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Articles 571 - 600 of 2075
Full-Text Articles in Computer Sciences
Computational Methods To Analyze Next-Generation Sequencing Data In Genomics And Metagenomics, Saidi Wang
Computational Methods To Analyze Next-Generation Sequencing Data In Genomics And Metagenomics, Saidi Wang
Electronic Theses and Dissertations, 2020-2023
This thesis focuses on two important computational problems in genomics and metagenomics with the public available next-generation sequencing data. One is about gene regulation, for which we explore how distal regulatory elements may interact with the proximal regulatory elements. The other is about metagenomics, in which we study how to reconstruct bacterial strain genomes from shotgun reads. Studying gene regulation, especially distal gene regulation, is important because regulatory elements, including those in distal regulatory regions, orchestrate when, where and how much a gene is activated under every experimental condition. Their dysfunction results in various types of diseases. Moreover, the current …
Challenges Of Constructing Entrainment Map For Arbitrary Circadian Models, Yuxuan (Nelson) Wu
Challenges Of Constructing Entrainment Map For Arbitrary Circadian Models, Yuxuan (Nelson) Wu
Honors Theses
The entrainment map, developed by Dr.Diekman and Dr.Bose, is claimed to be a 1-dimensional map that produces a better prediction for phase-locking than methods than the phase response curve for circadian models. In his paper, he constructs the entrainment map for the two-dimensional circadian model, the Novak-Tyson model, and the other two higher-dimensional circadian models. For this thesis, we concentrate on exploring if it is viable to construct the entrainment map for other circadian models that are not included in his paper: the Becker-Weimann model and the Relogio model. In addition, we discuss the challenges of constructing the entrainment map …
Artificial Intelligence System For Automatic Imaging, Quantification, And Identification Of Arthropods In Leaf Litter And Pitfall Samples, Pierce Helton, Khoa Luu, Ashley Dowling
Artificial Intelligence System For Automatic Imaging, Quantification, And Identification Of Arthropods In Leaf Litter And Pitfall Samples, Pierce Helton, Khoa Luu, Ashley Dowling
Inquiry: The University of Arkansas Undergraduate Research Journal
It is well known that arthropods are the most diverse and abundant eukaryotic organisms on the planet. Museum and research collections have huge insect accumulations from expeditions conducted over history that contain specimens of both temporal and spatial value, including hundreds of thousands of species. This biodiversity data is inaccessible to the research community, resulting in a vast amount of “dark data”. The primary objective of this study is to develop an artificial intelligence-driven system for specimen identification that greatly minimizes the time and expertise required to identify specimens in atypical environments. Successful development will have profound impacts on both …
Impact Of Sleep And Training On Game Performance And Injury In Division-1 Women’S Basketball Amidst The Pandemic, Samah Senbel, S. Sharma, S. M. Raval, Christopher B. Taber, Julie K. Nolan, N. S. Artan, Diala Ezzeddine, Kaya Tolga
Impact Of Sleep And Training On Game Performance And Injury In Division-1 Women’S Basketball Amidst The Pandemic, Samah Senbel, S. Sharma, S. M. Raval, Christopher B. Taber, Julie K. Nolan, N. S. Artan, Diala Ezzeddine, Kaya Tolga
School of Computer Science & Engineering Faculty Publications
We investigated the impact of sleep and training load of Division - 1 women’s basketball players on their game performance and injury prediction using machine learning algorithms. The data was collected during a pandemic-condensed season with unpredictable interruptions to the games and athletic training schedules. We collected data from sleep monitoring devices, training data from coaches, injury reports from medical staff, and weekly survey data from athletes for 22 weeks.With proper data imputation, interpretable feature set, data balancing, and classifiers, we showed that we could predict game performance and injuries with more than 90% accuracy. More importantly, our F1 and …
Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick
Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick
Complex Systems Faculty Publications and Presentations
An introduction to Reconstructability Analysis for the Discrete Multivariate Modeling course and for other purposes.
