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Computer Sciences & Mathematics Forum
ISSN 2813-0324 · MDPI AG · 277 artikel di indeks
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Software Architecture for Neurosymbolic Generative AI
Neurosymbolic AI promises to address the widening gap between logic and predictive inference in AI systems. It is the reaction to the dominance of deep learning neural networks and the lack of reasoning. Neurosymbolic models add human readable reasoning to neural networks and may address the…
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Explainable Intrusion Detection System Using Prototypical Network and Recursive Feature Elimination
This study explores the use of traditional machine learning and deep learning algorithms to develop efficient Intrusion Detection Systems (IDSs). It evaluates data using the NSL-KDD dataset, which contains both normal and attack traffic. The research compares the performance of various classifiers…
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Multifractal Analysis in Healthcare: A Review of Techniques, Applications, and Future Perspectives
Complex biological and medical systems often exhibit irregular and self-similar structures that can be effectively analyzed using fractal and multifractal frameworks. This study aims to provide a comprehensive overview of multifractal analysis as a mathematical tool for characterizing complex…
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Modelling SWRL Enabled OWL Ontologies for Neurosymbolic Computing in the Legal Domain
The rise of neurosymbolic AI promises to bridge the gap between learning technologies and symbolic computing and create models where predictive inference is complemented with logic reasoning and vice versa. Consequently, ontologies and knowledge graphs play important roles in defining neurosymbolic…
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Can You Trust Your Copilot? A Privacy Scorecard for AI Coding Assistants
The rapid integration of AI-powered coding assistants into developer workflows has raised significant privacy and trust concerns. As developers entrust proprietary code to services like OpenAI’s GPT, Google’s Gemini, and GitHub Copilot, the unclear data handling practices of these tools create…
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A Computational Model for Animal Language Processing: Translating Canine and Feline Behavior into Human-Readable Communication
Humans have always been curious about what animals are trying to communicate, especially our closest companions—dogs and cats. While we often rely on instinct and observation to understand their needs and feelings, this method can be inaccurate or limited. This research introduces a new…
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Heterogeneous Federated Learning Model for Recognizing Human Activity
A range of sensors are used by human activity recognition (HAR) to identify the activities that people complete each day. The recognition of human activities has benefited greatly from machine learning (ML), as it has made many human activities more easily recorded. Unfortunately, a centralized…
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DSGCNN-DA: A Deep Stacked Graph Convolutional Neural Network with Dynamic Aggregation for Malware Behavioral Learning
Malware remains a major threat to computer systems, posing serious risks to security and privacy by stealing sensitive data, disrupting services, and compromising system integrity. Traditional detection methods are often ineffective against rapidly evolving malware. In response, data-driven deep…
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Spectral Analysis of Neural Network Weight Matrices and the Impact of Weight Conditioning on Optimization Performance
This paper explores the relationship between random matrix theory (RMT) and the use of weight conditioning for training deep neural networks by employing an integrated framework. It has been shown that trained neural networks produce singular value distributions that follow universal distributions…
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Deep Learning Approaches for Efficient and Accurate DNA Sequence Alignment Using Large Language Models
This study addresses the challenge of DNA sequence similarity analysis by combining deep learning with DNABERT embeddings. Traditional alignment methods based on direct pairwise comparisons often fail to detect deeper biological relationships beyond nucleotide matching. However, DNABERT, a large…
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Encephalon_DC: Classification of Brain Diseases Using Deep Learning Techniques
The brain is the most complex organ in the human body, and neurological disorders pose significant diagnostic challenges. This study focuses on three prevalent conditions—Alzheimer’s disease, brain tumors, and Parkinson’s disease—collectively referred to as Encephalon Diseases. We propose a…
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Lightweight and Transparent Intrusion Detection in the Internet of Medical Things: The Role of Explainable AI
The rise of the Internet of Medical Things (IoMT) has transformed healthcare through real-time monitoring and improved outcomes but also introduced critical security and privacy challenges. This paper presents a focused survey of Explainable AI (XAI) approaches for intrusion detection in IoMT…
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Energy-Aware Bid-Based Client Selection for Federated Learning in Resource-Constrained IoT Networks
Federated learning (FL) enables distributed IoT devices to train machine learning models collaboratively without sharing raw data. However, energy heterogeneity among devices significantly challenges efficient and equitable participation, particularly in resource-constrained networks. This paper…
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Preface to the 1st International Conference on Emerging Tech & Innovation (ICETI)
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Blockchain-Based Secure Data Sharing in Cybersecurity: A Framework for Protecting Sensitive Information
With the growing volume of sensitive data stored and processed in cloud environments, conventional security models are no longer sufficient to guarantee privacy, integrity, and trust. This paper proposes a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) and homomorphic…
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Data Encryption Algorithms for Cloud Storage Systems—A Comparative Analysis
Cloud storage systems require strong and efficient encryption methods to ensure data security and reliability. However, selecting the most suitable encryption algorithm remains a challenge due to variations in performance, overhead, and reliability. This study aims to introduce a comparative…
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Advanced Machine Learning Approaches for Predicting ADHD in Females: A Data-Driven Study Employing the WIDS Dataset
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is found in both children and adults. While this disorder often continues in adulthood, diagnosis can be challenging, particularly in females. Unlike males, who are often diagnosed with ADHD due to their…
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A Comprehensive Analysis of Features, Benefits, Challenges, and Best Practices of Security Information and Event Management (SIEM) Solutions
Businesses need good defenses against any number of incidents in the continually evolving area of cybersecurity. SIEM (Security Information and Event Management) systems are now important tools for them. The current study offers a comprehensive analysis of SIEM solutions, such as their key…
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Visualizing Trends and Correlation Between Fashion Features for Product Design Prediction Using Classification and Sentiment Analysis
To demonstrate the interrelation of fashion elements for design forecasting, the research examines classification and sentiment analysis methodologies. The study combines survey data with information from social media and e-commerce sites to find important emotional and behavioral patterns that…