MATTER: International Journal of Science and Technology

WEB ATTACK DETECTION COMPARATIVE ANALYSIS OF LSTM AND DNN-BASED DEFENSE MODELS WITH CORRESPONDENCE ANALYSIS

Jaehyung Park — 2025-07-15

Recently since there exists more companies using web, web attacks to hijacking or manipulate the privacy information have increased. Among web vulnerability OWASP has introduced, SQL injection, XSS, File Inclusion have constantly occurred through more than a decade. It concludes that web servers have trouble with blocking old-fashioned web vulnerabilities. This paper is going to skim through web attack defending methods and compares existing web attack detection machine learning models and new ensemble model DPL with ANOVA, chi-square analysis, correspondence analysis to find out relativity between model and web attack. As result of correspondence analysis, brand new model DPL excels existing models but even DPL model have low relativity on XSS. It is expected that post research must introduce more XSS relevant model.

EXPLAINABLE PREDICTIVE ANALYTICS FOR SMART HEALTHCARE USING A MODULAR HYBRID INTELLIGENCE FRAMEWORK

Sérgio Silva — 2025-09-09

The exponential growth of healthcare data – driven by electronic health records (EHRs), wearable sensors, and continuous remote monitoring systems – has created both immense opportunities and complex challenges for modern clinical decision-making. Effectively harnessing this heterogeneous, high-volume, and high-velocity data requires intelligent systems that can not only deliver accurate predictions but also provide interpretable insights to support clinician trust and patient safety. In this paper, a modular hybrid computational intelligence framework designed to advance personalized, real-time healthcare analytics, is proposed. Our approach synergistically integrates deep learning for high-dimensional feature extraction, fuzzy inference systems for transparent reasoning under uncertainty, and genetic algorithms for adaptive optimization. This tri-layered architecture enables the system to learn from multimodal data sources, including physiological signals (e.g., ECG, glucose levels), structured clinical records, and unstructured patient-reported outcomes, to predict critical health risks such as cardiac arrhythmias, myocardial infarction, and diabetes-related complications. Through experimentation on publicly available real-world datasets, the proposed framework demonstrates superior predictive accuracy, enhanced interpretability, and computational efficiency compared to conventional machine learning and deep learning baselines. Importantly, the inclusion of fuzzy logic modules allows clinicians to trace back the reasoning paths of the system, addressing the growing demand for Explainable AI (XAI) in regulated healthcare environments. This research bridges the longstanding gap between model performance and transparency by offering a scalable and modular solution that is adaptable to diverse clinical contexts. By supporting proactive risk stratification and timely interventions, the framework has the potential to transform reactive care models into intelligent, preventative, and patient-centric healthcare delivery systems.

EFFECT OF ADJACENT SHADING ON BUILDING ENVELOPE HEAT GAIN IN TROPICAL CLIMATE

Yaik-Wah Lim — 2025-09-11

In tropical regions, approximately 60% of building energy is consumed by cooling systems, with heat gain through the building envelope being a major contributor. The Overall Thermal Transfer Value (OTTV) is a metric used to quantify average heat gain in air-conditioned buildings. However, the standard OTTV calculation does not account for shading from adjacent buildings—an increasingly common feature in high-density urban areas. This paper presents an empirical study on the thermal performance of building envelopes considering adjacent shading in tropical climates. Dynamic simulations of annual heat gain were conducted for buildings with and without adjacent shading for comparative analysis. The results highlight the significant impact of adjacent structures on heat gain performance and offer supplementary data to improve the current OTTV calculation method, especially for multi-block developments.

A STUDY ON SECURE PRIVATE SET UNION SCHEME IN IOT ENVIRONMENT

Ki-Hwan Kim — 2025-09-19

In the IOT environment, network edges have data, but a centralized server may need to obtain the union from edges for efficient data access. PSU is a cryptographic primitive that allows protocol parties to compute the union of their private datasets without revealing any extra information. Traditional PSU protocols presuppose that all parties must input their private datasets. This assumption doesn’t hold in some scenarios where an inputless Third party needs to get the union. For instance, a regulatory organization may need to get the union of patient data from hospitals for statistical analysis, without inputting any dataset. We propose a novel TP-PSU, specifically designed for a setting with three parties and an inputless Third party. Our protocol enables the Third party to compute the union, while preventing the leakage of any other extra information. This includes protecting the origin and duplication of each data item across edge nodes, thus maintaining privacy in the IoT environment.

COMPOSITION AND CHARACTERISTICS OF BRICK FOR CONSERVATION AND RESTORATION MATERIALS OF HERITAGE BUILDINGS AS AN INSPIRATION FOR SUSTAINABLE CONSTRUCTION

Hana Wardani Puruhita — 2025-12-03

To realize sustainable Cultural Heritage buildings and preserve cultural heritage, an appropriate concept of cultural heritage restoration and material appropriate to their function are needed. One of which is brick material for heritage buildings. Current bricks are different from heritage/old bricks, visually evident through their shape and colour. Heritage bricks must be studied to find their characteristics and composition so they can be used as cultural heritage restoration materials in the world. The composition and characteristics of heritage bricks have been studied and the results show that the physical and mechanical properties of heritage bricks vary. Therefore, research is needed on the development of renewable/repair heritage bricks as materials in the restoration of cultural heritage buildings, by identifying the characteristics and composition of renewable bricks. The methods applied include experiments and testing in the field and laboratory. The characteristics of renewable bricks in terms of physical strength have been designed with a size of 5.5x14x25.5 without shrinkage because they come from selected clay, have flat surfaces, sharp corners, and colours similar to heritage bricks. Tests were also conducted on the mechanical properties, such as water absorption, apparent density, and compressive strength, of all heritage bricks, and the best values for use as renewable materials were obtained.

EFFICIENCY ENHANCEMENT IN THE RECUPERATOR OF A REHEATING FURNACE

Alper Güneç — 2025-12-04

In today’s world, increasing environmental concerns have intensified the focus on renewable energy sources and effective energy management practices. In industrial processes, particularly in areas with high energy consumption, waste heat recovery has become one of the primary research topics. Reheating furnaces used in the steel industry reach extremely high temperatures during rolling operations, with approximately 50–70% of this energy being released directly into the atmosphere through flue gases. In this context, flue gases stand out as one of the most significant sources of waste heat. The aim of this study is to recover the waste energy contained in the flue gases emitted to the atmosphere from a slab reheating furnace used in the hot rolling process of the iron and steel industry by means of a recuperator. Through the newly implemented recuperator replacement, 31,274.93 kW of energy recovery was achieved and 6.32 tons of carbon emissions were reduced. Consequently, the amount of energy released into the atmosphere was decreased, and a significant reduction in natural gas consumption of the furnace was obtained.