An Exploration On Apts In Biocybersecurity And Cyberbiosecurity, Xavier-Lewis Palmer, Lucas Potter, Saltuk Karahan
An Exploration On Apts In Biocybersecurity And Cyberbiosecurity, Xavier-Lewis Palmer, Lucas Potter, Saltuk Karahan
School of Cybersecurity Faculty Publications
Novel and complex digital threats that are increasingly interwoven with means and products of biology that can affect society. Much work in Biocybersecurity/Cyberbiosecurity (BCS/CBS) discuss vulnerabilities, but few deeply address malicious actor varieties as attacks at this intersection are new. The path to those attacks remains mostly theoretical, presenting considerable difficulty to accomplish in practical scenarios. In terms of advanced persistent threats (APTs) this of course needs to change as biomanufacturing facilities are at risk, considering Covid-19 and other potential pandemics. Further attacks are not out of reach and thus we must start to imagine how BCS APTs may appear. …
Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, And Beyond, Robert El-Kareh, Dean F Sittig
Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, And Beyond, Robert El-Kareh, Dean F Sittig
Faculty, Staff and Student Publications
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.
Far-Red Photography For Measuring Plant Growth: A Novel Approach, Cole Webb, F. Mitchell Westmoreland, Bruce Bugbee, Xiaojun Qi
Far-Red Photography For Measuring Plant Growth: A Novel Approach, Cole Webb, F. Mitchell Westmoreland, Bruce Bugbee, Xiaojun Qi
Techniques and Instruments
A critical part of agricultural studies is determining plant stress and growth rate. Modern computer vision provides a series of tools that can be applied to derive this data. In this paper, we will show our findings, analyze their accuracy, and define a system capable of deriving this data with near-human accuracy in a fraction of the time. Denoising techniques applicable to this system will be discussed, as will our discoveries and findings. Finally, suggestions for further research opportunities will be provided.
Application Of Artificial Intelligence To Plasma Metabolomics Profiles To Predict Response To Neoadjuvant Chemotherapy In Triple-Negative Breast Cancer, Ehsan Irajizad, Ranran Wu, Jody Vykoukal, Eunice Murage, Rachelle Spencer, Jennifer B Dennison, Stacy Moulder, Elizabeth Ravenberg, Bora Lim, Jennifer Litton, Debu Tripathym, Vicente Valero, Senthil Damodaran, Gaiane M Rauch, Beatriz Adrada, Rosalind Candelaria, Jason B White, Abenaa Brewster, Banu Arun, James P Long, Kim Anh Do, Sam Hanash, Johannes F Fahrmann
Application Of Artificial Intelligence To Plasma Metabolomics Profiles To Predict Response To Neoadjuvant Chemotherapy In Triple-Negative Breast Cancer, Ehsan Irajizad, Ranran Wu, Jody Vykoukal, Eunice Murage, Rachelle Spencer, Jennifer B Dennison, Stacy Moulder, Elizabeth Ravenberg, Bora Lim, Jennifer Litton, Debu Tripathym, Vicente Valero, Senthil Damodaran, Gaiane M Rauch, Beatriz Adrada, Rosalind Candelaria, Jason B White, Abenaa Brewster, Banu Arun, James P Long, Kim Anh Do, Sam Hanash, Johannes F Fahrmann
Faculty, Staff and Student Publications
There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied …
Computational Analysis Of Drug Targets And Prediction Of Protein-Compound Interactions, Sina Ghadermarzi
Computational Analysis Of Drug Targets And Prediction Of Protein-Compound Interactions, Sina Ghadermarzi
Theses and Dissertations
Computational prediction of compound-protein interactions generated a substantial amount of interest in the recent years owing to the importance of the knowledge of these interaction for drug discovery and drug repurposing efforts. Research suggests that the currently known drug targets constitute only a fraction of a complete set of drug targets, limiting our ability to identify suitable targets to develop new drugs or to repurpose current drugs for new diseases. These efforts are further thwarted by our limited knowledge of protein-drug (and more generally protein-compound) interactions, where only a subset of drug targets is typically known for the currently used …
Completing Single-Cell Dna Methylome Profiles Via Transfer Learning Together With Kl-Divergence, Sanjeeva Dodlapati, Zongliang Jiang, Jiangwen Sun
Completing Single-Cell Dna Methylome Profiles Via Transfer Learning Together With Kl-Divergence, Sanjeeva Dodlapati, Zongliang Jiang, Jiangwen Sun
Computer Science Faculty Publications
The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with …
An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang
