses the K-Means algorithm to classify areas based on the prevalence of malnutrition in …

A Fundamental Framework For Analysis Of Rainfall Prediction Features Significance, Ye Zhian Teoh, Yim Ling Loo

Journal of Informatics and Web Engineering

Rainfall prediction efforts had been prevalent ever since the impact of climate change on occurrences of natural disasters globally. Implementation of machine and deep learning techniques on features that contribute to rainfall occurrences were conducted with aims of seeking greater prediction accuracy for rainfall occurrences with a lack of study for significance of features in rainfall occurrence prediction. This study presents a framework of rainfall prediction features' significance analysis in the case study of Peninsular Malaysia rainfall occurrences. Features investigated in this study consist of temperature, humidity and wind speed. The designed framework for the investigation includes phases of data …

Enhancing Citrus Plant Health Through The Application Of Image Processing Techniques For Disease Detection, C Pabitha, B Vanathi, K Revathi, S. Benila

Journal of Informatics and Web Engineering

The foremost task in agriculture is the decisive identification of citrus plants and the timely identification of diseases in the plants with the aim of improving the quality of crops and the yield. In this work, a machine learning algorithm focuses on image processing of citrus to solve issues that are significant and cause concern in agriculture. This work focus on the machine learning models like VGG 19 and VGG 16. In addition, dataset curation, data augmentation and various other methods were employed. The dataset used in this research is a composed one which is recorded in a comprehensive manner …

A New Bw Index For Quantifying Scholars' Research Influence, Bilal Ahmed, Li Wang, Waqar Hussain, Saim Qureshi

Journal of Informatics and Web Engineering

The growing importance of measuring and evaluating academic performance in academic hiring, promotions, funding allocation, and resource distribution has fueled the demand for better metrics. Traditional ranking indicators, such as publication count and citation-based indices, often fail to capture for the interdisciplinary influence and qualitative dimensions of research impact. These shortcomings highlight the need for more comprehensive evaluation metrics. The current study introduces a novel BW Index, which integrates both quantitative and qualitative aspects of researcher contributions aiming to provide a more balanced and comprehensive evaluation of scholarly impact. For evaluating the effectiveness of proposed index, a comparative analysis was …

Facilitating Digital Experience Sharing Among Vehicles Through Utilisation Of Pre-Existing Communication Infrastructure, Asad Hussain, Umar Farooq, Ihsan Rabbi

Journal of Informatics and Web Engineering

Vehicular communication applications are expanding quickly because of the approach of related technologies, such as vehicular cloud and the Internet of Vehicles (IoV). The combination of the Internet of Things (IoT) and smart transportation is the Internet of vehicles. Data related to infotainment, safety, and effectiveness with different vehicles and the Sustainable framework of vehicles can be exchanged. However, after the appearance of such empowering advancements, still a huge number of ideas need research. Data sharing (related trips and navigation) of new/old models and other new/old model vehicles with the owner's agreement should be taken care of. This paper proposes …

Sentiment Analysis Of The 2024 General Election Through Twitter Using Long-Short-Term Memory Algorithm, Angga Wahyu W, Haidar Hilmy Andana, Junta Zeniarja, Aris Febriyanto

Journal of Informatics and Web Engineering

This study analyses sentiment related to the 2024 Indonesian Presidential Election using the Long Short-Term Memory (LSTM) algorithm. A total of 2,400 tweets in the Indonesian language were gathered, with approximately 400 tweets sampled per week. In the data preparation, lexicon-based sentiment tagging, oversampling for class balance, and the creation and training of an LSTM model are all included in the study approach. The built model consists of embedding layers, Conv1D, and two LSTM layers. The LSTM model was selected due to its ability to capture long-range contextual dependencies in sequential text data like tweets, facilitated by its gate mechanisms …

Sentiment Analysis Of The Israel-Palestine Conflict On X: Insights From The Indonesian Perspective Using A Long Short-Term Memory Algorithm, Mohammad Taleb Noori, Muhammad Alif Rahman, Agus Purnomo, Aripin

