Detection Of Grape Clusters In Images Using Convolutional Neural Network,
2023
National University of Sciences and Technology, Islamabad, Pakistan
Detection Of Grape Clusters In Images Using Convolutional Neural Network, Mohammad Osama Shahzad, Anas Bin Aqeel, Waqar Shahid Qureshi
Articles
Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest …
How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review,
2023
Thapar Institute of Engineering and Technology, Patiala, Punjab, India
How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo
Articles
Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers …
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices,
2023
Thapar Institute of Engineering and Technology Patiala, Punjab, India
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo
Articles
Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …
A Big Data Smart Agricultural System: Recommending Optimum Fertilisers For Crops,
2023
Technological University Dublin, Ireland
A Big Data Smart Agricultural System: Recommending Optimum Fertilisers For Crops, Vuong Ngo, Thuy-Van T. Duong, Nguyen Nguyen, Cach N. Dang, Owen Conlan
Articles
Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart …
An Aggregation-Based Algebraic Multigrid Method With Deflation Techniques And Modified Generic Factored Approximate Sparse Inverses,
2023
Technological University Dublin, Ireland
An Aggregation-Based Algebraic Multigrid Method With Deflation Techniques And Modified Generic Factored Approximate Sparse Inverses, Anastasia Natsiou, George A. Gravvanis, Christos K. Filelis-Papadopoulos, Konstantinos M. Giannoutakis
Articles
In this paper, we examine deflation-based algebraic multigrid methods for solving large systems of linear equations. Aggregation of the unknown terms is applied for coarsening, while deflation techniques are proposed for improving the rate of convergence. More specifically, the V-cycle strategy is adopted, in which, at each iteration, the solution is computed by initially decomposing it utilizing two complementary subspaces. The approximate solution is formed by combining the solution obtained using multigrids and deflation. In order to improve performance and convergence behavior, the proposed scheme was coupled with the Modified Generic Factored Approximate Sparse Inverse preconditioner. Furthermore, a parallel version …
Nesnet: A Deep Network For Estimating Near-Surface Pollutant Concentrations,
2023
Technological University Dublin, Ireland
Nesnet: A Deep Network For Estimating Near-Surface Pollutant Concentrations, Prasanjit Dey, Bibhash Pran Das, Yee Hui Lee, Soumyabrata Dev
Articles
Atmospheric pollution has become a serious threat in recent years. The advancements and expansion of industrial activity and civilization have been the major catalysts. With serious consequences like climate change and global warming, the onset of which is already being observed, keeping a check on atmospheric pollutant levels is now more important than ever. Trace gases play a major role in atmospheric chemistry. Many of these are also regarded as major atmospheric pollutants. The concentration of gases, such as (SO2), (O3), (NO2), etc., are indicators of air quality. Therefore, in this study, we primarily concern ourselves with concentrations of NO2, …
Decision Making For Process Control Management In Control Rooms: A Survey Methodology And Initial Findings,
2023
Politecnico di Torino, Italy
Decision Making For Process Control Management In Control Rooms: A Survey Methodology And Initial Findings, Chidera Winifred Amazu, Ammar N. Abbas, Micaela Demichela, Davide Fissore
Articles
Control rooms and their operators are active elements in complex socio-technical systems such as process plants. Control room operators monitor process operations, respond to alarms, and manage process deviations until emergencies. The increase in automation of plants and equipment makes the operators less involved in manual process control or other physical roles while more exposed to cognitive load generated, for example, by increasing the number of alarms or potential system failures in abnormal situations. A shift in process control design and management techniques to holistically capture risks due to evolving process or monitoring capabilities and the related influencing factors is …
A Multidimensionality Reduction Approach To Rainfall Prediction,
2023
University College Dublin, Ireland
A Multidimensionality Reduction Approach To Rainfall Prediction, Menatallah Abdel Azeem, Prasanjit Dey, Soumyabrata Dev
Articles
The rainfall has an impact on various fields and industries, including transportation, construction, tourism, health, and wildlife preservation. Accurate rainfall prediction is essential for mitigating the negative impact of rainfall on these sectors. However, previous studies on rainfall prediction have been mainly based on datasets from North America, Europe, Australia, and Central Asia, covering different periods. This study proposes using weather datasets covering the past 5 to 10 years to capture recent patterns in weather data. Additionally, the curse of dimensionality can impact model performance and lead to overfitting. Therefore, this study proposes utilizing dimensionality reduction techniques to ensure that …
An Integrated Model For Information Adoption&Trust In Mobile Social Commerce,