An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang
Faculty, Staff and Student Publications
Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …
Hyperseed: An End-To-End Method To Process Hyperspectral Images Of Seeds, Tian Gao, Anil Kumar Nalini Chandran, Puneet Paul, Harkamal Walia, Hongfeng Yu
Hyperseed: An End-To-End Method To Process Hyperspectral Images Of Seeds, Tian Gao, Anil Kumar Nalini Chandran, Puneet Paul, Harkamal Walia, Hongfeng Yu
School of Computing: Faculty Publications
High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each …
Mass Spectra Of Mesonic Molecules At Finite Temperature, Arezu Jahanshir, Mohammad Reza Soltani
Mass Spectra Of Mesonic Molecules At Finite Temperature, Arezu Jahanshir, Mohammad Reza Soltani
Karbala International Journal of Modern Science
At a finite temperature, exotic hadronic systems, mass spectrum was studied using the quark structure and the radial Schrödinger equation with the Cornell interaction potential representing the hadronic interaction. The projective unitary representation has been used in the study of hadronic ground states in physics. A relativistic bound state's mass spectrum with temperature dependence is shown to have a unique feature. The resulting values are compared to experimental and theoretical values, and the study showed a high level of conformity with additional values wherever feasible.
Efficiency Of +Idonblender Photogrammetric Tool In Facial Prosthetics Rehabilitation – An Evaluation Study, Mohammed R. Falih, Fanar M. Abed, Rodrigo Salazar-Gamarra, Luciano Lauria Dib
Efficiency Of +Idonblender Photogrammetric Tool In Facial Prosthetics Rehabilitation – An Evaluation Study, Mohammed R. Falih, Fanar M. Abed, Rodrigo Salazar-Gamarra, Luciano Lauria Dib
Karbala International Journal of Modern Science
Several open-source 3D modeling software tools have recently been featured to reconstruct a 3D image-based model using Structure from Motion (SfM) algorithms such as +IDonBlender. It is, therefore, necessary to evaluate the accuracy of the models extracted from this tool to prove effectiveness for different applications. This paper aims to evaluate the +IDonBlender methodology tool in rehabilitating facial deformations. +IDonBlender is an add-on tool programmed within Blender software and designed for medical applications. In this research, two individuals from different genders have volunteered to contribute to this study, one with severe facial deformation and the other is healthy with no …
Proposed Hybrid Correlationfeatureselectionforestpanalizedattribute Approach To Advance Idss, Doaa Nteesha Mhawi, Prof. Soukaena H. Hashem
Proposed Hybrid Correlationfeatureselectionforestpanalizedattribute Approach To Advance Idss, Doaa Nteesha Mhawi, Prof. Soukaena H. Hashem
Karbala International Journal of Modern Science
NetworkIntrusionDetectionSystem(NIDS), widely used network infrastructure. Although many datamining has been used to increase the effectiveness of IDSs, current ID still struggle to perform well. therfore; proposed a new NIDS focused on feature_selection. The proposed CorrelationFeatureSelection_ForestPanalizedAttributes(CFS_FPA) used for dimensionality_reduction and selects the optimal_subset. based on two steps: first check each feature with a target(class) and choose only features that most effective by applying CFS filter using a statistical_method, then applied FPA to select only features will enhance ID and reduce_dimensionality. proposal tested with the NSLKDD experimental results of accuracy 0.997% and 0.004 FAR, wherein UNSWNB15_dataset accuracy and FAR are 0.995%, 0.008 …
Analysis Of Modern Communication Protocols For Iot Applications, Neerendra Kumar, Pragti Jamwal
Analysis Of Modern Communication Protocols For Iot Applications, Neerendra Kumar, Pragti Jamwal
Karbala International Journal of Modern Science
To facilitate increasing interactions of machines, various protocols are building up for IoT applications. Safeguarding communication among devices is necessary. In this paper, various protocols have been studied and compared using six parameters: Communication overhead, Security, Packet Loss, Throughput, Bandwidth and Support to QoS. LIDOR is found as the most successful communication protocol. A tabulated comparison of various communication protocols have been provided by comparing the parameters. Further, six star rating for the various protocols have been proposed. The study shows that LIDOR increases reliability under DoS attacks. Considering the existing literature, new research gaps and challenges have been presented.
