Computer and Information Science, Issue: Vol.18, No.2

Neuro-Symbolic Reasoning for Cyber Compliance Violation Detection

Tue, 12 Aug 2025 07:41:56 +0000

This paper presents a neuro-symbolic framework for detecting cyber compliance violations by integrating deep neural networks with symbolic rule-based reasoning. Traditional machine learning models, while effective in identifying complex patterns, often lack interpretability, limiting their use in regulated domains where explainability is essential. Conversely, symbolic systems offer transparency but are rigid and difficult to scale. Our approach unifies these paradigms by jointly optimizing predictive performance and symbolic rule consistency. Compliance knowledge is encoded as Boolean constraints and incorporated during training as a regularization objective. The model fuses neural embeddings with rule satisfaction signals to improve both accuracy and interpretability. Evaluated on real-world cybersecurity datasets, our method achieves a 99.1% accuracy, 0.96 F1-score, and a reduced false positive rate, outperforming existing baselines. The framework also provides interpretable justifications by identifying violated rules, enhancing trust and auditability. These results demonstrate the feasibility and value of neuro-symbolic systems in scalable, explainable compliance monitoring.

An Innovative Mobile Application for Analyzing Learning Objectives Using Bloom's Taxonomy

Mon, 06 Oct 2025 10:45:26 +0000

Nowadays most learning institutions use Bloom’s Taxonomy to measure the students' understanding levels and to determine their understanding levels of the learning materials, as well as instructional strategies that will enable them to complete the activities successfully.

In this paper, we are proposing An Innovative Mobile Application for Analyzing Learning Objectives using Bloom's Taxonomy, which can facilitate the use of Bloom’s taxonomy and provide a lot of help for educators, Our tool supports curriculum and syllabus design by helping instructors select appropriate measurable action verbs aligned with Bloom’s Taxonomy levels (e.g., "analyze," "evaluate," "design"). It also assists in creating clear, outcome-based learning objectives for syllabi and lesson plans, while ensuring consistency in mapping Course Learning Objectives (CLOs) to Program Learning Outcomes (PLOs).

Smart Waste Management Systems: An IoT and AI-Driven Approach for Urban Sanitation

Mon, 06 Oct 2025 10:48:43 +0000

The explosive rise in city dwellers has created a growing scenario of waste management, presenting tremendous environmental, logistical, and health dilemmas to the urban regime. This study focuses on how a more innovative waste management system could be designed and implemented by integrating the IoT devices and Artificial Intelligence (AI) technologies to increase efficiency, transparency, and sustainability of a city's sanitation. The smart bins that would have been introduced in the proposed system would have incorporated IoT and sensors to help understand the current levels of waste, as well as the temperature and humidity of each bin. The transmission of these data is facilitated by low-power, wide-area network technologies such as LoRaWAN and NB-IoT to a centralized waste management system. Such AI algorithms include linear regression, random forest, and A* search, which analyze past and real-time data to refine waste collection schedules, estimate fill levels, and create smart route plans. Moreover, AI models that rely on computer vision can automate the segregation of waste into categories of allowable, organic, and non-recyclable materials with minimal human input. To ensure accountability and track waste throughout its lifecycle, blockchain technology is incorporated, providing tamper-proof records from collection to final disposal. This essay illustrates how the combination of IoT, AI, and blockchain has the potential to reinvent one of the oldest premises of waste management into a data-driven, flexible, and ecologically conscious system that embraces the circular economy concept. Pilot implementation has demonstrated how operational costs are reduced, the recycling rate is increased, and how fuel usage and emissions are significantly decreased.

Synergistic Optimization of Humanoid Robot Arm Configuration and Flexible Vision Measurement and Calibration System

Thu, 25 Dec 2025 09:19:29 +0000

The editorial board announced this article has been retracted on December 24, 2025. If you have any further question, please contact us at: cis@ccsenet.org Article Title: Synergistic Optimization of Humanoid Robot Arm Configuration and Flexible Vision Measurement and Calibration System Author/s: Yinjin Xiao, Shuzhen Huang, Yi Chen, Liangcheng Xiao, Peiyi Huang Journal Title: Computer and Information Science ISSN 1913-8989 E-ISSN 1913-8997 Volume and Number: Vol. 18, No. 2, 2025 Pages: 50-57 doi:10.5539/cis.v18n2p50

Malware – Common Attacks & Preventions

Fri, 31 Oct 2025 03:46:33 +0000

In this study, the problem of malware proliferation is examined with emphasis on the role of artificial intelligence (AI) in its formation and propagation. The objective of this research is to analyze common malware attacks, their mechanisms, and prevention strategies, drawing upon literature. Methods involve a qualitative review of reported cases and cybersecurity guidelines published between 2010 and 2024. Findings indicate that AI both exacerbates malware threats through adversarial attacks, automated code generation, and phishing automation and offers tools for improved detection and defense. AI-driven anomaly detection, machine learning based intrusion prevention, and adaptive defense systems show promise in mitigating advanced threats. The review also highlights gaps in governance, adversarial machine learning defenses, and protection for IoT and embedded systems. It concludes that addressing malware proliferation requires coherent frameworks, administrative controls, and AI oversight. Future research should prioritize zero-trust architectures, adversarial machine learning defense strategies, supply chain resilience, and governance policies to ensure sustainable and adaptive cybersecurity defenses.

Reviewer Acknowledgements for Computer and Information Science, Vol. 18, No. 2

Fri, 31 Oct 2025 05:02:41 +0000

Reviewer Acknowledgements for Computer and Information Science, Vol. 18, No. 2, 2025