2023
University of Bristol, UK
An Integrated Model For Information Adoption&Trust In Mobile Social Commerce, Fulya Acikgoz, Abdelsalam Busalim, James Gaskin, Shahla Asadi
Articles
ABSTRACT Despite the growing importance of mobile social commerce (ms-commerce), little research has been conducted on the effects of informational and social factors on users’ post-adoption behavior. We, therefore, build on the understanding of mobile social commerce in the UK market and how it affects users’ post-adoption behaviors. Our theoretical model leverages the information adoption model, social support theory, and social influence theory. Data was gathered from 377 ms-commerce users from the UK and analyzed via Partial Least Squares (PLS-SEM). The research findings show that both informational and social factors have a positive impact on information adoption in ms-commerce apps. …
Assessing The Impact Of Contact Tracing With An Agent-Based Model For Simulating The Spread Of Covid-19: The Irish Experience,
2023
Technological University Dublin
Assessing The Impact Of Contact Tracing With An Agent-Based Model For Simulating The Spread Of Covid-19: The Irish Experience, Elizabeth Hunter, Sudipta Saha, Jwenish Kumawat, Ciara Carroll, John Kelleher, Claire Buckley, Conor Mcaloon, Patricia Kearney, Michelle Gilbert, Greg Martin
Articles
Contact tracing is an important tool in managing infectious disease outbreaks and Ireland used a comprehensive contact tracing program to slow the spread of COVID-19. Although the benefits of contact tracing seem obvious, it is difficult to estimate the actual impact contact tracing has on an outbreak because it is hard to separate the effects of contact tracing from other behavioural changes or interventions. To understand the impact contact tracing had in Ireland, we used an agent-based model that is designed to simulate the spread of COVID-19 through Ireland. The model uses real contact tracing data from the first year …
Interpreting Disentangled Representations Of Person-Specific Convolutional Variational Autoencoders Of Spatially Preserving Eeg Topographic Maps Via Clustering And Visual Plausibility,
2023
Technological University Dublin
Interpreting Disentangled Representations Of Person-Specific Convolutional Variational Autoencoders Of Spatially Preserving Eeg Topographic Maps Via Clustering And Visual Plausibility, Taufique Ahmed, Luca Longo
Articles
Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. Variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction. In most of the studies, VAE latent space interpretation is used to detect only the out-of-order distribution latent variable for anomaly detection. However, the interpretation and visualisation of all latent space components disclose information about how the model arrives at its conclusion. The main contribution of this study is interpreting the disentangled representation of VAE by activating only one latent component at a time, whereas the values for the remaining components are …
Survey Data On Dysfunctional Attitudes, Personality Traits, And Agreement With Persuasive Techniques,
2023
Technological University Dublin
Survey Data On Dysfunctional Attitudes, Personality Traits, And Agreement With Persuasive Techniques, Annye Braca, Pierpaolo Dondio
Articles
Persuasion techniques play a vital role in human commu- nication, influencing various aspects of our lives. With the increasing prevalence of digital platforms, these techniques have permeated online spaces such as websites, mobile apps, games, and social media. This article presents a dataset col- lected via a survey, designed to gather information about in- dividuals’ demographics, personality traits, dysfunctional at- titudes, and their responses to statements embedded with persuasion techniques. Core messages promoting paid news subscriptions, blood donations, and exercise serve as the fo- cus, while definitions and examples of persuasive techniques are provided. By analyzing this comprehensive dataset, re- …
การจำลองกำหนดการเดินเรือโดยใช้ไทม์ออโตมาตาแบบที่มีความน่าจะเป็น,
2023
คณะวิศวกรรมศาสตร์
การจำลองกำหนดการเดินเรือโดยใช้ไทม์ออโตมาตาแบบที่มีความน่าจะเป็น, รัตชนก เธียรปุญญธนากุล
Chulalongkorn University Theses and Dissertations (Chula ETD)
ในอุตสาหกรรมการขนส่งทางทะเลที่มีการจัดการด้านความเสี่ยงในการเกิดความล่าช้าในการเดินเรือตามกำหนดเป็นปัญหาที่ซับซ้อน และเกิดความเสี่ยงและเกิดค่าเสียหายผลจากถึงกำหนดล่าช้าที่จะต้องประสบกับค่าใช้จ่ายของต้นทุนที่สูงขึ้นจากปัญหาความล่าช้า จึงให้ความสนใจที่ปัญหาเหล่านี้อยู่ที่การให้ความสำคัญกับความน่าจะเป็นจากความไม่แน่นอนและเวลาในการเดินเรือ ซึ่งเป็นปัจจัยที่สำคัญในการวางแผนและจัดการตารางเดินเรือให้เหมาะสมและมีประสิทธิภาพและเหมาะสมกับเงื่อนไขและปัจจัยที่แปรผันในอุตสาหกรรมการขนส่งทางทะเล งานวิจัยนี้ จึงเล็งเห็นความสำคัญของการนำไทม์ออโตมาตาแบบที่มีความน่าจะเป็น Probabilistic Timed Automata (PTA) มาใช้ในการจำลองกำหนดการตารางเดินเรือ (Vessel Scheduling) เพื่อช่วยให้สามารถจำลองและประเมินผลของปัจจัยต่าง ๆ ที่ส่งผลต่อการเดินเรือได้อย่างเป็นระบบ และการช่วยให้ผู้วางแผนสามารถทำการปรับปรุงและวิเคราะห์ตารางเดินเรือ โดยมีผลจากการปรับปรุงค่าความนาจะเป็นและทำการทวนสอบผลที่ได้จากสถิติข้อมูลที่ใช้จำลองไม่เกิน 10% ผ่านการเขียนโปรแกรมด้วยภาษา PRISM โดยใช้ PRISM Model Checker โดยเครื่องมือสามารถจำลองพฤติกรรมการเดินเรือตามแบบจำลอง PTA ที่ออกแบบไว้ โดยคำนึงถึงปัจจัยของความน่าจะเป็นที่ส่งผลให้เกิดความล่าช้าและทำการทวนสอบด้วยสูตร PCTL ได้
