Preferred Language
Articles
/
1hYcsIsBVTCNdQwCVdZr
Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
...Show More Authors

Soil compaction is one of the most harmful elements affecting soil structure, limiting plant growth and agricultural productivity. It is crucial to assess the degree of soil penetration resistance to discover solutions to the harmful consequences of compaction. In order to obtain the appropriate value, using soil cone penetration requires time and labor-intensive measurements. Currently, satellite technologies, electronic measurement control systems, and computer software help to measure soil penetration resistance quickly and easily within the precision agriculture applications approach. The quantitative relationships between soil properties and the factors affecting their diversity contribute to digital soil mapping. Digital soil maps use machine learning algorithms to determine the above relationship. Algorithms include multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), cubist, random forest (RF), and artificial neural networks (ANN). Machine learning made it possible to predict soil penetration resistance from huge sets of environmental data obtained from onboard sensors on satellites and other sources to produce digital soil maps based on classification and slope, but whose output must be verified if they are to be trusted. This review presents soil penetration resistance measurement systems, new technological developments in measurement systems, and the contribution of precision agriculture techniques and machine learning algorithms to soil penetration resistance measurement and prediction.

Scopus Crossref
View Publication
Publication Date
Mon Jan 11 2021
Journal Name
Engineering, Technology & Applied Science Research
Impact Resistance of Bendable Concrete Reinforced with Grids and Containing PVA Solution
...Show More Authors

The development of new building materials, able of absorbing more energy is an active research area. Engineering Cementitious Composite (ECC) is a class of super-elastic fiberreinforced cement composites characterized by high ductility and tight crack width control. The use of bendable concrete produced from Portland Limestone Cement (PLC) may lead to an interest in new concrete mixes. Impact results of bendable concrete reinforced with steel mesh and polymer fibers will provide data for the use of this concrete in areas subject to impact loading. The experimental part consisted of compressive strength and impact resistance tests along with a result comparison with unreinforced concrete. Concrete samples, with dimensions of 100×

... Show More
Publication Date
Sat Jan 01 2022
Journal Name
Ieee Access
Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithms for English Text Classification: A Systematic Review
...Show More Authors

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

... Show More
View Publication Preview PDF
Scopus (72)
Crossref (58)
Scopus Clarivate Crossref
Publication Date
Mon Jan 01 2018
Journal Name
Rehabend
Prediction of impact force-time history in sandy soils
...Show More Authors

Scopus
Publication Date
Mon Mar 09 2020
Journal Name
Agrosystems, Geosciences & Environment
In-season potato yield prediction with active optical sensors
...Show More Authors

Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference ve

... Show More
View Publication
Publication Date
Sun Aug 20 2023
Journal Name
International Journal Of Drug Delivery Technology
Role of higB-higA Novel Genes in Antibiotics Resistance of Pseudomonas aeruginosa
...Show More Authors

Background: Pseudomonas aeruginosa is a devious pathogen with the tendency to prompt many acute and serious chronic diseases. This study aims to detect novel genes (Toxins-Antitoxins II system), especially; higB and higA encoded from P. aeruginosa by PCR technique and the relation between these genes and antibiotic resistance of P. aeruginosa. Methods: This study detected 50 isolates of P. aeruginosa from distinct clinical sources. The most common origin of isolates was (44%) burn swabs, (22%) urine culture, (12%) wound swabs, (14%) sputum, and (8%) ear swabs. The bacteria were isolated using implantation MacConkey agar and blood agar, as well as biochemical tests including oxidase test, catalase test then VITEK-2 System of P. aerug

... Show More
View Publication
Scopus (1)
Scopus Crossref
Publication Date
Thu Sep 18 2025
Journal Name
Sustainable Engineering And Innovation
Using fruit fly and dragonfly optimization algorithms to estimate the Fama-MacBeth model
...Show More Authors

This research proposes the application of the dragonfly and fruit fly algorithms to enhance estimates generated by the Fama-MacBeth model and compares their performance in this context for the first time. To specifically improve the dragonfly algorithm's effectiveness, three parameter tuning approaches are investigated: manual parameter tuning (MPT), adaptive tuning by methodology (ATY), and a novel technique called adaptive tuning by performance (APT). Additionally, the study evaluates the estimation performance using kernel weighted regression (KWR) and explores how the dragonfly and fruit fly algorithms can be employed to enhance KWR. All methods are tested using data from the Iraq Stock Exchange, based on the Fama-French three-f

... Show More
View Publication
Scopus Crossref
Publication Date
Wed Jul 02 2025
Journal Name
Advances In Nonlinear Variational Inequalities
Suggesting Approximation and Exact Algorithms to Solve New Tri-Criteria Machine Scheduling Problems
...Show More Authors

This study presents the multi criteria single-machine model. The machine scheduling problem (MSP) for ntasks on a single machine involves minimizing a function of three criteria: total completion time (C_j),maximum earliest (E_max), and tardiness (〖ΣT〗_j), This is an NP-hard issue. Within this work's theoretical section, we present the mathematical formulation of The presented topic thenhighlights the usefulness of the dominance rule (DR), which may be used to develop effective solutions. Whilein the practical part, one of the important exact methods; The proposed MSP tricriteria are solved by applyingthe Branch and Bound (BAB) method, which finds a set of efficient solutions for 1//F(ΣC_j ,ΣT_j ,E_max) upto n=100 jobs. The BAB appro

... Show More
Publication Date
Mon Jul 01 2024
Journal Name
Journal Of Engineering
Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods
...Show More Authors

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

... Show More
View Publication Preview PDF
Scopus (5)
Crossref (1)
Scopus Crossref
Publication Date
Sun Feb 03 2019
Journal Name
Iraqi Journal Of Physics
Compressive strength measurement for cement replacement with recycled glass in concrete
...Show More Authors

The most important environmental constraints at the present time
is the accumulation of glass waste (transparent glass bottles). A lot of
experiments and research have been made on waste and recycling
glass to get use it as much as possible. This research using recycling
of locally waste colorless glass to turn them into raw materials as
alternative of certain percentages of cement to save the environment
from glass waste and reduce some of the disadvantages of cement
with conserving the mechanical and physical properties of concrete
made. A set of required samples were prepared for mechanical test
with different weight percentage of waste glass (2%, 4%, 5%, 6%,
8%, 10%, 15%, 20% and 25%). American standard

... Show More
View Publication Preview PDF
Crossref
Publication Date
Fri Sep 15 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Determine, Predict and Map Soil pH Level by Fiber Optic Sensor
...Show More Authors
Abstract<p>Soil pH is one of the main factors to consider before undertaking any agricultural operation. Methods for measuring soil pH vary, but all traditional methods require time, effort, and expertise. This study aimed to determine, predict, and map the spatial distribution of soil pH based on data taken from 50 sites using the Kriging geostatistical tool in ArcGIS as a first step. In the second step, the Support Vector Machines (SVM) machine learning algorithm was used to predict the soil pH based on the CIE-L*a*b values taken from the optical fiber sensor. The standard deviation of the soil pH values was 0.42, which indicates a more reliable measurement and the data distribution is normal.</p> ... Show More
View Publication
Scopus (16)
Crossref (6)
Scopus Crossref