Computer Sciences Commons^™

ความสัมพันธ์ระหว่างพฤติกรรมการแวะชม มูลค่าในตะกร้าและจำนวนธุรกรรมการซื้อบนเว็บไซต์แอมะซอน, กฤษณ์ เครือศุภมาส

Chulalongkorn University Theses and Dissertations (Chula ETD)

ปัจจุบันพาณิชย์อิเล็กทรอนิกส์ได้รับความนิยมเป็นอันมากและมีการเติบโตอย่างรวดเร็ว โดยเว็บไซต์ Amazon เป็นเว็บไซต์ค้าปลีกรายใหญ่ที่มียอดขายและยอดเข้าชมสูงสุดในปี ค.ศ. 2018 งานวิจัยนี้มีวัตถุประสงค์เพื่อ (1) เปรียบเทียบพฤติกรรมการแวะชมเว็บไซต์ Amazon อันได้แก่ จำนวนหน้าต่อครั้ง (Pageview) ระยะเวลาการเข้าชมต่อครั้ง (Duration) และจำนวนครั้งการเข้าชม (Number of Session) ของผู้แวะชมที่มีการซื้อและไม่มีการซื้อ และ (2) สำรวจ (Explore) ผลของพฤติกรรมการแวะชมเว็บไซต์ Amazon (ของผู้แวะชมที่มีการซื้อ) ที่มีต่อมูลค่าในตะกร้าสินค้า (Basket Value) โดยงานวิจัยนี้อาศัยข้อมูลจากฐานข้อมูล Comscore เป็นข้อมูลทุติยภูมิ (Secondary data) สำหรับการวิเคราะห์ที่เป็นการแวะชมเว็บไซต์ Amazon ในปี ค.ศ. 2018 จำนวน 853,486 ครั้ง ผลการเปรียบเทียบพฤติกรรมการแวะชมพบว่า ผู้แวะชมที่มีการซื้อมีจำนวนหน้าต่อครั้ง ระยะเวลาการเข้าชมต่อครั้ง และจำนวนครั้งการเข้าชม มากกว่าผู้แวะชมที่ไม่มีการซื้ออย่างมีนัยสำคัญทางสถิติที่ระดับ .05 อีกทั้งผลการสำรวจพฤติกรรมการแวะชมของผู้แวะชมที่มีการซื้อที่ส่งผลต่อมูลค่าในตะกร้าสินค้า พบว่า จำนวนครั้งการเข้าชม และจำนวนหน้าต่อครั้ง เป็นสองตัวแปรที่ส่งผลต่อมูลค่าในตะกร้าสินค้าอย่างมีนัยสำคัญทางสถิติ นอกจากเป็นการต่อยอดองค์ความรู้เกี่ยวกับพาณิชย์อิเล็กทรอนิกส์ในส่วนที่เกี่ยวข้องกับพฤติกรรมการแวะชมเว็บไซต์และมูลค่าในตะกร้าสินค้าแล้ว ข้อค้นพบในงานนี้ยังเสนอแนะให้ผู้ประกอบการสนใจ จำนวนหน้าต่อครั้งและจำนวนครั้งการเข้าชม เพราะเป็นสองตัวแปรที่ส่งผลต่อมูลค่าในตะกร้าสินค้า ซึ่งเป็นรายได้จากการขายสินค้าของธุรกิจค้าปลีกออนไลน์

Pig Pose Estimation Based On Extracted Data Of Mask R-Cnn With Vgg Neural Network For Classifications, Sang Kwan Lee

Electronic Theses and Dissertations

This paper proposes a pig pose estimation operating with Region Proposal Network (RPN) of Mask Region based Convolutional Neural Network (Mask R-CNN) and Visual Geometry Group (VGG) Neural Network (NN). Object pose estimations generates from the associations of different key points. Key points could be explained as specific location of an object such as different joints of a human body or joints of different object. Hourglass network is one of a NN delivering key points of an object. Associating the different key points with the hourglass network results could be represented as instance-level detection [3]. However, the instance-level detection shows …

Svat4: A Computer Program For Visualization And Analysis Of Crystal Structures, Xingzhong Li

Nebraska Center for Materials and Nanoscience: Faculty Publications

SVAT4 is a computer program for interactive visualization of three-dimensional crystal structures, including chemical bonds and magnetic moments. A wide range of functions, e.g. revealing atomic layers and polyhedral clusters, are available for further structural analysis. Atomic sizes, colors, appearance, view directions and view modes (orthographic or perspective views) are adjustable. Customized work for the visualization and analysis can be saved and then reloaded. SVAT4 provides a template to simplify the process of preparation of a new data file. SVAT4 can generate high-quality images for publication and animations for presentations. The usability of SVAT4 is broadened by a software suite …