The Effect Of Diode Laser On The Life And Appearance Of White Ant (Psammotermes Hypostoma), Estabraq Mahmood Mahdi Msc, Sahar Naji Rashid Msc., Saeed Maher Lafta Ph.D., Arshad Mahdi Hamad Msc
The Effect Of Diode Laser On The Life And Appearance Of White Ant (Psammotermes Hypostoma), Estabraq Mahmood Mahdi Msc, Sahar Naji Rashid Msc., Saeed Maher Lafta Ph.D., Arshad Mahdi Hamad Msc
Karbala International Journal of Modern Science
This work focused on studying effect of diode laser on external appearance of white ants, and finding the percentage of mortality resulting from this beam of (650nm),(5mW),exposure times (60,70,80,90,100)sec at (3,5)cm for each exposure. The results recorded showed a clear increase in death rates, and increase in deformations as laser exposure times increased (higher percentage at lower distance), where laser was used to capture results of this therapeutic effect after passage time periods (12,24,48,72)hours. These results, show laser thermal effects resulting from its interaction with tissues, thermal propagation leads to ravage to the structures, thus to an increase death rates.
Calculation And Comparison Of Certain Physical Properties Of Sample Irregular Galaxies With The Milky Way Galaxy, Hasanain Hassan Al-Dahlaki Msc
Calculation And Comparison Of Certain Physical Properties Of Sample Irregular Galaxies With The Milky Way Galaxy, Hasanain Hassan Al-Dahlaki Msc
Karbala International Journal of Modern Science
Irregular galaxies often include a mix of old and young celestial populations, as well as dust and gas. This study makes an early effort at the morphological classification of irregular galaxies. These sources have been chosen from (70) irregular galaxies data samples from a Catalogue survey. We present a statistical analysis of the physical properties for the chosen data from the database and a collection of tools that used to study the physics of galaxies and cosmology HYPERLEDA (http://atlas.obshp.fr/hyperleda/) and NASA /IPAC Extragalactic Database NED (https://ned.ipac.caltech.edu/) survey.
