International Journal of Applied Sciences and Society Archives (IJASSA)

Enhancing Additive Manufacturing with Deep Learning: Predictive Modeling and Process Optimization Using the NIST Additive Manufacturing Material Database

2024-11-10T10:11:56+00:00

Bullying is an important issue in higher education, with heavy consequences for student's mental health, well-This study presents a predictive modeling approach to optimize additive manufacturing (AM) processes using a synthetic dataset based on the NIST Additive Manufacturing Material Database. A combination of regression and classification models were employed to evaluate key material properties and process parameters, aiming to improve AM output quality and reduce defect rates. Data preprocessing included normalization and correlation analysis to identify high-influence features, which informed feature selection for modeling. A Linear Regression model effectively predicted material behavior, achieving low Mean Squared Error (MSE) across training, validation, and test sets. A classification model was also developed to predict defect rates, yielding high accuracy, precision, and recall. Performance metrics, including a confusion matrix and ROC curve, underscored the model’s high specificity and sensitivity, indicating robustness in distinguishing between defective and non-defective outputs. Findings suggest that this approach has substantial potential for real-world applications in AM process optimization and quality control. However, further work involving complex modeling and real-world validation is recommended to enhance predictive accuracy and generalizability.

Leveraging EuPathDB Genomic Datasets with AI for Advancements in Molecular Parasitology: A Path to Data-Driven Discoveries

2024-11-10T10:33:46+00:00

This study leverages deep learning models, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to analyze genomic and gene expression data from the EuPathDB database for molecular parasitology applications. The CNN model demonstrated high efficacy in detecting pathogenic motifs within genomic sequences, achieving an accuracy of 86% and a balanced F1-score of 0.84, indicating strong potential for pathogenic feature identification in parasitic genomes. The LSTM model, while moderately accurate with a 79% test accuracy, effectively captured temporal patterns in gene expression relevant to infection stages, though it showed limitations in sensitivity that suggest avenues for further refinement. Confusion matrices and ROC curves provided insights into the classification accuracy and sensitivity of both models, indicating generalizability across parasite species. These findings highlight the potential for deep learning to transform data-driven parasitology research, with practical applications in genomic analysis, diagnostic support, and therapeutic target discovery. Future work should explore hybrid architectures and data augmentation techniques to enhance model robustness and accuracy.

Utilizing AI for Liver Cell Biology: Insights and Research Gaps through Analysis of the Human Protein Atlas (HPA) Liver Tissue Dataset

2024-11-10T10:45:10+00:00

This study investigates the use of artificial intelligence (AI) in liver cell biology by analyzing protein expression and localization patterns using the Human Protein Atlas (HPA) Liver Tissue Section dataset. Convolutional neural networks (CNNs) and multi-layer perceptron (MLP) models were employed to classify protein localization and predict expression levels, respectively. The CNN model achieved high test accuracy (87%) with balanced precision and recall, demonstrating strong performance in distinguishing cellular localization. The MLP model also achieved reliable predictions with a mean absolute error (MAE) of 0.14 on the test set. These findings highlight AI’s potential to advance liver-specific protein analysis, offering valuable insights for future research in liver biology and disease diagnosis. Future work could expand this framework to incorporate hybrid models for enhanced interpretability and accuracy.

Automated Incident Response using Deep Learning

2024-11-10T10:57:33+00:00

Cybersecurity threats are increasing in sophistication, requiring a shift from traditional manual incident response (IR) systems to automated approaches that can react more quickly and efficiently. This paper investigates the role of deep learning in automating incident response systems (AIRS), focusing on how advanced neural networks can enhance the detection, classification, and mitigation of cyberattacks in real-time. By leveraging deep learning architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, we conduct experiments on the NSL-KDD dataset to analyze their performance. Our results indicate that deep learning models significantly outperform traditional machine learning approaches, providing faster and more accurate responses to cyber incidents. This research highlights the potential of deep learning in redefining the landscape of cybersecurity through efficient, automated systems.

Agriculture Sector and Economic Development

2025-01-01T07:59:36+00:00

Pakistan is the developing country. It totally depends upon the agriculture. Agriculture is the strength of the economy. It contributes 28% of GDP. Pakistan export totally depend upon the agriculture sector. It exports cotton, rice, and wheat to the foreign country. This sector has also face many challenges like water scarcity, flood, drought, climate issues. Due to this problem the growth rate of the Pakistan is slow down. The demand of the food increase day by day due to the shortage of supply and over Population. The price rate of the wheat increase at the great level. The government should make such a policy to enhance the food productivity and production in the developing countries. By giving loan to the poor farmers, fertile land provides to the small farmers, introduce technology, special seeds, facilitate to the farmers, and better infrastructure which can easily move from one place to another, storage market.