A Machine Learning System For Glaucoma Detection Using Inexpensive Machine Learning, Jon Kilgannon

West Chester University Master’s Theses

This thesis presents a neural network system which segments images of the retina to calculate the cup-to-disc ratio, one of the diagnostic indicators of the presence or continuing development of glaucoma, a disease of the eye which causes blindness. The neural network is designed to run on commodity hardware and to be run with minimal skill required from the user by packaging the software required to run the network into a Singularity image. The RIGA dataset used to train the network provides images of the retina which have been annotated with the location of the optic cup and disc by …

Novel Computational Approaches For Multidimensional Brain Image Analysis, Harish Raviprakash

Electronic Theses and Dissertations, 2020-2023

The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical …

The Concept Of Representation Capability Of Databases And Its Application In Is Development, Lishuan Qin, Junkang Feng

Journal of International Technology and Information Management

The representation capability of an information system in general and a database in particular seems an important and yet elusive concept, which is concerned with, in our view, how a database ever becomes capable of representing real-world objects accurately or otherwise. To explore how to approach and then define this concept, we explore what is meant and required by the statement that a database connection (i.e., a connection between database constructs such as entities in an Entity-relationship (ER) diagram and relations in a relational schema that are made available by a database) refers to, represents and accurately represents a …

A User-Centric And Sentiment Aware Privacy-Disclosure Detection Framework Based On Multi-Input Neural Network, A. K. M. Nuhil Mehdy, Hoda Mehrpouyan

Computer Science Faculty Publications and Presentations

Data and information privacy is a major concern of today’s world. More specifically, users’ digital privacy has become one of the most important issues to deal with, as advancements are being made in information sharing technology. An increasing number of users are sharing information through text messages, emails, and social media without proper awareness of privacy threats and their consequences. One approach to prevent the disclosure of private information is to identify them in a conversation and warn the dispatcher before the conveyance happens between the sender and the receiver. Another way of preventing information (sensitive) loss might be to …

Detecting Fake News Spreaders In Social Networks Via Linguistic And Personality Features: Notebook For Pan At Clef 2020, Anu Shrestha, Francesca Spezzano, Abishai Joy

Computer Science Faculty Publications and Presentations

This paper addresses the problem of automatically detecting fake news spreaders in social networks such as Twitter. We model the problem as a binary classification task and consider several groups of features, including writing style, word and char n-grams, BERT semantic embedding, and sentiment analysis, which are computed from a set of tweets each user authored. Our proposed approach is evaluated on the dataset made available by the PAN at CLEF 2020 shared task on profiling fake news spreader, which provided labeled data in both English and Spanish. Experimental results show that we can detect fake news spreaders with an …

Measuring Differential Forest Growth In The Sheepscot River Headwaters With Bitemporal Lidar, Soren Denlinger

Honors Theses

In recent years, lidar has proven itself as a forestry tool capable of accurate, large- scale inventories. Lidar has even shown utility in multitemporal analysis and growth assessment, given high-resolution or small-scale point clouds. However, lidar’s efficacy as a multitemporal tool with relatively low-resolution, large-scale datasets is comparatively unknown. In this study, I compared forest in Midcoast Maine bitemporally, with publicly available datasets from the years 2007 and 2012. Specifically, I compared differences in growth characteristics of riparian, wetland, and upland forests. Although the 2007 dataset (created for geomorphological research) and the 2012 dataset (statewide, general-purpose) possess varying point densities …

Leveraging Peer-To-Peer Energy Sharing For Resource Optimization In Mobile Social Networks, Aashish Dhungana

Theses and Dissertations

Mobile Opportunistic Networks (MSNs) enable the interaction of mobile users in the vicinity through various short-range wireless communication technologies (e.g., Bluetooth, WiFi) and let them discover and exchange information directly or in ad hoc manner. Despite their promise to enable many exciting applications, limited battery capacity of mobile devices has become the biggest impediment to these appli- cations. The recent breakthroughs in the areas of wireless power transfer (WPT) and rechargeable lithium batteries promise the use of peer-to-peer (P2P) energy sharing (i.e., the transfer of energy from the battery of one member of the mobile network to the battery of …

Editorial

Journal of Cybersecurity Education, Research and Practice

Editorial.

