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Research ArticleOpen accessIn the keywordsEmploying Electromagnetic Induction Technique for the Assessment of Soil CompactionKhalid A. Al-GaadiAn experiment on a sandy soil field was conducted to investigate the potential of determining soil compaction from apparent soil Electrical Conductivity (ECa) measured by Electromagnetic Induction (EMI) technique. A soil conductivity meter (EM38) was used to measure ECa under four soil Moisture Contents (MC) (4.965.336.94 and 8.0%) and a range of Soil Compaction (SC) levels (on the average, from … …American Journal of Agricultural and Biological Sciences, Vol. 7, Issue 4 · 7 Nov 2012 · 8,253 accessesView PDFCite
Research ArticleOpen accessIn the keywordsGeo-Spatial Technologies for Carbon Sequestration Monitoring and ManagementV. Jeyanny, S. K. Balasundram, M. H.A. HusniProblem statement: Globally, the quantification of Carbon Sequestration (CS) potential of various ecosystems was a challenge. There was an urgent need for technologies that can quantify CS potential cost-efficiently in a repeated and organized manner. Approach: Remote Sensing (RS) and Geographic Information System (GIS) had great potential in current estimation, future prediction and management of carbon sequestration potential in terrestrial … …American Journal of Environmental Sciences, Vol. 7, Issue 5 · 3 Nov 2011 · 8,633 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOptimization of Solenoid Valve for Variable Rate Application SystemSaleh Mufleh Al-Saqer, Ghulam Mubashar HassanProblem statement: The aim of this research was to optimize the performance of solenoid valve used in Variable Rate Application System (VRA) in term of time response. The overall time response is usually divided into four parts i.e., plunger opening time, pressure opening time, plunger closing time and pressure closing time. Approach: The performance and design of the a solenoid … …American Journal of Agricultural and Biological Sciences, Vol. 6, Issue 3 · 4 Aug 2011 · 8,654 accessesView PDFCite
Research ArticleOpen accessIn the abstractEffectual Deep Learning Based Digital Image Forgery Detection Model Using Ameliorate CNN, VGG 16 and VGG 19 for Copy Move and Image Splicing Classification With Enhanced PerformancePreeti Dhiman, Usha ChauhanIn this research, a pioneer passive approach for detecting and localizing tampering in images is proposed. In the past, image forgeries have created a huge impact in the world. Developments of various image processing technologies made it easier to tamper images with the objective to cover up or hide a significant feature in an image. In some situation, such as … …Journal of Computer Science, Vol. 22, Issue 10 · 3 Oct 2026 · 568 accessesView PDFCite
Research ArticleOpen accessIn the abstractAssessing the Comparative Effectiveness of Ensemble CatBoost Versus XGBoost Models in Predicting Heart Disease OutcomesManivannan D., G. Gifta Jerith, G. Chandra Sekhar et al.Cardiovascular Diseases (CVDs) are a common form of heart disease that remains a significant global cause of mortality, responsible for more than 30% of all fatalities. Without intervention, the global fatality count is projected to reach 22 million by 2030. Arterial plaque buildup may impede blood flow, potentially causing heart attacks or strokes. A combination of risk causes, including lack … …Journal of Computer Science, Vol. 22, Issue 4 · 4 May 2026 · 1,544 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Approach for Differentiating AI-Generated and Real Images Using Gabor and Color Layout Filters (GF and CLF)G. Ayyappan, A. Thankaraj, S. Surendran et al.The emergence of generative AI images has profoundly upended the art world. Distinguishing AI-generated images from human-created artwork is increasingly challenging. If this issue remains unresolved, dishonest individuals may exploit those who are willing to pay more for original artwork and businesses whose stated policies exclude AI graphics. There are a number of methods for differentiating AI photos from human … …Journal of Computer Science, Vol. 22, Issue 2 · 3 Mar 2026 · 1,751 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Edge Efficient and Lightweight Hybrid CNN Vision Transformer Network With CBAM for IoT Based Plant Disease DetectionNyabeye Pangop Doris-Khöler, Kengni Ngangmo OlgaDetecting crop disease early enough to act on it remains a bottleneck for smallholder and precision farming alike, particularly when diagnosis depends on manual inspection or lab analysis that cannot keep pace with an outbreak. We present AgriViTNet, a compact CNN-Vision Transformer hybrid built for exactly this constraint: It extracts local texture cues through convolution, relates them across the whole … …Journal of Computer Science, Vol. 22, Issue 9 · 20 Sep 2026 · 578 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Optimized YOLOv11-Based Deep Learning Framework With CNN Feature Enhancement for Tile Crack DetectionVinod Kumar Pal, Pankaj MudholkarThe tile business is a critical part of the national economic development, as it is one of the areas that provides employment, produces goods, and exports them. Regardless of its significance, there are challenges in the industry that are caused by issues with production, which in most cases is caused by low-quality materials used or by mishandling of the products … …Journal of Computer Science, Vol. 22, Issue 8 · 12 Aug 2026 · 979 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 accessesView PDFCite
Research ArticleOpen accessIn the abstractEvaluation of Machine Learning Models to Predict Student Academic Performance Using Structured Educational DataHardik Ishwarbhai Patel, Dharmendra PatelThis study analyses the use of machine learning for predicting the academic performance of students using their academic information from the institution, combined with socio-economic information that comes from outside sources. The collection of information is done using structured questionnaires as well as through data extraction from the Student Information System (SIS). To increase the reliability of models built, a … …Journal of Computer Science, Vol. 22, Issue 5 · 26 May 2026 · 1,419 accessesView PDFCite