Journal of Informatics and Web Engineering

The Israel-Palestine conflict which has persisted for decades drives mounting global interest that consequently influences public opinion worldwide. This article examines the sentiment analysis of X (Twitter) data pertaining to the conflict using the Long Short-Term Memory (LSTM) model. This study presents public reactions through an analysis of 1,700 tweets collected between May and July 2023 which encapsulate key recent developments. In this study, several steps were conducted, namely 1) crawling process to get raw data; 2) preprocessing: cleansing, case folding, tokenization, stop word removal, and stemming; 3) modelling and validation using the LSTM model; 4) model evaluation based on …

Aspects-Based Sentiment Analysis Of Extreme Weather On Twitter Using Long Short-Term Memory, Tursun Abdurahmonov, Muhammad Nabil Toby Abiyyu, Dzikru Nur Khayat, M. Ary Heryanto

Journal of Informatics and Web Engineering

This study presents an aspect-based sentiment analysis of tweets related to extreme weather events in Indonesia, utilizing the Long Short-Term Memory (LSTM) model. The dataset was obtained through a Twitter crawling process, followed by a series of preprocessing steps including data cleaning, stop word removal, normalization, tokenization, and stemming. The three primary areas of emphasis in the study were kinds of bad weather forecasts, and the government or society reactions. Using a lexicon-based technique, sentiment labelling generated three groups: positive, neutral, and negative. A random oversampling method was employed to address the data imbalance. The model using the LSTM algorithm …

Sentiment Analysis And Topic Modelling On Twitter Related To Mobile Legends: Bang Bang Game Using Lexicon-Based, Lda, And Svm, Hikmal Muhammad, Fauzi Adi Rafrastara, Kristoforus Adrian Setiadi, Arley Japardi

Journal of Informatics and Web Engineering

Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and …

Editorial Preview For June 2025 Issue, Su-Cheng Haw

Journal of Informatics and Web Engineering

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This June 2025 release contains 28 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Augmented Intelligence, Artificial Intelligence (AI), Generative AI, Data Mining (DM), Software Engineering, Sentiment Analysis, Recommender Systems, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 6 papers curated by our Thematic Editor, Assoc. Prof. Dr. Heru Agus Santoso, under the theme "Augmented Intelligence". In his editorial, Assoc. Prof. Dr. Heru …

Diabetes Risk Prediction Using Shapley Additive Explanations For Feature Engineering, Chinwe Miracle Chituru, Sin-Ban Ho, Ian Chai

Journal of Informatics and Web Engineering

Diabetes is prevalent globally, expected to increase in the next few years. This includes people with different types of diabetes including type 1 diabetes and type 2 diabetes. There are several causes for the increase: dietary decisions and lack of exercise as the main ones. This global health challenge calls for effective prediction and early management of the disease. This research focuses on the decision tree algorithm utilization to predict the risk of diabetes and model interpretability with the integration of SHapley Additive exPlanations (SHAP) for feature engineering. Random forest and gradient boosting models were developed to identify the risk …

Image-Based Detection And Classification Of Poultry Diseases From Chicken Droppings In Open House Poultry Farms, Md Najmul Hasan, Zu Jun Khow, Vik Tor Goh, Sarina Mansor, Yi-Fei Tan

Journal of Informatics and Web Engineering

Monitoring chicken health is essential for maintaining the production efficiency of poultry farms and meeting the demand for poultry products. Previous studies have explored various methods, including utilizing sound, behaviour, and the shape of the chickens, as well as the conditions of their droppings, to assess chicken health. In this research, we monitor chicken droppings as a reliable indicator of chicken health. We develop an automated system for detecting chicken droppings and identifying health conditions, specifically in open house poultry farms in Malaysia. Open poultry houses are the most common design in Malaysia due to their lower construction and maintenance …