Seasonal Variation Of Microbiota In Shank Acanthopagrus Latus Living In Both Brackish And Fresh Water, Ali A. Al-Hisnawi Ph.D., Jassim M. Mustafa, Yass K. Yasser, Khalid A. Hussain, Ameera M. Jabur
Seasonal Variation Of Microbiota In Shank Acanthopagrus Latus Living In Both Brackish And Fresh Water, Ali A. Al-Hisnawi Ph.D., Jassim M. Mustafa, Yass K. Yasser, Khalid A. Hussain, Ameera M. Jabur
Karbala International Journal of Modern Science
The present study was conducted to monitor the microbiota in the posterior intestine of Shank fish living in the river (fresh water) and Al-Razzaza lake (salt or brackish water) in Kerbala, Iraq. Cultivable bacteria were calculated and identified from specimens obtained from the posterior intestine of mucosa (PM) and digesta (PD) during summer and winter seasons. The total culturable bacteria (TCB) of bacteria isolated from both intestinal regions from fresh water fish during summer time were higher than counterparts in winter time. In contrast, the TVC of bacteria isolated from the PM from salt water fish during winter time were …
Evaluation Of The Healing Activity Of Cucurbita Spp. Leaf And Seed Extracts On Experimental Thermal Burns, Sanaa Jameel Thamer Ph.D., Prof. Maha Khalil Ibrahim, Dr. Khalid Shanshal Alnema
Evaluation Of The Healing Activity Of Cucurbita Spp. Leaf And Seed Extracts On Experimental Thermal Burns, Sanaa Jameel Thamer Ph.D., Prof. Maha Khalil Ibrahim, Dr. Khalid Shanshal Alnema
Karbala International Journal of Modern Science
Burns are the most common skin injuries associated with many complications and high incidences of wound infections. Many compounds of plant products have been assessed to accelerate wound healing or reduce contamination. The possible healing effect of two Cucurbita spp. in skin burns was investigated. Cucurbita maxima and Cucurbita pepo leaves and seeds were extracted using ethanol and analyzed by gas chromatography–mass spectrometry. Second-degree burn wounds were made on laboratory rats, and the burn wounds were applied topically with extracts for 14 days and silver sulfadiazine (SS) for comparison. Serum vascular endothelial growth factor (VEGF) was analyzed post-treatment. Hydroxyproline content …
Energy Conservation Approach Of Wireless Sensor Networks For Iot Applications, Suha Abdulhussein Abdulzahra Msc, Ali Kadhum M. Al-Qurabat Ph.D., Prof. Dr. Ali Kadhum Idrees
Energy Conservation Approach Of Wireless Sensor Networks For Iot Applications, Suha Abdulhussein Abdulzahra Msc, Ali Kadhum M. Al-Qurabat Ph.D., Prof. Dr. Ali Kadhum Idrees
Karbala International Journal of Modern Science
Wireless Sensor Network is one of the most important contributors to IoT and performs significant role in people's lives due to its extensive use in many applications. Energy-saving is essential since sensor nodes are working by their restricted battery. In this article, data reduction method proposed to work at Gateway level of network. In GW, proposed method works as filtering via enabling GW to identify, then remove, sets of data that are redundant and produced by neighboring nodes. Principle idea of method recommended at this level is to exploit the advantage of spatial correlation between sensors to minimize energy depletion.
Biosynthesis, Characterization And Antiproliferative Activity Of Green Synthesized Gold Nanoparticles Using Lantadene A Extracted From Lantana Camara, Dawood Ali Salim Dawood, Lee Suan Chua Ph.D, Tian Swee Tan Ph.D, Ahmed F. Alshemary Ph.D
Biosynthesis, Characterization And Antiproliferative Activity Of Green Synthesized Gold Nanoparticles Using Lantadene A Extracted From Lantana Camara, Dawood Ali Salim Dawood, Lee Suan Chua Ph.D, Tian Swee Tan Ph.D, Ahmed F. Alshemary Ph.D
Karbala International Journal of Modern Science
This study was focused on the biosynthesis of gold nanoparticles loaded with lantadene A (LA-AuNPs) from Lantana camara extract and the antiproliferative effects on prostate cancer cells (LNCaP). The synthesized LA-AuNPs had smooth surface, and nearly spherical in shape (67.94 nm) with the zeta potential (-30.11±0.83 mV). LAAuNPs showed a dose dependent cytotoxic activity with IC50, 126.82 µg/mL against LNCaP, but non-cytotoxic to normal prostate cells. The intrinsic pathway of apoptosis was caused by the increase of caspases-3/7 and -9 activity and cell cycle arrest at the G0/G1 phase. Therefore, LA-AuNPs could be a potent lead to inhibit LNCaP
An Online E-Voting System Based On An Adaptive Ledger With Singular Value Decomposition Technique, Rihab Habeeb Sahib, Prof. Dr. Eman Salih Al-Shamery
