Data Storage Systems Commons^™

An Accurate Real-Time Method For Face Mask Detectionusing Cnn And Svm, Shili Hechmi

Knowledge Engineering and Data Science

Infectious respiratory diseases, including COVID-19, pose a significant challenge to humanity and a potential threat to life due to their severity and rapid spread. Using a surgical mask is among the most significant safety precautions that can help keep this sort of pandemic from spreading, and manual monitoring of large crowds in public places for face masks is problematic. In this research, we suggest a real-time approach for face mask detection. First, we use a multi-scale deep neural network to extract features. As a result, the attributes are better suited for training the detection system. We employ SVM post-processing in …

Hybrid Artificial Bee Colony And Improved Simulatedannealing For The Capacitated Vehicle Routing Problem, Farhanna Mar'i, Hafidz Ubaidillah, Wayan Firdaus Mahmudy, Ahmad Afif Supianto

Knowledge Engineering and Data Science

Capacitated Vehicle Routing Problem (CVRP) is a type of NP-Hard combinatorial problem that requires a high computational process. In the case of CVRP, there is an additional constraint in the form of a capacity limit owned by the vehicle, so the complexity of the problem from CVRP is to find the optimum route pattern for minimizing travel costs which are also adjusted to customer demand and vehicle capacity for distribution. One method of solving CVRP can be done by implementing a meta-heuristic algorithm. In this research, two meta-heuristic algorithms have been hybridized: Artificial Bee Colony (ABC) with Improved Simulated Annealing …

Adaptive Neuro-Fuzzy Inference System For Waste Prediction, Haviluddin Haviluddin, Herman Santoso Pakpahan, Novianti Puspitasari, Gubtha Mahendra Putra, Rima Yustika Hasnida, Rayner Alfred

Knowledge Engineering and Data Science

The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best …

Indonesian Language Term Extraction Using Multi-Task Neural Network, Joan Santoso, Esther Irawati Setiawan, Fransiskus Xaverius Ferdinandus, Gunawan Gunawan, Leonel Hernandez Collantes

Knowledge Engineering and Data Science

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two …

Associated Patterns In Open-Ended Concept Maps Within E-Learning, Didik Dwi Prasetya, Tsukasa Hirasama

Knowledge Engineering and Data Science

A concept map is a diagram that visualizes the structure of individual cognitive knowledge. An approach to creating a concept map structure that allows users to contribute concepts and linkages that express their understanding freely is known as an "open-ended concept map." It has been demonstrated that an open-ended concept map accurately depicts student knowledge structures and reveals student differences. However, manually analyzing an open-ended map is difficult, time-consuming, and includes many propositions, especially in a big classroom. Educational data mining could be used to further process and analyze a collection of concept maps. However, many works attempted to employ …

Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn

Masters Projects

This project applied software specification gathering, architecture, work planning, and development to a real-world development effort for a local business. This project began with a feasibility meeting with the owner of Zeal Aerial Fitness. After feasibility was assessed the intended users, needed functionality, and expected user restrictions were identified with the stakeholders. A hybrid software lifecycle was selected to allow a focus on base functionality up front followed by an iterative development of expectations of the stakeholders. I was able to create various specification diagrams that express the end projects goals to both developers and non-tech individuals using a standard …

Digital Platform To Aid Youth Substance Abuse Prevention, Bingxuan Li

Discovery Undergraduate Interdisciplinary Research Internship

Through research and interviews, I discovered that a significant portion of students in Africa become drug addicts and drop out of school. The solution is to prevent youth substance abuse before it happens, so that more students in Africa may continue their education. With the strong motivation of expanding African student involvement in higher education, I participated DURI program to increase higher education rates in the Democratic Republic of the Congo, Africa. The local government is establishing rehabilitation centers to monitor at-risk students and prevent youth substance abuse, but due to extremely limited resources, it is critical to evaluate the …

A Comparative Study On Blockchain-Based Electronic Health Record Systems: Performance, Privacy, And Security Between Hyperledger Fabric And Ethereum Frameworks, Md Jobair Hossain Faruk, Hossain Shahriar, Maria Valero, Xia Li

