Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Machine Learning Algorithms DOI Open Access
Muhammad Junaid Anjum, Fatima Tariq,

Khadeeja Anjum

et al.

VFAST Transactions on Software Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 312 - 325

Published: Dec. 31, 2024

The production of vaccines for diseases depends entirely on its analysis. However, to test every disease extensively is costly as it would involve the investigation known gene related a disease. This issue further elevated when different variations are considered. As such use computational methods considered tackle this issue. research makes machine learning algorithms in identification and prediction Single Nucleotide Polymorphism. presents that Gradient Boosting algorithm performs better comparison other genic variation predictions with an accuracy 70%.

Language: Английский

Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research Frontiers DOI Creative Commons
Kamran Razzaq, Mahmood Shah

Computers, Journal Year: 2025, Volume and Issue: 14(3), P. 93 - 93

Published: March 6, 2025

Machine learning (ML) and deep (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation innovation in various industries by integrating AI-driven solutions. Understanding ML DL is essential to logically analyse applicability identify their effectiveness different areas like healthcare, finance, agriculture, manufacturing, transportation. consists supervised, unsupervised, semi-supervised, reinforcement techniques. On other hand, DL, a subfield ML, comprising neural networks (NNs), can deal with complicated datasets health, autonomous systems, finance industries. This study presents holistic view technologies, analysing algorithms application’s capacity address real-world problems. The investigates application which techniques implemented. Moreover, highlights latest trends possible future avenues for research development (R&D), consist developing hybrid models, generative AI, incorporating technologies. aims provide comprehensive on serve as reference guide researchers, industry professionals, practitioners, policy makers.

Language: Английский

Citations

2
A Bibliometric Review of Trends and Insights of Internet of Things on Cybersecurity Issues DOI
Mushtaq Yousif Alhasnawi, Ahmed Abbas Jasim Al-Hchaimi, Yousif Raad Muhsen

et al.

Studies in computational intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 127 - 147

Published: Jan. 1, 2025

Language: Английский

Citations

0
BankNet: Real-Time Big Data Analytics for Secure Internet Banking DOI Creative Commons

Kaushik Sathupadi,

Sandesh Achar,

Shyam Bhaskaran

et al.

Big Data and Cognitive Computing, Journal Year: 2025, Volume and Issue: 9(2), P. 24 - 24

Published: Jan. 26, 2025

The rapid growth of Internet banking has necessitated advanced systems for secure, real-time decision making. This paper introduces BankNet, a predictive analytics framework integrating big data tools and BiLSTM neural network to deliver high-accuracy transaction analysis. BankNet achieves exceptional performance, with Root Mean Squared Error 0.0159 fraud detection accuracy 98.5%, while efficiently handling rates up 1000 Mbps minimal latency. By addressing critical challenges in operational efficiency, establishes itself as robust support system modern banking. Its scalability precision make it transformative tool enhancing security trust financial services.

Language: Английский

Citations

0
Leveraging machine learning for enhanced cybersecurity: an intrusion detection system DOI

Wurood Mahdi sahib,

Zainab Ali Abd Alhuseen,

Iman Dakhil Idan Saeedi

et al.

Service Oriented Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 11, 2024

Language: Английский

Citations

1
Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms DOI Creative Commons

Ali Alhchaimi

Wasit Journal of Computer and Mathematics Science, Journal Year: 2024, Volume and Issue: 3(2), P. 19 - 31

Published: June 30, 2024

Context: Cloud-based services are increasingly central in financial technology, enabling scalable and efficient transactions. However, they also heighten vulnerability to fraud, challenging the security of online activities. Traditional fraud detection struggles against sophisticated tactics, highlighting need for advanced, cloud-compatible solutions. Objectives: This study assesses machine learning (ML) algorithms' ability detect cloud environments, focusing on Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), XGBoost (XGB). It uses a comprehensive dataset determine which ML model best identifies fraudulent transactions, aiming optimize these models accuracy, precision, efficiency real-time detection. Results: The outperformed others with showing high effectiveness. These were particularly good at balancing precision recall, minimizing false positives, accurately identifying complex transaction patterns. Conclusion: ML, especially ensemble boosting like Forest, offers strong approach detecting cloud-based systems. Their capacity handle vast data volumes adapt new patterns enhances security. Implication: provides guide implement emphasizes importance continual innovation tackle digital finance suggesting that adopting advanced can significantly reduce risks, ensuring secure, efficient, trustworthy platform users.

Language: Английский

Citations

0
Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization DOI Creative Commons

Isam Sadeq Rasham

Wasit Journal of Computer and Mathematics Science, Journal Year: 2024, Volume and Issue: 3(4), P. 40 - 53

Published: Dec. 30, 2024

Context: In the age of rapid e-commerce growth; Robotic Mobile Fulfillment Systems (RMFS) have become major trend in warehouse automation. These systems involve use self- governed mobile chares to collect shelves as well orders for deliveries with regard optimization task allocation and reduced expenses. However, a manner implement such systems, one needs find enhanced algorithms pertaining resource mapping planning movement robots sensitive environments. Problem Statement: Despite RMFS certain challenges especially when it comes distribution tasks overall distances that employees cover. Objective: The main goal this paper is propose new compound model based on RL-ACO optimize RMFS’s assignment navigation. Also, direction study investigate how methods can be applied real-life automation effective large scale. Methodology: This research introduces selection which integrates reinforcement learning Ant Colony Optimization (ACO). Specifically, real gym environment was created perform order training way robotic movement. Reinforcement Learning (RL) models were trained Proximal Policy (PPO) improving dynamic control ACO used computing optimal shelf trajectories. performance also measured by policy gradient loss, travelled distance time taken complete tasks. Results: proposed framework showed potential enhancing efficiency required travel involved. each RL shortest paths identified best route determined total 102.91 units. other values as, value function loss convergence iterations. To build global solution, integration went step forward enabling through combinatorial problems solving. Implications: offers practical, generalizable flexible approach improvement operations thinking

Language: Английский

Citations

0
Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Machine Learning Algorithms DOI Open Access
Muhammad Junaid Anjum, Fatima Tariq,

Khadeeja Anjum

et al.

VFAST Transactions on Software Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 312 - 325

Published: Dec. 31, 2024

The production of vaccines for diseases depends entirely on its analysis. However, to test every disease extensively is costly as it would involve the investigation known gene related a disease. This issue further elevated when different variations are considered. As such use computational methods considered tackle this issue. research makes machine learning algorithms in identification and prediction Single Nucleotide Polymorphism. presents that Gradient Boosting algorithm performs better comparison other genic variation predictions with an accuracy 70%.

Language: Английский

Citations

0