Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management DOI Creative Commons
Maryam Bagheri, Mohsen Bagheritabar,

Sohila Alizadeh

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 296 - 296

Published: Dec. 31, 2024

The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding how these models are divided analyzed, leaving gaps in normalization benchmarking. present research usually overlooks holistic for comparing ML-enabled ISs, significantly considering pivotal function criteria like precision, sensitivity, specificity. To address gaps, we conducted broad exploration 306 state-of-the-art papers to novel taxonomy IS management. We categorized studies six key areas, namely systems, treatment-planning monitoring resource allocation preventive hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is most effective parameter throughout all models. In addition, majority were published 2022 2023, with MDPI as leading publisher Python prevalent programming language. This extensive synthesis not only bridges but also proposes actionable insights ML-powered

Language: Английский

Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks DOI Creative Commons

R. Augustian Isaac,

P. Sundaravadivel,

V. Marx

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 19, 2025

As the number of service requests for applications continues increasing due to various conditions, limitations on resources provide a barrier in providing with appropriate Quality Service (QoS) assurances. result, an efficient scheduling mechanism is required determine order handling application requests, as well use broadcast media and data transfer. In this paper innovative approach, incorporating Crossover Mutation (CM)-centered Marine Predator Algorithm (MPA) introduced effective resource allocation. This strategic allocation optimally schedules within Vehicular Edge computing (VEC) network, ensuring most utilization. The proposed method begins by meticulous feature extraction from network model, attributes such mobility patterns, transmission medium, bandwidth, storage capacity, packet delivery ratio. For further analysis Elephant Herding Lion Optimizer (EHLO) algorithm employed pinpoint critical attributes. Subsequently Modified Fuzzy C-Means (MFCM) used vehicle clustering centred selected These clustered characteristics are then transferred stored cloud server infrastructure. performance methodology evaluated using MATLAB software simulation method. study offers comprehensive solution challenge Cloud Networks, addresses burgeoning demands modern while QoS assurances signifies significant advancement field VEC.

Language: Английский

Citations

1
Smart Farming Revolution: A Cutting-Edge Review of Deep Learning and IoT Innovations in Agriculture DOI

J. Siva Prashanth,

G. Bala Krishna,

A. V. Krishna Prasad

et al.

Operations Research Forum, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 14, 2025

Language: Английский

Citations

1
A novel deep learning model for stock market prediction using a sentiment analysis system from authoritative financial website’s data DOI Creative Commons
Jitendra Chauhan, Tanveer Ahmed, Amit Sinha

et al.

Connection Science, Journal Year: 2025, Volume and Issue: 37(1)

Published: Jan. 24, 2025

Language: Английский

Citations

0
Optimizing cybercrime detection: A hybrid deep learning approach for enhanced intrusion detection systems DOI

Rima Alshaya,

Salim El Khediri

Peer-to-Peer Networking and Applications, Journal Year: 2025, Volume and Issue: 18(3)

Published: April 10, 2025

Language: Английский

Citations

0
An Efficient Multi‐Core DSP Power Management Controller DOI Creative Commons

Jian Huang,

Huili Wang, G. H. Gong

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(4)

Published: April 1, 2025

ABSTRACT Today's society has entered a digital era, and the use of DSP is becoming increasingly frequent important. In order to achieve market targets high energy efficiency, it necessary integrate low‐power design from chip stage. Based on FT‐xDSP architecture, this work designs power management controller for suitable multi‐core multi‐integrated peripherals perspective control in This can precisely supply, clock, memory each module introduces clamp unit solve problem possible glitches during asynchronous reset ensures that system no overflow redundant requests. Additionally, configurable state transition counter also set up avoid insufficient time low‐speed or long waiting high‐speed peripherals. After pre‐tapeout experiment post‐tapeout testing data analysis, above new manager excellent performance. low consumption chip, overall core reduced by over 95%, which great significance achieving high‐efficiency processor chips.