An Online E-Voting System Based On An Adaptive Ledger With Singular Value Decomposition Technique, Rihab Habeeb Sahib, Prof. Dr. Eman Salih Al-Shamery
Karbala International Journal of Modern Science
Regular E-voting systems for elections may count the votes in less time,less cost,save the privacy of citizens,but still considered risky as votes can be tampered.E-voting systems based on a network distributed ledger show fast results,more trusted,save privacy,cannot be tampered,and distributed in which no central organization controls the system.This paper illustrate an e-voting system to solve the challenge of a massive ledger that is distributed among network-nodes using a data reduction technique as a security-matching-tool,singular value decomposition(SVD) that handle a copy of election results in another form and matched with the SQL-database results to announce a successful election-event representing a transparency-powerful-secured-system
Detecting Malicious Dns Queries Over Encrypted Tunnels Using Statistical Analysis And Bi-Directional Recurrent Neural Networks, Mohammad Al-Fawa'reh, Zain Ashi, Mousa Tayseer Jafar
Detecting Malicious Dns Queries Over Encrypted Tunnels Using Statistical Analysis And Bi-Directional Recurrent Neural Networks, Mohammad Al-Fawa'reh, Zain Ashi, Mousa Tayseer Jafar
Karbala International Journal of Modern Science
The exponential rise in the number of malicious threats targeting computer networks and digital services puts network infrastructure in jeopardy. Domain name protocol attacks are one of the most pervasive network attacks posing a threat to networks, whereby attackers send harmful information to the network; this type of threat is identified as DNS tunneling. The DNS protocol has recently gained increased attention from cyber-attackers, targeting organizations with a web presence or reliance on e-commerce businesses. Cyber-attackers can subtly exploit the contents of encrypted DNS packets that are sent across covert network tunnels, which are difficult for firewalls and blacklist detection …
Artificial Intelligence For Para Rubber Identification Combining Five Machine Learning Methods, Chairote Yaiprasert Ph.D.
Artificial Intelligence For Para Rubber Identification Combining Five Machine Learning Methods, Chairote Yaiprasert Ph.D.
Karbala International Journal of Modern Science
This study aims to identify Para rubber species using a combination of five machine learning techniques to classify leaf images. The learning process is defined using a dataset for each classification method. Approximately 1,472 leaf images are prepared consisting of various sizes, shapes, quality provided for the model. The classification indicators are defined with the help of an algorithm to identify at least three of the top five potential classification outcomes. The algorithm accurately predicts 100% of the five classification methods. Methods can provide precise and rapid classification of large quantities, without the need for image preprocessing prior to classification.
Synergistic Extraction Of Silver From Nitric Acid Medium With Dithizone In The Presence Of A Secondary Amine- Amberlite La-2 Using 110mag-Radioisotope, Madhusudan Mandal Ph.D.
Synergistic Extraction Of Silver From Nitric Acid Medium With Dithizone In The Presence Of A Secondary Amine- Amberlite La-2 Using 110mag-Radioisotope, Madhusudan Mandal Ph.D.
Karbala International Journal of Modern Science
Synergistic extraction of silver has been investigated in the presence of two different kinds of extractants, one of which is a chelating ligand, dithizone and the other is a long-chain secondary amine amberlite LA-2 at pH 3.0 using a radiotracer technique. The apparent formation constant, overall equilibrium constant and adduct formation constants were calculated from distribution coefficients. Interestingly, it was observed that the adduct formation constant is too high when the ligand concentration is increased by keeping the amine concentration fixed at 0.044 M compared to that obtained when the ligand concentration is kept fixed at 8.74×10-4 M.
Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler
Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler
Computer Science and Computer Engineering Undergraduate Honors Theses
Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …
Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy
Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy
All Dissertations
Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these …