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Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

This chapter presents ways in which educators can use technology to enhance learning for students with autism spectrum disorder (ASD). The chapter begins with an overview of the communicative and social challenges that students with ASD typically experience. Next, it defines technology-aided instruction and intervention (TAII) and offers suggestions for educators planning to incorporate TAII into their classroom. The third section offers specific supports on using TAII to enhance communication while the final section offers specific supports on using TAII to enhance affective and social learning. Case studies and tables with clearly defined supports are included throughout the chapter. Educators may combine the supports presented because each support addresses more than one characteristic or need. In addition, educators will find that these supports benefit students with neurodiverse learning needs who may not be identified with ASD.

This chapter presents an exploration of the phenomenon of speaking with, or perhaps better stated “through,” a device. Autobiographical works and other published accounts of perceptions of Speech-Generating Devices (SGDs) by persons who have used them are reviewed. The bulk of the chapter focuses on insights gathered from research into the lived experiences of young people who use SGDs. Emerging themes focus on what is “said” by a person who cannot speak, how SGDs announce one’s being in the word, the challenge of one’s words not being one’s own, and the constant sense of being out of time. Reflections on these themes provide insights for practice in the fields of speech language pathology, education, and rehabilitation engineering. The importance of further qualitative inquiry as a method to gather and listen to the voices and experiences of these often unheard individuals is stressed.

This chapter presents a combined approach of social and open data to evaluate a hybrid education model with online and face-to-face classes. The study consists of a sample of 310 students from the UPIITA-IPN college. Thus, a grouping model was applied based on each student's profile and academic performance in various subjects to identify patterns and learning styles. In addition, a social sensor of emotions was implemented to measure reactions in online and face-to-face classes. It helped to identify which strategies and methods are most significant for student performance. Data were collected from forms and the Twitter social network, filtering data by general opinions about learning and experiences in class. Considering trends and patterns, we identified four types:

Pattern (1) personalization of learning: This group stood out because online teaching allows you to work at your own pace and on your own schedule. In addition, a trend toward a more individualized learning approach or the versatility of personalizing learning was observed. Pattern (2) an excessive number of channels and information: This group of students was characterized by feeling overwhelmed by the amount of information they must process in an online environment, in addition, to using various communication channels (messaging, Classroom, Zoom, Teams, email, among others) this was associated with a feeling of isolation and a lack of commitment. Pattern (3) inequality and asynchronous learning: Students with difficult access to adequate resources at home (connection, own computer, etc.). They were characterized by not being able to have the same performance in the different learning activities and expressed that the content must be adapted to the individual needs of the students. Technical problems, such as Internet connection failures or problems with electronic devices, interrupted the learning process and generated frustration for students and teachers. Pattern (4) lack of social interaction: This affected the student's ability to develop social and emotional skills. Moreover, it generates difficulties for the students to collaborate, slowing the development of social and emotional skills. It concluded that a hybrid model is successful, having schemes combined with 65% face-to-face sessions and 35% online.

The impact of rapidly developing technologies on digitalization of production and planning has affected all sectors in a short period. This impact has led to both positive and negative outcomes. While it is helping the decision-makers to make better and more productive choices, it also can create a cluster of information and data that can put an undue burden on processes. Today, we call this Industry 4.0, which is defined as the use of digital technologies, based on data processing, the end-to-end connection of value chains, and ensuring data fluidity. Industry 4.0 consists of technologies such as cyber-physical systems, internet of things, cloud computing, big data and analysis, autonomous vehicles, and augmented reality. With the digitalization of the entire production and planning processes, data-based applications are made; thus it is important to protect data in this context, pointing to the critical importance of cybersecurity. Companies are constantly working on taking the necessary cybersecurity measures to prevent exposure to any cyberattacks. One of the biggest steps toward the development of production and planning is undoubtedly the integration and adaptation of blockchain technology. The use of blockchain technologies has been a major breakthrough for the sector in order to effectively meet customer needs, ensure information security, reduce costs, and achieve rapid growth. With digital transformation, production and planning must comply with the principle of transparency. In this context, integrating blockchain technologies into the production and planning ecology for data security will provide companies with a serious competitive advantage.