Completion Reasoning Emulation For The Description Logic El+, Aaron Eberhart, Monireh Ebrahimi, Lu Zhou, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn εℒ+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner’s and LSTM’s …

Two Novel Radar Detectors For Spiky Sea Clutter With The Presence Of Thermal Noise And Interfering Targets, Nouh Guidoum, Faouzi Soltani, Amar Mezache

Turkish Journal of Electrical Engineering and Computer Sciences

In the context of noncoherent detection and high-resolution maritime radar system with low grazing angle, new Constant False Alarm Rate (CFAR) decision rules are suggested for two Compound Gaussian (CG) clutters namely: The K distribution and the Compound Inverse Gaussian (CIG) distribution, which are considered among the most appropriate models for sea clutter. The proposed decision rules are then modified to deal with the presence of thermal noise and interfering targets. The proposed detectors are investigated on the basis of synthetic data as well as real data of the IPIX radar database. The obtained results exhibit a high probability of …

Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş

Turkish Journal of Electrical Engineering and Computer Sciences

Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an …

Cnn-Based Speed Detection Algorithm For Walking And Running Using Wrist-Worn Wearable Sensors, Venkata Devesh Reddy Seethi

Graduate Research Theses & Dissertations

In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track human physical activities effortlessly. These devices have enabled common citizens to track their physical fitness and encourage them to lead a healthy lifestyle. Among various exercises, walking and running are the most common activities people do in everyday life, either through commute, exercise, or by doing household chores. While performing these activities, the speed at which a person walks and runs is an essential factor to determine the intensity of activity. Therefore, it is important to measure walking/running speed to …

A Design Of Mac Model Based On The Separation Of Duties And Data Coloring: Dsdc-Mac, Soon-Book Lee, Yoo-Hwan Kim, Jin-Woo Kim, Chee-Yang Song

Computer Science Faculty Research

Among the access control methods for database security, there is Mandatory Access Control (MAC) model in which the security level is set to both the subject and the object to enhance the security control. Legacy MAC models have focused only on one thing, either confidentiality or integrity. Thus, it can cause collisions between security policies in supporting confidentiality and integrity simultaneously. In addition, they do not provide a granular security class policy of subjects and objects in terms of subjects' roles or tasks. In this paper, we present the security policy of Bell_LaPadula Model (BLP) model and Biba model as …

Page-Net: Interpretable And Integrative Deep Learning For Survival Analysis Using Histopathological Images And Genomic Data, Jie Hao, Sai Chandra Kosaraju, Nelson Zange Tsaku, Dae Hyun Song, Mingon Kang

Computer Science Faculty Research

The integration of multi-modal data, such as histopathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions in cancer study. Histopathology, as a clinical gold-standard tool for diagnosis and prognosis in cancers, allows clinicians to make precise decisions on therapies, whereas high-throughput genomic data have been investigated to dissect the genetic mechanisms of cancers. We propose a biologically interpretable deep learning model (PAGE-Net) that integrates histopathological images and genomic data, not only to improve survival prediction, but also to identify genetic and histopathological patterns that cause different …

Wind Power Forecasting Methods Based On Deep Learning: A Survey, Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang

Electrical & Computer Engineering Faculty Research

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of …

Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin

Browse all Theses and Dissertations

Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide …

Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie

Browse all Theses and Dissertations

Deep Neural Networks (DNNs) are powerful tools blossomed in a variety of successful real-life applications. While the performance of DNNs is outstanding, their opaque nature raises a growing concern in the community, causing suspicions on the reliability and trustworthiness of decisions made by DNNs. In order to release such concerns and towards building reliable deep learning systems, research efforts are actively made in diverse aspects such as model interpretation, model fairness and bias, adversarial attacks and defenses, and so on. In this dissertation, we focus on the research topic of DNN interpretations for visual intelligence, aiming to unfold the black-box …

Design And Development Of Electronic Sensor And Monitoring System Of Smart Low-Cost Phototherapy Light System For Non-Invasive Monitoring And Treatment Of Neonatal Jaundice, Paul M. Cabacungan, Carlos Oppus, Gregory L. Tangonan, Nerissa G. Cabacungan, John Paul Mamaradlo, Neil Angelo Mercado

Electronics, Computer, and Communications Engineering Faculty Publications

This paper showcases our previous and continuously improving development at Ateneo Innovation Center (AIC) and partners in designing and further enhancing the existing Low-cost Phototherapy Light System (LPLS) and Improved Low-cost Phototherapy Light System (ILPLS) to the new Smart Low-cost Phototherapy Light System (Smart LPLS) with non-invasive jaundice monitoring for newborns with Neonatal Jaundice (NNJ). Developing this tool will help determine the intensity of yellowish color in infants and can monitor NNJ in a non-invasive way. The system is envisioned to be integrated with Mobile or Near Cloud as part of Smart Nursing Station together with other hospital equipment for …

You Don’T Say... Linguistic Features In Sarcasm Detection, Martina Ducret, Lauren Kruse, Carlos Martinez, Anna Feldman, Jing Peng

Department of Computer Science Faculty Scholarship and Creative Works

We explore linguistic features that contribute to sarcasm detection. The linguistic features that we investigate are a combination of text and word complexity, stylistic and psychological features. We experiment with sarcastic tweets with and without context. The results of our experiments indicate that contextual information is crucial for sarcasm prediction. One important observation is that sarcastic tweets are typically incongruent with their context in terms of sentiment or emotional load.