Language: Английский

Citations

0
An efficient electricity theft detection based on deep learning DOI Creative Commons
Nada M. Elshennawy, Dina M. Ibrahim,

Ahmed M. Gab Allah

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 15, 2025

Abstract Electrical theft is a pervasive issue that has detrimental impacts on both utility companies and electrical consumers worldwide. It undermines the economic growth of businesses, poses risks, affects customers’ expensive energy bills. Smart grids produce vast quantities data, including consumer usage data which crucial for identifying instances theft. Machine learning deep algorithms may use this to identify This research presents new approach using convolutional neural networks long-short-term memory extract abstract characteristics from power consumption improve accuracy detection electricity users. We mitigate dataset shortcomings, such as incomplete imbalanced class distribution, by LoRAS augmentation. The method’s efficiency evaluated authentic obtained State Grid Corporation China. Finally, we demonstrate competitiveness our when compared other approaches have been assessed same dataset. During validation process, attained 97% rate, surpassing highest reported in previous studies 1%. values 98.75%, 95.45%, 97.7%, along with corresponding recall F1 scores. findings indicate suggested surpasses existing state-of-arts methods.

Language: Английский

Citations

0
Leveraging stacking machine learning models and optimization for improved cyberattack detection DOI Creative Commons

Neha Pramanick,

Jimson Mathew, Shitharth Selvarajan

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 14, 2025

The ever-growing number of complex cyber attacks requires the need for high-level intrusion detection systems (IDS). While available research deals with traditional, hybrid, and ensemble methods network data analysis, serious challenges are still being met in terms producing robust highly accurate systems. There high hurdles managing high-dimensional traffic since current methodologies limited dealing imbalanced issues minority classes versus majority false positive rate classification accuracy. This study introduces an innovative framework that directly addresses these persistent through a novel approach to detection. proposed method integrates two ML models: J48 ExtraTreeClassifier classification. Besides, we propose improved equilibrium optimizer (EO) whereby previous EO is modified. In this enhanced (EEO), Fisher score accuracy K-Nearest Neighbors (KNN) algorithm select attributes optimally, whereas synthetic oversampling technique combined iterative partitioning filters (SMOTE-IPF) used provide class balancing. KNN also imputation improve overall system superior performance has been validated experimentally on several benchmark datasets, i.e., NSL-KDD, UNSW-NB15, achieving 99.7% 98.1% F1 99.6 98.0 respectively. By subjecting comparative analysis recent state-of-the-art works, paper shown methodology yields better improvement feature selection precision accuracy, handling instance, less demanding storage computational efficiency.

Language: Английский

Citations

0
A Hybrid Machine Learning Framework for Dynamic Resource Optimization in 5G Networks DOI

S. Umamaheswaran,

A. B. Gurulakshmi,

J. Mannar Mannan

et al.

Wireless Personal Communications, Journal Year: 2025, Volume and Issue: unknown

Published: May 14, 2025

Language: Английский

Citations

0
On the characteristics of next generation for redundant clustered reliable data transmission scheme in critical IoT infrastructures DOI Creative Commons
Grace Khayat, Constandinos X. Mavromoustakis, George Mastorakis

et al.

Discover Internet of Things, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 28, 2025

Language: Английский

Citations

0
Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm DOI Creative Commons
Mohamed S. Sawah, Hela Elmannai,

Alaa A. El‐Bary

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 30, 2025

Distributed Denial of Service (DDoS) attacks pose significant threats to network security, disrupting critical services by overwhelming targeted systems with malicious traffic. In this study, a machine learning-based approach is proposed classify DDoS using multiple classification models, including Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). The DDoS-SDN dataset was used for training evaluation, feature selection via Backward Elimination (BE) hyperparameter tuning Grid Search 5-fold Cross-Validation (CV = 5). Experimental results demonstrate improvement in performance after parameter optimization, RF achieving the highest accuracy 99.99%. we propose framework enhanced optimization techniques through employing Recursive Feature (RFE) .Our model based on (RF) achieved remarkable 99.99%, outperforming other baseline classifiers, Naive (98.85%), (97.90%), (97.10%), (95.70%). addition accuracy, also demonstrated superior F1 score, recall, precision, each reaching These validate effectiveness our strategy improving performance. study highlights engineering enhancing detection making learning viable solution real-time cybersecurity applications.

Language: Английский

Citations

0