Engineering Headway Vol. 35

Abstract: This research investigates the integration of Global System for Mobile Communications (GSM), laser technology, Arduino microcontrollers, and Light Dependent Resistors (LDR) to develop an active laser sensor security system for homes. The system activates when doors and windows are closed, whether locked or not, by utilizing an active laser detector connected to a microcontroller. This microcontroller interprets the detector's signal state, triggering an alarm through the laser and sending a call signal to a mobile phone via a GSM module if the laser beam is interrupted. By integrating these technologies, the paper offers a promising solution for improving home security and showcases potential innovative applications in electronics and telecommunications. The alert device automatically begins to ring whenever a person or object crosses in front of the laser light. The laser beam is nearly undetectable and may travel great distances without experiencing any scattering. When it detects any irregular activity, a laser security system can function as a stand-alone device that emits noise or commotion. It can also be a component of a larger security system or any other automation system that can notify users.

Abstract: To get precise in this paper, study anew fuzzy transform based on the Gupta transform is proposed , where exact solutions for fuzzy first-order differential equations are derived and explained in detail by proving the relevant theorems and properties and by presenting the solution of examples.

Abstract: The technology of Heterogeneous Wireless Sensor Networks (HWSNs) is critical to the efficient operation and deployment of a variety of real-time Internet of Things (IoT) and Mobile Ad hoc Networks (MANETs). It is crucial for reducing overall energy dissipation and ensuring consistent energy distribution throughout the network. Bio-inspired hybrid optimization algorithms are emerging as a possible option for overcoming basic difficulties in Wireless Sensor Networks (WSNs), with a focus on sensor lifespan restrictions. A significant topic that must be considered prior to network configuration is attaining energy efficiency and optimal communication. Several papers have been published on the use of bio-inspired algorithms in WSNs. Few articles, however, addressed the hybrid strategy for routing and clustering in WSNs with communication. This research focuses on hybrid bio-inspired optimization algorithms and elaborates on their taxonomy and problem domains in WSNs. Furthermore, we explored and investigated the hybridization of the Whale Optimization Algorithm (WOA) with other meta-heuristic algorithms such as the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and others. This review can assist researchers in exploring the uses of such algorithms within and outside of this study area.

Abstract: Simulation programs are considered one of the crucial tools in helping to test modern technologies in laboratories before applying them to reality. Optisystem is one of these important programs in the field of communications and data transmission. In this article , the optisystem program was used to simulate a telecommunications system to transmit data via an fiber optics for a distance of 40 km .Where the variable parameter was the power .Where the data was sent at the first power value, which is zero dBm, and both the Q. factor and the Bit Error Rate BER were recorded for each power value .The power was increased by 4 dBm each time .In return, the value of Q. factor and BER is recorded . Until the power value reaches 24 dBm .Where the value of the Q. factor becomes 0 and BER is 1.

Abstract: Several well-known Unmanned Aerial Vehicle (UAV) mobility models that make use of cellular networks will be compared in this study. The acquisition of services for ground-based User Equipment (UE) from Drone Base Stations (DBSs) is the primary focus of the examination. The four distinguishable mobility models— Random Waypoint (RWP), Straight Line (SL), Random Stop (RS), and Random Walk (RW)—are analysed and compared in this work. The UDM and UIM are two service models that are researched in this study. The primary contribution of this work is the development of a thorough method for investigating the point process of DBSs across different mobility and service models. The study compares the basic SL mobility model to more complex models that incorporate curved trajectories and finds performance disparities between the two. It also looks at the average session and received pricing of standard user equipment (UEs). The results of this study shed light on how well drone mobility models perform in cellular network settings, which can help with the development and refinement of drone-optimized cellular networks.

Abstract: This study presents a methodology for selecting the most preferred data mining algorithm for a construction project, leveraging the Analytical Hierarchy Process (AHP) .AHP, known for its application to complex decision-making problems, is adapted in this research to fit the context of data mining.The methodology involves significant modifications, including creating a collective decision-making environment that accommodates participants from diverse backgrounds and establishing a suitable data collection method tailored for AHP.The study contributes in two key areas. First, it designs and develops the methodology, enabling AHP to be effectively used for selecting data mining algorithms in construction projects. This adaptation considers the specific needs of the domain, allowing experts from different fields to contribute without requiring a comprehensive understanding of the entire model. Second, the methodology is applied to the problem, addressing existing limitations in the literature.By incorporating all relevant performance measures and leveraging expert knowledge, it facilitates informed decision-making even in the absence of extensive model testing data.The study's data was collected from two distinct participant groups: construction practitioners and machine learning experts, focusing on their personal preferences. This approach enhances the methodology's robustness and relevance to real-world applications. The proposed methodology demonstrated its effectiveness through various applications. A preference for Artificial Neural Networks (ANN) was observed in predicting concrete compressive strength, with a 59.4% weighting due to their capability to handle large datasets and non-linear relationships. In cost estimation tasks, Support Vector Machines (SVM) outperformed other models, receiving a 64.9% preference and achieving a lower mean absolute percentage error (MAPE) of 7.06%. The AHP-based approach maintained consistency across evaluations, with consistency ratios below 0.10, confirming the reliability of group judgments in the algorithm selection process.

