Networking has become more common in recent years because it provides structural support and consistent avenues for contact among experts. The purpose of the study was to examine tutors’ and students’ perceptions and sustenance of networking in Food and Nutrition education in the Colleges of Education of Ghana. Quantitatively the study employed a cross-sectional survey research design. The population of the study comprised tutors and students in the Colleges of Education in Ghana offering Food and Nutrition. Purposive, stratified and simple random sampling techniques were used to select colleges of education, 16 tutors and 256 students for the study. The main instrument used for data collection was a questionnaire. The data collected were processed and analysed with the aid of Statistical Package for Social Sciences (SPSS) version 23.0. All statistical analyses were tested at a 5% level of significance. Levene's Test for Equality of Variances was computed to determine the significant difference in the perception of networking in Food and Nutrition education between tutors and students. It can be concluded that both tutors and students are in favour of networking in Food and Nutrition education. The tutors and students believed networking education can foster collaboration, help implement new ideas to improve the quality of teaching, create an environment conducive to teaching and learning; and help students in sharing vital information. Since there is a positive perception on the use of networking, it is recommended that the tutors should foster collaboration, and create a conducive environment to enhance the positive perception and smooth implementation of networking in Food and Nutrition education at Colleges of Education in Ghana. The study indicated that in order to sustain networking education, teachers need to be more cognizant of their interactions and the influence they have on students. It is therefore recommended that teachers maintain contact with students, and ensure a spirit of unity in diversity among the students.

Wireless networking is the connection of computers, digital communication devices, network equipment, and various other devices via radio waves. It is applied in places where the wired infrastructure cannot be installed or the price of introducing such a structure is too high. In addition, it has some features that are a great advantage over wired networking, such as customer mobility, easy expandability, and fast and low-cost temporary networking. Wireless technology allows us mobility and ease of use, but most users do not think about security. Users are insufficiently informed about the dangers of the Internet. Many of them do not pay attention to that and access important data such as bank accounts, e-mail, and any other contents that must be preserved and hidden. Today, there are more and more malicious actions, where hackers use various methods and technologies to attack users' accounts, bypassing all protections. Today, the issue of security is one of the priorities for every Internet user. Due to its characteristics, wireless communication is exposed to attacks due to the way they are sent, and there is a possibility of intercepting information.

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].