Search Results

Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices
Article describes how embedded devices, constrained by limited memory and processors, require deep learning models to be tailored to their specifications. This research explores customized model architectures for classifying drainage crossing images.
Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies
Article describes how Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. This article presents an overview of ICS security, covering its components, protocols, industrial applications, and performance aspects.
Feasibility of PROMIS using computerized adaptive testing during inpatient rehabilitation
Article describes how there has been an increased significance on patient-reported outcomes in clinical settings. The authors aimed to evaluate the feasibility of administering patient-reported outcome measures by computerized adaptive testing (CAT) using a tablet computer with rehabilitation inpatients, assess workload demands on staff, and estimate the extent to which rehabilitation inpatients have elevated T-scores on six Patient Reported Outcomes Measurement Information System (PROMIS) measures.
Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations
Article describes how the accurate and efficient determination of hydrologic connectivity has garnered significant attention from both academic and industrial sectors due to its critical implications for environment management. To address these challenges, the focus of the author's study is on detecting drainage crossings through the application of advanced convolutional neural networks.
Predicting psoriasis using routine laboratory tests with random forest
Article describes how psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. The goal of the authors' study is to derive a powerful predictive model for psoriasis disease based on only routine hospital tests.
FlexiChain 3.0: Distributed Ledger Technology-Based Intelligent Transportation for Vehicular Digital Asset Exchange in Smart Cities
Article describes how, due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. In this work, the authors explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS.
SUPREME: multiomics data integration using graph convolutional networks
Article states that, to pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utilizing multiomics features and integrates them with raw features to capture complementary signals.
PPAD: a deep learning architecture to predict progression of Alzheimer’s disease
Article asserts that Alzheimer’s disease (AD) is a neurodegenerative disease that affects millions of people worldwide. The authors of the article propose two deep learning architectures based on RNN, namely Predicting Progression of Alzheimer’s Disease (PPAD) and PPAD-Autoencoder.
NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool
Article asserts that the nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. The authors developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized.
NextGen-Malloc: Giving Memory Allocator Its Own Room in the House
Article describes how memory allocation and management have a significant impact on performance and energy of modern applications. The authors observe that performance can vary by as much as 72% in some applications based on which memory allocator is used, and in this paper, the authors make a case for offloading memory allocation (and other similar management functions) from main processing cores to other processing units to boost performance, reduce energy consumption, and customize services to specific applications or application domains.
FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs
Article describes how today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost. In this paper, the authors develop a fast and high- ratio error-bounded lossy compressor on GPUs for scientific data (called FZ-GPU).
OptiFit: Computer-Vision-Based Smartphone Application to Measure the Foot from Images and 3D Scans
Article asserts that the foot is a vital organ, as it stabilizes the impact forces between the human skeletal system and the ground. The authors present an instep girth measurement algorithm, and they used a pixel per metric algorithm for measurement; these algorithms were accordingly integrated with the application.
Visual object tracking: Progress, challenge, and future
Article discusses how visual object tracking aims to continuously localize the target object of interest in a video sequence. To provide the community an overview, in this commentary, the authors discuss visual tracking from different aspects.
agroString: Visibility and Provenance through a Private Blockchain Platform for Agricultural Dispense towards Consumers
Article discusses the large quantities of farm and meat products that rot and are wasted if correct actions are not taken leading to serious health concerns if consumed. Because there is no proper system for tracking and communicating the status of goods to consumers, a right which according to the authors should be a given, they propose a method of increased communication using Corda private blockchain.
Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends
Article states that vaccines, though reliable preventative measures for diseases, also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines including the COVID-19 vaccines. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis.
Privacy-Preserving Object Detection with Secure Convolutional Neural Networks for Vehicular Edge Computing
Article discusses how with the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. The authors aim to address the privacy problem by protecting both vehicles' sensor data and the detection results.
Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features
Article discusses how despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models.
Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation
Authors of the article created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study.
PharmaChain: A blockchain to ensure counterfeit‐free pharmaceutical supply chain
Article discusses how globalisation has facilitated different industries to eliminate geographical boundaries and equipped organisations to work collectively to produce goods. The authors of the article propose a novel Distributed Ledger Technology (DLT) based transparent supply chain for PSC and proof-of-concept is implemented to analyse the scalability and efficiency of the proposed architecture.
Detection of DDoS Attack in Software-Defined Networking Environment and Its Protocol-wise Analysis using Machine Learning
Article describes how distributed-denial-of-service (DDoS) attacks can cause a great menace to numerous organizations and their stakeholders. The authors assert that the objective of this research work is to take into account a DDoS afflicted SDN specific dataset and detect the malicious traffic by using various machine learning algorithms namely., K-Nearest Neighbours, Logistic Regression, Multilayer Perceptron, Iterative Dichotomiser 3, and Stochastic Gradient Descent.
A Gaze into the Internal Logic of Graph Neural Networks, with Logic
Article exploring graph node property prediction. Originally presented as part of the application track at the 38th International Conference on Logic Programming in Haifa, Israel.
