·         Table of Contents (Volume #9, Issue #1)

·         Cover Page (Volume #9, Issue #1)

·         Editorial (Volume #9, Issue #1)

Paper #1

Integrating Intelligent Web Scraping Techniques in Internship Management Systems: Enhancing Internship Matching

Hyrmet Mydyti and Andrew Ware

Abstract: The study explores the integration of intelligent web scraping techniques to enhance the internship matching process within internship management systems. The increasing demand for internships necessitates timely and efficient intern matching, a task that conventional manual techniques need help with due to its complexity and time-consuming nature. Intelligent web scraping algorithms and machine learning techniques analyze extensive datasets to match interns with businesses based on competencies, interests, and professional objectives. The integration leverages natural language processing to extract relevant information from internship listings and candidate profiles, enhancing the precision and effectiveness of the matching process. Additionally, clustering and matching algorithms refine recommendations, pairing students with opportunities that fit their competencies and career objectives. However, implementing intelligent web scraping raises ethical concerns, particularly regarding data privacy and algorithmic bias. Ensuring the ethical utilization of these techniques is critical for fair and unbiased internship matching. The research addresses these ethical considerations while proposing a framework for integrating intelligent web scraping into existing systems. The study reviews the literature on web scraping and machine learning in internship management, critically analyzing and synthesizing past research findings to demonstrate the efficacy of these techniques over conventional methods. The study also introduces a theoretical model for effective internship matching, investigating intelligent web scraping and machine learning techniques to optimize the process. Additionally, it examines the benefits, challenges, and limitations of integrating these techniques. The proposed intelligent web scraping approach simplifies internship matching, aligns student strengths with opportunities, enhances onboarding efficiency, and bridges academic learning with practical application.

Keywords: Businesses; Internship Management System; Internship Matching; Machine Learning; Natural Language Processing; Web scraping.

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Paper #2

Blockchain and NFC for Real Estate Certificate Management: An Albanian Case Study

Elva Leka, Luis Lamani, Arjol Lule and Klajdi Hamzallari

Abstract: This paper presents a novel framework designed to enhance the certification of real estate ownership and transactions through the integration of Ethereum blockchain technology, Near Field Communication (NFC), and smart contracts. The proposed architecture establishes a secure, transparent, and efficient digital certificate management system by leveraging immutable blockchain records, decentralized document storage via the InterPlanetary File System (IPFS), and NFC paper tags for effective physical-to-digital integration. The implementation employs encryption keys, Quick Response (QR) codes, and NFC tags to ensure data integrity and accessibility. A user-centric design has been developed to accommodate various stakeholders, including property owners, buyers, notaries, and land registry officials. This solution significantly improves upon traditional real estate transaction methods by facilitating end-to-end digital transactions, enhancing transparency and traceability, and substantially reducing the risk of fraud. By empowering participants to execute transactions and update records on a decentralized platform, this system fosters increased trust and operational efficiency within the Albanian real estate market. Furthermore, the design ensures compatibility with existing tax and payment procedures, providing a seamless transition for all stakeholders involved. The findings of this project aim not only to revolutionize real estate practices in Albania, but also to offer a scalable model for global implementation. By addressing the challenges associated with property certification and transactions, this innovative system contributes to a safer, more transparent, and efficient real estate environment. This framework is characterized by legal compliance, enhanced traceability, and robust fraud prevention mechanisms, ultimately paving the way for a modernized approach to real estate management in Albania and beyond.

Keywords: Blockchain; Cadastre; Ethereum; NFC; Smart Contracts.

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Paper #3

Impact of Radial Grounding Model Granularity on Directivity of 433 MHz Monopole Antennas with Flat and Inclined Radials for ISM IoT Applications