Image Steganography Based On Chaoticfunction Andrandomize Function,
2023
Al Iraqia University
Image Steganography Based On Chaoticfunction Andrandomize Function, Rusul Mansoor Al-Amri, Dalal N. Hamood, Alaa Kadhim Farhan
Iraqi Journal for Computer Science and Mathematics
The exchange of data is not limited to personal text information or information about institutions and governments, but includes digital mediatransferredvia the Internet includingeverything, whether texts, images or videosandaudio, or animation.These media need high-security protection and high speed during its transmission from one site to another. In this study, a new methodis suggestedfor hiding a gray-level image within a larger color imagebased on theproposed steganography mapthatmergedchaoticfunctionand randomize function. The size of the chaos and randomize functionsis16 bytes. Experimental resultsobtained a successful method based on mean squarederror, signal-to-noise ratio,peak signal noise rate, embedding capacity, entropy, and histogram. This method can rapidlyhideandextractciphertext …
Analysis Of Attention Mechanisms In Box-Embedding Systems,
2023
Trinity College Dublin, Dublin, Ireland
Analysis Of Attention Mechanisms In Box-Embedding Systems, Jeffrey Sardina Jeffrey Sardina, Callie Sardina, John Kelleher, Declan O’Sullivan
Conference papers
Large-scale Knowledge Graphs (KGs) have recently gained considerable research attention for their ability to model the inter- and intra- relationships of data. However, the huge scale of KGs has necessitated the use of querying methods to facilitate human use. Question Answering (QA) systems have shown much promise in breaking down this human-machine barrier. A recent QA model that achieved state-of-the-art performance, Query2box, modelled queries on a KG using box embeddings with an attention mechanism backend to compute the intersections of boxes for query resolution. In this paper, we introduce a new model, Query2Geom, which replaces the Query2box attention mechanism with …
Action Classification In Human Robot Interaction Cells In Manufacturing,
2023
Technological University Dublin, Ireland
Action Classification In Human Robot Interaction Cells In Manufacturing, Shakra S.M. Mehak, Maria Chiara Leva, John Kelleher, Michael Guilfoyle
Conference papers
Action recognition has become a prerequisite approach to fluent Human-Robot Interaction (HRI) due to a high degree of movement flexibility. With the improvements in machine learning algorithms, robots are gradually transitioning into more human-populated areas. However, HRI systems demand the need for robots to possess enough cognition. The action recognition algorithms require massive training datasets, structural information of objects in the environment, and less expensive models in terms of computational complexity. In addition, many such algorithms are trained on datasets derived from daily activities. The algorithms trained on non-industrial datasets may have an unfavorable impact on implementing models and validating …
Face Image And Video Analysis In Biometrics And Health Applications,
2023
West Virginia University
Face Image And Video Analysis In Biometrics And Health Applications, Na Zhang
Graduate Theses, Dissertations, and Problem Reports (ETD)
Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different …
Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning,
2023
Wright State University
Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams
Browse all Theses and Dissertations
Obtaining accurate inferences from deep neural networks is difficult when models are trained on instances with conflicting labels. Algorithmic recognition of online hate speech illustrates this. No human annotator is perfectly reliable, so multiple annotators evaluate and label online posts in a corpus. Labeling scheme limitations, differences in annotators' beliefs, and limits to annotators' honesty and carefulness cause some labels to disagree. Consequently, decisive and accurate inferences become less likely. Some practical applications such as social research can tolerate some indecisiveness. However, an online platform using an indecisive classifier for automated content moderation could create more problems than it solves. …
Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection,
2023
Wright State University
Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula
Browse all Theses and Dissertations
Malware detection is a critical task in ensuring the security of computer systems. Due to a surge in malware and the malware program sophistication, machine learning methods have been developed to perform such a task with great success. To further learn structural semantics, Graph Neural Networks abbreviated as GNNs have emerged as a recent practice for malware detection by modeling the relationships between various components of a program as a graph, which deliver promising detection performance improvement. However, this line of research attends to individual programs while overlooking program interactions; also, these GNNs tend to perform feature aggregation from neighbors …
Effective Systems For Insider Threat Detection,
2023
Wright State University
Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman
Browse all Theses and Dissertations
Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …
