The inefficient use of the IP network bandwidth is a fundamental issue that restricts the exponential spreading of Voice over IP (VoIP). The primary reason for this is the big header size of the VoIP packet. In this paper, we propose a method, called Short Voice Frame (SVF), that addresses the big header size of the VoIP packet. The main idea of the SVF method is to make effective use of the VoIP packet header fields that are unneeded to the VoIP technology. In particular, these fields will be used for temporarily buffering the voice frame (VoIP packet payload) data. This will make the VoIP packet payload short or even zero in some cases. The performance evaluation of the proposed SVF method showed that the use of the IP network bandwidth has improved by up to 28.3% when using the G.723.1 codec.

The revolution of Voice over Internet Protocol (VoIP) technology has propagated everywhere and replaced the conventional telecommunication technology (e.g. landline). Nevertheless, several enhancements need to be done on VoIP technology to improve its performance. One of the main issues is to improve the VoIP network bandwidth (BW) utilization. VoIP packet payload compression is one of the key approaches to do that. This paper proposes a new method to compress VoIP packet payload. The suggested method works over internet telephony transport protocol (ITTP) and named Delta-ITTP method. The core idea of the Delta-ITTP method is to find and transmit the delta between the successive VoIP packet payloads, which is typically smaller than the original VoIP packet payload. The suggested Delta-ITTP method implements VoIP packet payload compression at the sender side and decompression at the receiver side. During the compression process, the Delta-ITTP method needs to keep some values to restore the original VoIP packet payload at the receiver side. For this, the Delta-ITTP method utilizes some of the IP protocol fields and no additional header is needed. The Delta-ITTP method has been deployed and compared with the traditional ITTP protocol without compression. The result showed that up to 19% BW saving was achieved in the tested cases leading to the desired enhancement in the VoIP network BW utilization.

Aim/Purpose: This study aims to devise a personalized solution for online healthcare platforms that can alleviate problems arising from information overload and data sparsity by providing personalized healthcare services to patients. The primary focus of this paper is to develop an effective medicine recommendation approach for recommending suitable medications to patients based on their specific medical conditions. Background: With a growing number of people becoming more conscious about their health, there has been a notable increase in the use of online healthcare platforms and e-services as a means of diagnosis. As the internet continues to evolve, these platforms and e-services are expected to play an even more significant role in the future of healthcare. For instance, WebMD and similar platforms offer valuable tools and information to help manage patients’ health, such as searching for medicines based on their medical conditions. Nonetheless, patients often find it arduous and time-consuming to sort through all the available medications to find the ones that match their specific medical conditions. To address this problem, personalized recommender systems have emerged as a practical solution for mitigating the burden of information overload and data sparsity-related issues that are frequently encountered on online healthcare platforms. Methodology: The study utilized a dataset of MC ratings obtained from WebMD, a popular healthcare website. Patients on this website can rate medications based on three criteria, including medication effectiveness, ease of use, and satisfaction, using a scale of 1 to 5. The WebMD MC rating dataset used in this study contains a total of 32,054 ratings provided by 2,136 patients for 845 different medicines. The proposed HSMCCF approach consists of two primary modules: a semantic filtering module and a multi-criteria filtering module. The semantic filtering module is designed to address the issues of data sparsity and new item problems by utilizing a medicine taxonomy that sorts medicines according to medical conditions and makes use of semantic relationships between them. This module identifies the medicines that are most likely to be relevant to patients based on their current medical conditions. The multi-criteria filtering module, on the other hand, enhances the approach’s ability to capture the complexity of patient preferences by considering multiple criteria and preferences through a unique similarity metric that incorporates both distance and structural similarities. This module ensures that patients receive more accurate and personalized medication recommendations. Moreover, a medicine reputation score is employed to ensure that the approach remains effective even when dealing with limited ratings or new items. Overall, the combination of these modules makes the proposed approach more robust and effective in providing personalized medicine recommendations for patients. Contribution: This study addresses the medicine recommendation problem by proposing a novel approach called Hybrid Semantic-based Multi-Criteria Collaborative Filtering (HSMCCF). This approach effectively recommends medications for patients based on their medical conditions and is specifically designed to overcome issues related to data sparsity and new item recommendations that are commonly encountered on online healthcare platforms. The proposed approach addresses data sparsity and new item issues by incorporating a semantic filtering module and a multi-criteria filtering module. The semantic filtering module sorts medicines based on medical conditions and uses semantic relationships to identify relevant ones. The multi-criteria filtering module accurately captures patient preferences and provides precise recommendations using a novel similarity metric. Additionally, a medicine reputation score is also employed to further expand potential neighbors, improving predictive accuracy and coverage, particularly in sparse datasets or new items with few ratings. With the HSMCCF approach, patients can receive more personalized recommendations that are tailored to their unique medical needs and conditions. By leveraging a combination of semantic-based and multi-criteria filtering techniques, the proposed approach can effectively address the challenges associated with medicine recommendations on online healthcare platforms. Findings: The proposed HSMCCF approach demonstrated superior effectiveness compared to benchmark recommendation methods in multi-criteria rating datasets in terms of enhancing both prediction accuracy and coverage while effectively addressing data sparsity and new item challenges. Recommendations for Practitioners: By applying the proposed medicine recommendation approach, practitioners can develop a medicine recommendation system that can be integrated into online healthcare platforms. Patients can then utilize this system to make better-informed decisions regarding the medications that are most suitable for their specific medical conditions. This personalized approach to medication recommendations can ultimately lead to improved patient satisfaction. Recommendation for Researchers: Integrating patient medicine reviews is a promising way for researchers to elevate the proposed medicine recommendation approach. By leveraging patient reviews, the approach can gain a more comprehensive understanding of how certain medications perform for specific medical conditions. Additionally, exploring the relationship between MC-based ratings using an improved aggregation function can potentially enhance the accuracy of medication predictions. This involves analyzing the relationship between different criteria, such as medication effectiveness, ease of use, and satisfaction of the patients, and determining the optimal weighting for each criterion based on patient feedback. A more holistic approach that incorporates patient reviews and an improved aggregation function can enable the proposed medicine recommendation approach to provide more personalized and accurate recommendations to patients. Impact on Society: To mitigate the risk of infection during the COVID-19 pandemic, the promotion of online healthcare services was actively encouraged. This allowed patients to continue accessing care and receiving treatment while adhering to physical distancing guidelines and shielding measures where necessary. As a result, the implementation of personalized healthcare services for patients is expected to be a major disruptive force in healthcare in the coming years. This study proposes a personalized medicine recommendation approach that can effectively address this issue and aid patients in making informed decisions about the medications that are most suitable for their specific medical conditions. Future Research: One way that may enhance the proposed medicine recommendation approach is to incorporate patient medicine reviews. Furthermore, the analysis of MC-based ratings using an improved aggregation function can also potentially enhance the accuracy of medication predictions.

The use of Voice over Internet Protocol (VoIP) innovation is rising due to its various merits. Nevertheless, the ineffective use of bandwidth is a key dilemma that restricts the fast-rising use of VoIP innovation. The main factor behind this ineffective use of the bandwidth is the sizable VoIP packet preamble. This research creates a technique to address this dilemma of packet preamble. The created technique is known as payload elimination (PldE). The fundamental concept of the PldE technique is to exploit the information (elements) of the VoIP packet preamble that is superfluous for point-to-point calls. In general, these elements are utilized to transport the payload of VoIP packets. Consequently, the payload size of VoIP packet will be lowered or removed, preserving the available bandwidth. The performance test of the PldE technique indicated an improvement of up to 41.6% in the exploitation of IP network bandwidth. So, the PldE technique is showing signs that it could help solve the problem of the IP network's inefficient use of bandwidth.