Research Publishing
With Editorial Care
A publication house dedicated to peer-reviewed scholarship across the Science, Engineering and Humanities
Empowering Voices. Advancing Knowledge.
Welcome to Resonara Publishers Pvt. Ltd., where groundbreaking ideas meet world-class publishing. We are a premier academic and literary publishing house dedicated to amplifying the voices of researchers, educators, scientists, and authors globally.
By blending rigorous editorial standards with cutting-edge digital distribution, we transform raw manuscripts into impactful publications that resonate across disciplines.
Why Partner with Resonara?
At Resonara, we believe that every manuscript holds the potential to inspire change, spark innovation, or advance human understanding. We don't just print journals; we nurture intellectual property.
Global Distribution Network
Your work reaches leading global libraries, academic repositories, and major online retailers.
Rigorous Editorial Excellence
Our dedicated team of editors and peer-reviewers ensures the highest standards of accuracy, academic integrity, and presentation.
Author-Centric Approach
We offer tailored support at every stage—from initial manuscript evaluation and professional formatting to post-publication marketing.
Diverse Publishing Models
We offer flexible publishing pathways across subscription-based, hybrid, and open-access journals.
Our Core Publishing Area
🔬 Scientific Journals
High-impact, peer-reviewed monographs and specialized journals spanning Science, Technology, Medicine, Humanities, and Social Sciences.
The Resonara Journey: How It Works
Submission
Share your manuscript or proposal with our editorial board via our secure portal.
Evaluation
Our experts review your work for quality, relevance, and market potential.
Production
Benefit from professional copyediting, typesetting, and stunning cover designs.
Distribution
Your work is published in print and digital formats, accessible worldwide.
Featured Journals
Browse active journals organized for readers, authors, editors, and reviewers.
Recent Research
Newly published articles from our journals.
Detecting and Quantifying Bias in Machine Learning Models
Mission Franklin — Rivers State University, Rivers State
Machine learning (ML) systems are increasingly being used to support decisions in areas such as healthcare, finance, education, hiring, and criminal justice. While these technologies can improve efficiency and accuracy, they may also produce unfair outcomes when biases present in data are learned and reproduced by algorithms. Such biases can lead to discrimination against certain individuals or groups, undermining trust in AI-driven systems. This study examines how bias can be detected and measured in machine learning models using established fairness metrics and evaluation techniques. A quantitative approach is adopted to assess model performance across different demographic groups. Metrics such as demographic parity, disparate impact, equal opportunity, and predictive parity are used to evaluate fairness. The findings indicate that machine learning models can exhibit significant disparities among demographic groups even when they achieve high predictive accuracy. The study emphasizes the importance of incorporating fairness assessments throughout the machine learning lifecycle and recommends the adoption of bias mitigation strategies to ensure more equitable outcomes. The research contributes to the development of responsible and trustworthy artificial intelligence systems.
ENSEMBLE MACHINE LEARNING MODELS FOR REAL-TIME NETWORK INTRUSION DETECTION USING SNMP DATA: A SYSTEMATIC REVIEW
Bukola A. Ifedayo-Ojo — Federal University of Technology, Akure, Nigeria.
Due to the rising sophistication and frequency of cyberattacks targeting business, cloud, Internet of Things (IoT), and software-defined network settings, Network Intrusion Detection Systems (NIDS) remain critical components of current cybersecurity architecture. Traditional intrusion detection systems (IDSs), which rely primarily on single machine learning algorithms or signatures, have limitations in identifying zero-day attacks, reducing false alarms, and adapting to changing network conditions. As a result, ensemble machine learning techniques have attracted significant interest due to their ability to improve detection accuracy and reduce falsepositive rates. However, their effectiveness is strongly dependent on the quality of the input data. Simple Network Management Protocol (SNMP) data offers a lightweight, scalable, and standardised alternative to packet-level traffic analysis in intrusion detection systems. This review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify, evaluate, and synthesise relevant articles published between 2015 and 2025 on the use of ensemble machine learning models for real-time network intrusion detection using SNMP data. This study analyses commonly used ensemble approaches, SNMP Management Information Base (MIB) features, datasets, evaluation metrics and computational efficiency issues. Electronic databases were searched with predefined Boolean searches. Results show that the most widely used methods for SNMP-based intrusion detection include Random Forest, AdaBoost, Gradient Boosting, XGBoost, and stacking ensembles. It also highlights issues such as dataset imbalance, computational overhead, lack of standardised SNMP datasets and real-time deployment limits. The study concludes that combining ensemble learning with SNMP-MIB data is a good approach to developing flexible and scalable intrusion detection systems for modern cybersecurity environments.
Designing Governance Frameworks for Trustworthy and Responsible Artificial Intelligence Systems
Mission Franklin — Rivers State University, Rivers State
Artificial intelligence is now being widely used in important real-world systems such as healthcare, finance, transportation, and public services. As these systems become more powerful and autonomous, concerns have grown about whether they are always transparent, fair, safe, and accountable in the decisions they make. This paper proposes a structured governance framework designed to support the development and use of trustworthy and responsible AI systems. The framework brings together ethical principles, technical safeguards, and regulatory requirements into a single integrated model. It draws on internationally recognized standards such as the OECD AI Principles and the European Union AI Act to ensure alignment with global best practices. The study presents a multi-layer governance structure that operates across four key dimensions: policy, technical design, system operations, and legal compliance. It also discusses practical challenges that organizations face when implementing AI governance, including bias in algorithms, differences in regulatory requirements across regions, and the difficulty of explaining decisions made by complex AI models. The proposed framework offers a comprehensive approach to ensuring that AI systems are not only effective, but also aligned with human values, ethical expectations, and institutional accountability.
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