Journal of ICT Standardization

A Continuous Hidden Markov Algorithm-Based Multimedia Melody Retrieval System for Music Education

Yingjie Cheng — Tue, 21 May 2024 00:00:00 +0200

Education professionals receive instruction in Music Education (ME) to prepare for prospective jobs like secondary or primary music teachers, schools ensembles executives, or ensembles directors at music institutions. In the discipline of music education, educators do original research on different approaches to teaching and studying music. The most accurate and effective method of extracting music from huge music databases has become one of the most frequently discussed participants in contemporary multimedia information retrieval development. The essence of multimedia material is presented within a range of techniques since it is not bound to a single side. These several categories could consist of the song’s audio components and lyrics for musical information. Retrieving melodic information, subsequently, becomes the main focus of most recent studies. Aside from being an expensive deviate from academics, music programs are neither a viable profession neither a valid pastime. Therefore, in this study, we offer a Continuous Hidden Markov Algorithm (CHMA) related a novel method for recovering melodies from musical multimedia recordings. CHMA is considered to be the most basic dynamic Bayesian network. Two various types of audio frame features and audio example features are extracted throughout the feature extraction procedure from the audio signal according to unit length. Every music clip receives a unique approach that we implement with concurrently using various CHMA. The initial music gets processed using a trained CHMA that monitors fundamental frequencies, maps states, and generates retrieval outcomes. The training time for Traditional opera reached 455.76 minutes, the testing time for Narration achieved 56.10 minutes, and the recognition accuracy for advertisement reached an impressive 98.02%. A subsequently experimental result validates the applicability of the proposed approach.

Setting Standards for Personal Health Data in the Age of 5G and 6G Networks

Ana Koren, Ramjee Prasad — Tue, 21 May 2024 00:00:00 +0200

Electronic health records (EHRs) play a vital role in simplifying thorough and effective patient treatment, promoting smooth exchange of information between medical professionals, and enhancing the process of making clinical decisions. With the increasing adoption of sensor-embedded smart wearables and home automation devices, new opportunities arise for innovative solutions in various sectors, such as eHealth. In the age of 5G and 6G, the potential of utilizing user-collected health data becomes vast, promising significant improvements in people’s health and well-being. Realizing continuous healthcare access takes a step closer to reality by equipping EHRs to effectively store and interpret data collected by these sensors. This would result in personalized medical services that adhere to standardized practices. This paper presents a comprehensive review of contemporary advancements in the realm of standardization methods aimed at managing personal health data. The study delves into an extensive analysis of state-of-the-art solutions that have emerged to address the intricate challenges associated with the harmonization and uniformity of personal health information. By systematically examining these cutting-edge approaches, the review elucidates the diverse strategies employed to establish a cohesive framework for organizing, storing, and exchanging personal health data. Furthermore, the review critically evaluates the effectiveness and limitations of each solution in terms of promoting interoperability, safeguarding data privacy, and facilitating seamless data sharing among healthcare stakeholders. Furthermore, this paper then presents an approach to standardize the data by establishing semantic constraints for healthcare data types and proposing a validation procedure to ensure compliance with relevant standards and regulations.

Research on Task Scheduling for Internet of Things Cloud Computing Based on Improved Chicken Swarm Optimization Algorithm

Shizheng Liu, Xuan Chen, Feng Cheng — Tue, 21 May 2024 00:00:00 +0200

Aiming at the shortcomings of long completion time and high consumption cost of cloud computing batch task scheduling in IoT, an Improved Chicken Swarm Optimization Algorithm (ICSO) for task scheduling in cloud computing scenarios is proposed. Specifically, in order to solve the problems of slow convergence and falling into local optimum of the chicken swarm optimization algorithm, we adopt the nonlinear decreasing technique of the rooster and the weighting technique of the hen, optimize the following coefficients of the chicks, and apply ICSO to cloud computing task scheduling. In simulation experiments, we conducted a large number of experiments using four standard benchmark functions with different number of tasks and the results show that ICSO algorithm reduces 25.8%, 9.3%, 8.8%, 7.5% in small task time compared to CSO, DCSO, GCSO, ABCSO in large task time by 30.8%, 8.3%, 7.8%, 6.3%, 11.8%, 10.3%, 8.8%, 7.5% savings in small task cost and 25.8%, 11.2%, 10.8%, 9.3% savings in large task cost. This method effectively reduces task scheduling time and cost consumption. Meanwhile, we tested it in combination with an IoT-based cloud platform and achieved very satisfying Results.