Detecting Covid-19 chaos driven phishing/malicious URL attacks by a fuzzy logic and data mining based intelligence system
Article analyses the impact of Covid-19 on various cyber-security related aspects.
A Parallel Convolution and Decision Fusion-Based Flower Classification Method
This article proposes a novel flower classification method that combines enhanced VGG16 (E-VGG16) with decision fusion.
Computing microRNA-gene interaction networks in pan-cancer using miRDriver
This article is a study where the authors integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach.
Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model
Article is a study proposing an approach for blood cancer disease prediction using the supervised machine learning approach to perform blood cancer prediction with high accuracy using microarray gene data.
COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset
Article is a study analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset, natural language processing, and machine learning.
Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model
This article presents a study detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments.
Artificial Intelligence for Colonoscopy: Past, Present, and Future
Article summarizing the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials, (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities.
Vector mosquito image classification using novel RIFS feature selection and machine learning models for disease epidemiology
Article proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes in order to prevent mosquito-borne infection.
Annotating If Authors of Tweets are Located in the Locations They Tweet About
Article presented at the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). The study investigates spatial information of tweets whereby the authors present a corpus of tweets annotated with temporally-anchored spatial information involving the author.
Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network
This article develops a deep-learning algorithm for an on-loom fabric defect inspection system by combining the techniques of image pre-processing, fabric motif determination, candidate defect map generation, and convolutional neural networks (CNNs).
Bayesian analysis of complex mutations in HBV, HCV, and HIV studies
This article provides a review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. The authors also provide a summary of the Bayesian methods' applications toward these viruses' studies, where several important and useful results have been discovered.
Incorporating Emoji Descriptions Improves Tweet Classification
Article presenting a simple strategy to process emojis in Tweets: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. Results show that this strategy is more effective than using pretrained emoji embeddings for tweet classification.
Annotating Educational Questions for Student Response Analysis
Article introduces the first taxonomy and annotated educational corpus of questions that aims to help with the analysis of student responses.
A Corpus of Metaphor Novelty Scores for Syntactically-Related Word Pairs
Article introduces a large corpus of metaphor novelty scores for syntactically related word pairs, and releases it freely to the research community. This article describes the corpus, includes an analysis of its score distribution and the types of word pairs included in the corpus, and provides a brief overview of standard metaphor detection corpora.
Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies
Article presenting a two-step methodology to annotate temporally-anchored spatial knowledge on top of OntoNotes.
Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies
Article (1) developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework.
Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction
This article presents a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. The authors discuss the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in massive MIMO systems and discuss state-of-the-art mitigation techniques. Recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems are outlined. Finally, future research for massive MIMO systems for 5G and beyond is discussed.
Exploring Edge Computing in Multi-Person Mixed Reality for Cooperative Perception
Article and accompanying poster presenting a prototype for the use of Edge with MR devices to provide cooperative perception capability to the MR device.
JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases
Article develops a a simple, fast, and powerful method, named JS-MA, based on Jensen-Shannon divergence and agglomerative hierarchical clustering, to detect the genome-wide multi-locus interactions associated with multiple diseases.
A Performance Study of Some Approximation Algorithms for Computing a Small Dominating Set in a Graph
Article implements and tests the performances of several approximation algorithms for computing the minimum dominating set of a graph. This article belongs to the Special Issue: Algorithms for Hard Graph Problems.
Mining Potential Effects of HUMIRA in Twitter Posts Through Relational Similarity
Article investigating HUMIRA effects mentioned in Twitter posts using a relational similarity-based method. The authors were able to identify effects previously known as well as potentially unreported, which demonstrates the power of this method and its potential for studying effects of other medications shared by Twitter users.
Deriving Theorems in Implicational Linear Logic, Declaratively
This article aims to generate all theorems of a given size in the implicational fragment of propositional intuitionistic linear logic. It was presented at the 36th International Conference on Logic Programming (ICLP).
Fine-Grained Emotion Detection in Health-Related Online Posts
This article detects fine-grained emotion types from health-related posts and shows how high-level and abstract features derived from deep neural networks combined with lexicon-based features can be employed to detect emotions.
BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases
Article proposes a novel Bayesian method, named BAM, for simultaneously partitioning Single Nucleotide Polymorphisms (SNPs) into Linkage Disequilibrium(LD)-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient.
An Experiment-Based Review of Low-Light Image Enhancement Methods
Article reviews the current techniques of low-light image enhancement.
Accelerometer-Based Automated Counting of Ten Exercises without Exercise-Specific Training or Tuning
Article presents research that creates an automatic repetition counting system that is flexible enough to measure multiple distinct and repeating movements during physical therapy without being trained on the specific motion.
Detecting Negation Cues and Scopes in Spanish
Article addresses the processing of negation in Spanish by presenting a machine learning system that processes negation in Spanish and providing a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.
Determining Event Outcomes: The Case of #fail
Article presents research determining event outcomes in social media.
Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success
Article studies the communication styles present in chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. The authors find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success.
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