Nnv: The Neural Network Verification Tool For Deep Neural Networks And Learning-Enabled Cyber-Physical Systems, Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson

Computer Science Faculty Publications

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear …

The Knapsack Subproblem Of The Algorithm To Compute The Erdos-Selfridge Function, Brianna Sorenson

Undergraduate Honors Thesis Collection

This thesis summarizes the methodology of a new algorithm to compute the Erdos-Selfridge function which uses a wheel sieve, shows that a knapsack algorithm can be used to minimize the work needed to compute these values by selecting a subset of rings for use in the wheel, and compares the results of several different knapsack algorithms in this particular scenario.

Artificial Intelligence-Enhanced Decision Support For Informing Global Sustainable Development: A Human-Centric Ai-Thinking Approach, Meng-Leong How, Sin Mei Cheah, Yong-Jiet Chan, Aik Cheow Khor, Eunice Mei Ping Say

CCX Research

Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic …

Process Data Analytics Using Deep Learning Techniques, Majid Moradi Aliabadi

Wayne State University Theses

In chemical manufacturing plants, numerous types of data are accessible, which could be process operational data (historical or real-time), process design and product quality data, economic and environmental (including process safety, waste emission and health impact) data. Effective knowledge extraction from raw data has always been a very challenging task, especially the data needed for a type of study is huge. Other characteristics of process data such as noise, dynamics, and highly correlated process parameters make this more challenging.

In this study, we introduce an attention-based RNN for multi-step-ahead prediction that can have applications in model predictive control, fault diagnosis, …

A Survey On Recognizing Textual Entailment As An Nlp Evaluation, Adam Poliak

Computer Science Faculty Research and Scholarship

Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to compare semantic understanding of different NLP systems. In this survey paper, we provide an overview of different approaches for evaluating and understanding the reasoning capabilities of NLP systems. We then focus our discussion on RTE by highlighting prominent RTE datasets as well as advances in RTE dataset that focus on specific linguistic phenomena that can be used to evaluate NLP systems on a fine-grained level. We conclude by arguing that when evaluating NLP systems, the community should utilize newly introduced RTE datasets that focus on specific linguistic phenomena.

Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan

Turkish Journal of Electrical Engineering and Computer Sciences

Diabetic patients are quite hesitant in engaging in normal physiological activities due to difficulties associated with diabetes management. Over the last few decades, there have been advancements in the computational power of embedded systems and glucose sensing technologies. These advancements have attracted the attention of researchers around the globe developing automatic insulin delivery systems. In this paper, a method of closed-loop control of diabetes based on neural networks is proposed. These neural networks are used for making predictions based on the clinical data of a patient. A neural network feedback controller is also designed to provide a glycemic response by …

A Power And Area Efficient Approximate Carry Skip Adder For Error Resilient Applications, Sujit Patel, Bharat Garg, Shireesh Kumar Rai

Turkish Journal of Electrical Engineering and Computer Sciences

The compute-intensive multimedia applications on portable devices require power and area efficient arithmetic units. The adder is a prime building block of these arithmetic units and limits the overall performance. Therefore, this paper analyzes the logic operations of the state-of-the-art adders and presents a novel low complexity adder segment with new carry prediction logic by removing the redundant logic and sharing the common operations. Further, a new power and area efficient approximate carry skip (PAEA-CSK) adder is proposed using the novel adder segment. The effectiveness of the proposed PAEA-CSK adder is evaluated and compared over the existing adders by implementing …

Rule Extraction And Performance Estimation By Using Variable Neighborhoodsearch For Solar Power Plant In Konya, Yusuf Uzun, Muci̇z Özcan

Turkish Journal of Electrical Engineering and Computer Sciences

The use of renewable energy sources in the production of electricity has become inevitable in order to reduce the greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basis have implemented a number of policies to support electricity generated from renewable energy sources, investments to produce electricity without a license on a local basis are not desirable. Those who want to invest medium and small scale for the most reason expect that this work will be supported by real data. Although the electricity generated by renewable investments is generated by simulation …