Jinfeng Li and Haolin Zhou

Abstract: Grounding is a critical factor in the performance of quarter-wave monopole antennas. Previous studies have explored finite, continuous grounding configurations with one- and two-dimensional variations in size for a 433 MHz vertical monopole antenna, identifying optimal geometries that maximize directivity while minimizing material costs and grounding size. However, these findings are not directly applicable to mission-critical environments (e.g., space, airborne, underwater, or ground-based applications) where continuous metallic grounding may be unavailable. This study extends the investigation to discretized grounding configurations, specifically employing radial monopoles formed by metal rods arranged in sparse or dense radial patterns. Both flat planar and inclined configurations of radial rods are analysed, with a focus on understanding the influence of design parameters, such as radial length and inclination angle, on antenna directivity and radiation patterns—key factors affecting signal reception in wireless communication systems, particularly in applications such as the internet of things (IoT). Using the Method of Moments (MoM) for simulation, the study provides practical guidelines for optimizing the design of 433 MHz monopole antennas constrained by finite and discrete grounding structures. The results indicate that an elevated radial configuration, consisting of five radials inclined at 5° from the monopole plane (equivalent to 85° from the horizontal plane) with a radial length of 2.5 meters, achieves a directivity of 9.23 dBi at 433 MHz. This represents a significant improvement over the flat planar configuration, which achieves a directivity of 6.23 dBi under the same conditions. These findings are particularly relevant for mobile communication, Internet of Things (IoT) devices, and radiofrequency (RF) systems requiring high performance from vertical monopole antennas in challenging grounding environments.

Keywords: Antenna directivity; Electromagnetics; Grounding in monopole antennas; Image theory; Internet of Things; ISM band; Radial monopole; 433 MHz.

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Paper #4

Research on Music Content Identification and Recommendation Technology Based on Deep Learning

Ping Li

Abstract: This research introduces a novel music recommendation system leveraging deep learning techniques to tackle significant challenges in traditional recommendation methods, such as the cold start problem, limited recommendation diversity, and difficulty in adapting to evolving user preferences. The proposed model employs Convolutional Neural Networks (CNNs) for genre recognition, coupled with Harmonic-Percussive Source Separation (HPSS) to extract rich audio features, capturing intricate musical distinctions across genres. These features, combined with user interaction data, enable the model to deliver highly personalized recommendations based on individual listening habits. Experimental results show that the system significantly outperforms conventional approaches, with a genre classification accuracy of 92%, offering greater recommendation accuracy and diversity. This marks a substantial improvement over traditional collaborative filtering and content-based methods, which struggle to deliver relevant suggestions in dynamic user environments. The findings highlight that deep learning, particularly CNNs, can effectively overcome data sparsity issues and provide more adaptive, user-centered recommendations. Moreover, the system's ability to integrate real-time user interaction data leads to enhanced user engagement, as the recommendations become more relevant and aligned with individual preferences. Future work will explore enhancing the dataset's diversity and optimizing computational efficiency to support scalability, ensuring the model can be applied across different cultures and regions. By improving the model's adaptability and efficiency, this research aims to create a more inclusive and scalable music recommendation system, capable of catering to global audiences with diverse musical tastes. Ultimately, the proposed system contributes to the development of more accurate, personalized, and engaging music recommendation frameworks, marking a significant advancement in the field of music information retrieval..

Keywords: Convolutional Neural Network; Deep Learning; Music Content Recognition; Recommendation Technology..

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Paper #5

Research on Unmanned Path Planning of Intelligent Vehicle Based on Swarm Intelligence Algorithm

Tao Yang, Dandan Song

Abstract: Unmanned vehicles represent a significant advancement in automotive technology, with their development hinging on sophisticated perception, decision-making, and control capabilities. However, existing path planning methods for driverless cars face challenges in complex road environments due to their susceptibility to environmental factors. This paper aims to address this issue by first providing an overview of trajectory planning algorithms for driverless cars. Subsequently, a novel global path planning approach is proposed, leveraging an improved A* algorithm and a predictive model of travel time. The proposed method enhances path planning accuracy by integrating the A* algorithm with predictive capabilities regarding travel time. By considering not only the shortest path but also the anticipated time required to traverse it, the model can account for dynamic factors such as traffic congestion and road conditions. This predictive aspect adds a layer of adaptability to the path planning process, enabling intelligent vehicles to make informed decisions in real-time. Simulation results demonstrate the efficacy of the proposed model in accurately planning trajectories for intelligent vehicles. The research results indicate that the prediction results of the bidirectional LSTM network are highly consistent with the actual values, demonstrating good predictive ability. From the perspective of prediction error, the MAE (Mean Absolute Error) of the bidirectional LSTM model is 7.3165, which is superior to the other three models. Especially compared with unoptimized BPNN, bidirectional LSTM reduced MAE, MAPE, and RMSE by 32%, 38%, and 3%, respectively, which fully demonstrates the advantages of bidirectional LSTM in processing time series data. It can accurately predict the inflow of road segments in real time and calculate the travel time of a future road segment.

Keywords: Autonomous driving; Bidirectional LSTM network; Path planning; Predicting inflow volume; Swarm intelligence algorithm; Vehicle trajectory.

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