Metallurgical and Materials Engineering (E-Journal)
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Comparative Performance Analysis of Different Nanotechnology Based Advance Roofing Materials
Full text linkNanotechnology in building materials, specifically for roofing applications. The current research delves into the incorporation of nanomaterials into roofing systems to improve mechanical strength, weather resistance, and thermal performance. This paper discusses the eligibility of nanotechnology for roofing system, recent developments, applications and future perspectives of these nanotechnology in roofing part. The comparative performance analysis of four advanced roofing nanomaterials is also the main focus of this paper
Advanced Information Retrieval Techniques in the Big Data Era: Trends, Challenges, and Applications
Full text linkInformation Retrieval (IR) has seen new potentials and challenges brought about by the fast growth of Big Information. We examine the present state of IR approaches and how they have industrialized to deal with the problems of organizing and deriving expressive deductions from large datasets. It dives into how mechanism learning techniques, deep learning replicas, and natural language dispensation (NLP) can enhance the exactness and velocity of data recovery. The study's comprehensive examination of current methods suggests that modified search engines, e-commerce, and healthcare have a lot of room to grow in terms of recovery accuracy, scalability, and significance. While highlighting ethical concerns counting data privacy and slide, the study explores novel requests in autonomous systems and modified AI helpers. Improving IR methods is vital in the Big Data era; future investigation should be on creating new procedures, using quantum computing, and concentrating on ethical AI does. The significance of IR progressions is highlighted in this study as a means to avoid Big Data's constraints and pave the way for new forms of novelty
Synthetic Cognition Meets Data Deluge: Architecting Agentic AI Models for Self-Regulating Knowledge Graphs in Heterogeneous Data Warehousing
Full text linkThe realities of contemporary data management and representation are evolving at an increasing rate. However, we still lack the broad foundational bridges of core data warehousing principles relating to how high-level reports are generated internally so that users can psychologically intuit where they are in the vast and complex repository of data that resides in a typical data warehouse. IT workers must constantly support users or worry about failed ad-hoc or automated operations or whose results appear without explanation. Data management may not yet exist as a science. We need a more complete transformational view of the details of the internal mappings between data from diverse sources and conceptual data model object types. Cognitive model-driven and symbolic techniques have been approached to design and develop systems to automate and rationalize these transformational processes and to support user navigation and work. These techniques are now being displaced by advanced statistical learning methods. As designed, these methods mostly do knowledge creation in the basic steps of the transformational process, but they likewise at times pave data as well. Through AI as Intentional Cognition supplemented by language, this inhibition may be bypassed. Thus, despite both their synthetic and agentic capabilities, these approaches follow a surprising and quite diverse transition. The goal of this work is to show what tasks of data management and representation these methods might be able to tackle and when and how they might interleave towards a more collaborative AI Data Management. We conclude with directions for future work
An Overview of the Normalization and Cartesian Product on Intuitionistic Fuzzy Matrices
Full text linkIn this paper, we present an Overview of the Normalization and Cartesian Product on Intuitionistic Fuzzy Matrices. In the case of discussion ordinary fuzzy matrices into normalization on intuitionistic fuzzy matrices. Also using the cartesian product x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11 we prove several important results for normalized intuitionistic fuzzy matrices
Optimal Fuel Consumption in Solid Oxide Fuel Cell based Hybrid Electric Tractor Using Improved Walrus Optimization Technique (IWaOT)
Full text linkThe global demand for electrical energy is rising rapidly due to industrialization and modernization. While fossil fuels are commonly used to meet this demand, their drawbacks—such as global warming, limited availability, and harmful emissions—restrict their long-term use. To address these challenges, there is a growing need for sustainable and environmentally friendly energy sources. Over the past two decades, fuel cells have gained significant attention as a renewable energy option due to their zero-emission operation and high efficiency. Among them, the solid oxide fuel cell (SOFC) stands out for its high operating efficiency and temperature. SOFCs are particularly advantageous because they can directly utilize natural gas. Known for their versatility and quick response, SOFCs are increasingly seen by manufacturers as a promising solution for generating electrical energy.
This research proposes an improved approach to enhance efficiency and reduce fuel consumption in Solid Oxide Fuel Cell-based Hybrid Electric Tractors (SOFC-HET). Central to this strategy is the use of the Improved Walrus Optimization Technique (IWaOT), a predictive controller designed to anticipate the tractor’s power demand and the fuel cell’s operating conditions. By leveraging these predictions, IWaOT optimizes key control parameters, including power distribution, fuel flow, air flow, and temperature. This targeted optimization not only reduces hydrogen fuel consumption but also improves overall efficiency and extends the fuel cell system's lifespan
Detection of Phishing Websites Using Machine Learning
Full text linkPhishing websites have proven to be a major security concern. Several cyberattacks risk the confidentiality, integrity, and availability of company and consumer data, and phishing is the beginning point for many of them. Many researchers have spent decades creating unique approaches to automatically detect phishing websites. While cutting-edge solutions can deliver better results, they need a lot of manual feature engineering and aren't good at identifying new phishing attacks. As a result, finding strategies that can automatically detect phishing websites and quickly manage zero-day phishing attempts is an open challenge in this field. The web page in the URL which hosts that contains a wealth of data that can be used to determine the web server's maliciousness. Machine Learning is an effective method for detecting phishing. It also eliminates the disadvantages of the previous method. We conducted a thorough review of the literature and suggested a new method for detecting phishing websites using features extraction and a machine learning algorithm. The goal of this research is to use the dataset collected to train ML models and deep neural nets to anticipate phishing websites
Prospects for Development of the Defense Sector of Central and Eastern Europe in the Conditions of Russian Aggression
Full text linkThe scientific article identifies the prospects for the development of the defense sector of the countries of Central and Eastern Europe in the context of Russian aggression. In particular, it is noted that the war in Ukraine has become a catalyst for the search by European countries that have struggled with balancing military power and the social security system. It is emphasized that the policy of upholding common security interests, intensive interaction of allied countries, strengthen the positions of all parties, strengthen the basis for bilateral and multilateral relations, and increase the arsenal of means for responding to common threats. The need to strengthen the European defense budget in general and individual countries of the European continent, the development of their military-industrial capabilities is indicated, and the prospects for deepening internal cooperation in the defense industry, the creation of a European army, subregional alliances of countries, the development of new formats of interaction with NATO and the European Union, and the establishment of partnership relations with the United States and allied countries are identified. The arguments are presented that the construction of joint defense enterprises for the production of ammunition with the help of partner countries, the expansion of their own production of new technical equipment, the involvement of NATO member states in exercises within the framework of the Northern European military alliance, the deployment of new forces of the North Atlantic Alliance in the region in order to prevent the occupation of countries or their parts will contribute to increasing the defense capability of the countries of Central and Eastern Europe in modern conditions. The conclusion is made that the countries of Central and Eastern Europe should resist today's changes in the security paradigm through more effective coordination of defense spending, increasing the role of the European Defense Agency and increasing financing of the defense industry
Emotion-Driven Intelligent Segmentation for Hyper-Personalized Customer Experiences: A Rule Mining Approach
Full text linkThe study examines the integration of emotion analysis and association rule mining to facilitate intelligent customer segmentation, aiming to deliver hyper-personalized experiences. A sample comprising 250 respondents has been utilized to analyse the relationship between emotional sentiment and behavioral attributes in shaping consumer preferences and engagement patterns. The analysis of sentiment and emotion has been employed to classify textual feedback into contemporary emotional categories, such as joy, anticipation, and anger, thus providing a nuanced understanding of customer perceptions. In combination, association rule mining was used to highlight encoded patterns and interconnections amongst the demographic characteristics, behavioral responses, and emotional states. The findings suggest a strong correlation between customer sentiments and emotional expressions, and also their purchasing tendencies, including a preference for personalized offers and engagement with AI-powered chatbots. The insights gained the identification of independent emotional-behavioral customer segments, each characterized by specific personalization requirements. The integration of rule-based approach with emotion mining in the study displays an effective technique for extracting actionable insights, thereby improving customer targeting and engagement strategies. The findings would help in developing scholarly discussion and practical implementations in marketing analytics, highlighting the significance of emotional intelligence in data-driven personalization
Examining the Role of Artificial Intelligence in Influencing Consumer Preferences and Purchase Intentions toward Green Fashion
Full text linkThe study investigates pivotal function of artificial intelligence (AI) in promoting sustainable fashion practices and influencing customer behavior. The garment business contributes 10% of global carbon emissions and produces substantial textile waste; thus, AI is a vital answer for mitigating environmental impact while preserving profitability. The study examines AI applications in industry (predictive analytics diminishing overproduction by 20-30%), design (virtual sampling minimizing material waste by 80%), and retail (AI-driven recommendations enhancing sustainable purchasing by 35%). The study uses Structural Equation Modeling (PLS-SEM) with information from 212 fashion customers to test a framework based on the Theory of Planned Behavior and the Stimulus-Organism-Response model. The important results show that what consumers know (β=0.482, p=0.045) and their trust (β=0.536, p=0.013) are key factors in their sustainable preferences, along with a very strong link between preference and intention (β=20.139, p<0.001). The measuring model has robust reliability (Cronbach's α > 0.88) and validity (AVE > 0.74); however, motivation displays minimal direct impact (β = 0.031, p = 0.735). The practical consequences underscore the necessity for honest AI communication, tailored sustainability suggestions, and investment in circular design tools. The limitations encompass the cross-sectional methodology and possible cultural disparities in AI adoption. Future studies should investigate longitudinal behavioural effects and do net sustainability evaluations of AI installations
An Integrated Supervised Learning Approach For High-Accuracy String Matching
Full text linkA fundamental problem in computer science is the string matching problem, which is the challenge of locating all instances of one string as a substring of another. Due to several applications in computational biology, this subject has recently got a lot of attention. Different ciphers are considered to speed up the search process in this research, which is a revised form of Horspool's string detection algorithm. The numerous pattern identification algorithms are used to locate all instances of a restricted set of patterns inside an input text or input file in order to examine the information of the documents. String matching can be done in one of two ways: exact matching or approximate matching. The proposed research focuses on employing an exact string matching using Inclusive Supervised Learning Model to develop a Accurate String Matching (ISL-ASM) that is an upgraded form of the Boyer-Moore-Horspool algorithm. When compared to traditional models, the proposed model's string matching accuracy is superior