Abstract: Writing matching has evolved dramatically from simple string comparison algorithms to sophisticated natural language processing techniques. This comprehensive literature review examines matching methods over the last 20 years, with special emphasis on transitioning from traditional frameworks to modern NLP methods to identify opportunities for practical theoretical integration and development exploring both models' fundamental principles, strengths and limitations. Our systematic review covers three main areas: (1) classical text matching algorithms, including Levenstein distance, Boyer-Moore, and Knuth-Morris-Pratt; (2) modern NLP techniques, such as transformer-based models and contextual ontologies; and (3) emerging hybrid approaches that seek to integrate these approaches. Intensive analysis of more than 40 papers from leading areas in information retrieval, natural language processing, and algorithmic evolution reveals key patterns in adopting text-matching strategies and highlights promising directions for future research. The study highlights a significant difference between the computational efficiency of traditional methods and the logical comprehension capabilities of modern NLP methods. Our study examines various attempts to bridge this gap and discusses the challenges and opportunities in integrating classical and modern approaches. We examine how different approaches manage the trade-off between computational complexity, logical clarity, and application-specific requirements.

Abstract: The exponential growth of virtual information presents unprecedented challenges for conventional records processing systems. This research explores the Hadoop surroundings as an innovative method to Big Data control, analyzing its architecture, talents, and strategic importance in cutting-edge data analytics. The take a look at investigates Hadoop's disbursed computing framework, which permits parallel processing of huge datasets throughout commodity hardware. Key additives including the Hadoop Distributed File System (HDFS), MapReduce programming version, and YARN aid control are analyzed to illustrate the platform's specific method to handling large, complex records workloads. Comparative analysis famous Hadoop's tremendous benefits over conventional systems, consisting of cost-effectiveness, scalability, and fault tolerance. The studies highlight the environment's evolution, from its origins to contemporary cloud-based implementations, and examines integration skills with equipment like Hive, Pig, and Spark that increase its analytical potential. While identifying challenges which includes operational complexity and security concerns, the observe in the long run positions Hadoop as a vital generation for agencies in search of to leverage Big Data for strategic selection-making. The findings underscore Hadoop's ability to convert information processing tactics, offering a sturdy, flexible option to the developing needs of current statistics-pushed companies.

Abstract: Data clustering is a critical data mining technique for grouping similar objects and differentiating dissimilar ones. While advancements in machine learning, statistical, and metaheuristic methods have addressed some challenges, issues like accuracy, efficiency, and scalability persist. Building on the History-Based Artificial Bee Colony (HD-ABC) algorithm, this paper introduces the Enhanced History-Driven Artificial Bee Colony (EHD-ABC) algorithm. Refining the historical memory mechanism and optimization process, the proposed algorithm achieves improved clustering accuracy, reduced computational complexity, and enhanced efficiency. Experimental results on artificial and real-world datasets demonstrate EHD-ABC's superiority over existing methods in clustering quality and error reduction, such as HD-ABC and K-means.

Abstract: Software-defined networking (SDN) infrastructures are facing a growing menace from cyber threats, which leave them vulnerable to distributed denial of service (DDoS) attacks. This study extends the earlier research [Detecting DDOS attacks in SDN Networks Using Machine Learning Techniques], which extensively investigated vulnerabilities in the SDN architecture for DDoS attacks. We develop and evaluate machine learning methods that are specifically tailored to protect software-defined networks (SDN) from such malicious attacks. The centralized and rigorous operational protocol of SDN has enabled us to develop a range of detecting methods. This study examines the efficacy of different machine learning algorithms, including XGBoost, Native Bayes, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). The algorithm's computational efficiency and precision were evaluated using a dataset specifically created to simulate the intricate and unpredictable characteristics of network traffic. The experimental findings demonstrate that the XGBoost and Random Forest algorithms exhibit commendable performance in terms of both accuracy and speed. The precision varies between 99.26% and 77.0%, depending on the specific detection algorithm employed and the selected features. Consequently, these algorithms are well-suited for promptly dealing with and reducing potential hazards. XGBoost demonstrated exceptional versatility by maintaining accuracy across several testing scenarios while also reaching great processing efficiency. Utilizing machine learning has the potential to significantly enhance the security of SDN systems, as indicated by the results. This revelation has two significant ramifications: This study enhances our comprehension of efficient SDN DDoS mitigation strategies and sets a standard for the next research on integrating machine learning in security frameworks. We offer essential knowledge that aids in preserving vital network infrastructures in an ever more unpredictable digital landscape.