International Journal on Wireless, Networking & Mobile Communication Innovations [ISSN: 2581-5113 (online)]

Analysis of the using Digital Interactive Internet Resources to Enhance Learning Efficiency in the University

2023-05-05T09:07:39+00:00

While confirming the urgency of the problem identified in the title of this research article, first of all, it appears necessary for us to consider the requirement of proficiency in a foreign language for successful business activity. The use of interactive digital resources for this purpose in teaching a foreign language in a non-linguistic university is proven. Examples of the use of resources for control as well as building language skills are given. The techniques for working with certain resources and the possibilities of their use in both classroom and extracurricular work are described. Namely: the effectiveness of the use of such resources in teaching a foreign language.

Mobile Auto Silent Mode App (Triggerinfence)

2023-07-03T07:44:30+00:00

The usage of mobile devices such as smartphones and tablets has increased dramatically over the past years. Most of people carry at least one mobile device wherever they go. Mobile devices are becoming really important nowadays because they are usually the main tool for communications. However, sometimes the ringing sound of mobile devices can be a nuisance in certain circumstances such as during an important meeting or inside places that require silence such as library, cinema and prayer area. This problem occurs because most users forgot to switch their mobile device into silent mode. To address the problem, this paper presents a novel concept of automatically switching mobile devices into silent mode. This concept is developed based on the geo-fencing technique where a virtual fence will be created around a specific area. Whenever a mobile device crosses the virtual fence into the area, the device will be automatically switched into silent or vibrate mode. The device will be switched back to normal mode once it crosses the virtual fence to exit the area. This is done by utilizing the current location of the user based on the Global Positioning System (GPS) data provided through the device. The advantage of this application over other geofencing applications is that the geo-fence locations will be preloaded in the application, allowing applications with specific purpose and pre-determined locations to be developed

Smart Phone Comparison App

2023-11-28T06:25:31+00:00

The smartphone industry is marked by rapid technological advancements and an ever-expanding array of device options, making the process of choosing the right smartphone a challenging task for consumers. This comprehensive review paper delves into the realm of Smartphone Comparison Apps, an innovative solution to simplify the decision-making process. With a meticulous methodology involving app selection, data collection, and analysis, this review offers an in-depth assessment of these apps' features, functionality, and overall performance. We explore user feedback, ratings, and reviews to gauge user satisfaction and the impact of these apps on purchase decisions. Additionally, we investigate the apps' reliability in providing up-to-date information, their personalization capabilities, and the algorithms that power their recommendations.

A Study on AI and Unsupervised Learning Approaches for Clustered Analysis of Attacker Activities in IoT Data

2024-01-30T15:03:01+00:00

The realm of Internet of Things (IoT) has expanded its influence across various applications, spanning from compact wearable devices to large-scale industrial systems, delivering substantial benefits to humanity. With an escalating number of devices equipped with sensing and processing units, the increased interconnectedness poses a heightened risk of data breaches. Security and privacy concerns loom large over diverse applications utilizing IoT technology. The susceptibility of IoT data to malicious activities by attackers, who may compromise the integrity of the data by hacking into IoT devices, is a significant issue.

This paper introduces distinct clustering techniques aimed at identifying potential attacker activities within IoT data. Leveraging AI and Machine Learning techniques, the proposed approach clusters both attacker-modified data and authentic data. The utilization of Bayesian algorithm, Chi-square algorithm, and convergence algorithm contributes to training and validating a model designed to recognize such attacks on IoT data.