Master of Science in Software Engineering Theses

Traditional data collection, storage, and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches that compromise their reliability and availability. Addressing the challenges of conventional database techniques and improving the overall aspects of EHR application, blockchain technology is being evaluated to find a possible solution. Blockchain refers to an emerging distributed technology and incorruptible database of records or digital events which execute, validate, and maintain by a ledger technology to provide an immutable architecture and prevent records manipulation …

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

Scalable Data-Driven Predictive Modeling And Analytics For Cho Process Development Optimization, Sarah Mbiki

All Dissertations

In 1982, the FDA approved the first recombinant therapeutic protein, and since then, the biopharmaceutical industry has continued to develop innovative and highly effective biological drugs for various illnesses¹. These drugs are produced using host organisms that are modified to hold the genetic encoding of the targeted protein¹. Of the many host organisms, Chinese hamster ovary (CHO) cells are often used due to capability to perform posttranslational modification (PTM): which allows human-like synthesis of proteins unlikely to invoke immunogenicity in humans ^1,2.

Despite all the positive attributes, many challenges are associated with CHO cell cultures, …

Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro

The Journal of Purdue Undergraduate Research

No abstract provided.

Towards Qos-Based Embedded Machine Learning, Tom Springer, Erik Linstead, Peiyi Zhao, Chelsea Parlett-Pelleriti

Engineering Faculty Articles and Research

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the …

Artificial Justice: The Quandary Of Ai In The Courtroom, Paul W. Grimm, Maura R. Grossman, Sabine Gless, Mireille Hildebrandt

Judicature International

No abstract provided.

An Attribute-Aware Attentive Gcn Model For Attribute Missing In Recommendation, Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie

Research Collection School Of Computing and Information Systems

As important side information, attributes have been widely exploited in the existing recommender system for better performance. However, in the real-world scenarios, it is common that some attributes of items/users are missing (e.g., some movies miss the genre data). Prior studies usually use a default value (i.e., "other") to represent the missing attribute, resulting in sub-optimal performance. To address this problem, in this paper, we present an attribute-aware attentive graph convolution network (A(2)-GCN). In particular, we first construct a graph, where users, items, and attributes are three types of nodes and their associations are edges. Thereafter, we leverage the graph …

Bitrdf: Extending Rdf For Bitemporal Data, Di Wu

Dissertations, Theses, and Capstone Projects

The Internet is not only a platform for communication, transactions, and cloud storage, but it is also a large knowledge store where people as well as machines can create, manipulate, infer, and make use of data and knowledge. The Semantic Web was developed for this purpose. It aims to help machines understand the meaning of data and knowledge so that machines can use the data and knowledge in decision making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web which is organized as the Semantic Web Layer Cake. RDF is limited and can only express a binary …

Fine-Grained And Controllably Editable Data Sharing With Accountability In Cloud Storage, Huiying Hou, Jianting Ning, Yunlei Zhao, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increasing cloud storage service, users can enjoy non-interactive data sharing. Nonetheless, the data owner cannot timely update the shared data all the while. To ensure the timeliness and the authoritative source of the data, some users should be allowed to update the data on behalf of an authoritative data owner without changing data source. However, this allows harmful information to be injected into the data unnoticeably. How to efficiently realize editable cloud-based data sharing supporting malicious user tracing has not been fully explored. To address the problem, we propose a fine-grained and controllably editable cloud-based data sharing scheme …

Towards Public Vaccination Data Resilience During Natural Disaster Using Blockchain-Based Decentralized Application, Riri Fitri Sari, Mokhammad Rizqi Herdiawan, Muhammad Hamzah, Muhammad Aljundi, Jauzak Hussaini Windiatmaja

Smart City

This paper presents a platform development plan for a public vaccination decentralized data base based on Blockchain technology. Vaccination is a medical practice that allows a human body to produce a specific antibody as a means of developing a preventive measure and reducing the risk of contracting a particular disease. Systematic vaccination data recording is an utmost important system that is required particularly during the Covid-19 pandemic. The recording mechanism should utilize the state-of-the-art information and communication technology (ICT). For faster vaccination data recording, it is common to use digital technology, making the health care process faster thus decreasing time …

Structured Encryption For Knowledge Graphs, Yujie Xue, Lanxiang Chen, Yu Mi, Lingfang Zeng, Fatemeh Rezaeibagha, Robert H. Deng

Research Collection School Of Computing and Information Systems

We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents-symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational …

Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, …

Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary …