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Sentiment analysis is a natural language processing (NLP) technique used to decide if the underlying sentiment is positive, negative, or neutral. Subjective information from the text can be extracted using sentiment analysis by recognizing its context and position. Data from a variety of sources, like social network comments, news stories, consumer reviews, and more, can be used for sentiment analysis. Sentiment analysis uses different algorithms to analyze words, phrases, and context available in text and different procedures to determine the overall sentiment communicated. There are various ways in which sentiment analysis is performed, ranging from rule-based methods that use lists of positive and negative terms as labeled data for training machine learning algorithms to building classifiers. Understanding social sentiment, underlying intents, and responses to various characteristics of humans can be done with the help of sentiment analysis, which helps in decision-making. The primary goal of this work is to provide the audience with the knowledge needed to understand sentiment analysis, highlight potential opportunities and challenges, and investigate recent studies that have been published in reputable resources focusing on the field of sentiment analysis in NLP.

The COVID-19 pandemic led to all institutions of education having to transition to fully online learning almost immediately. However, research showed that online learning still lacked adequate interactions with students. This is even more problematic when students are online learning on their own, when adequate online scaffolding activities are absent. This study investigated the impact of chatbots as a scaffolding agent to assist student learning during their independent online learning times. A total of 62 Diploma level students participated in this mixed method research study and presented with a multimedia-based AI chatbot named MERLIN. Data was collected on their attitudes towards using it. Results showed that students were motivated to learn more using MERLIN, improved their learning, and wanted more chatbots in their other courses. These findings have important implications for using AI chatbots as a scaffolding and instructional tool in 21st-century learning environments.

Background: Studies have acknowledged that social media enables students to connect with and learn from experts from different ties available in the students’ personal learning environment (PLE). Incorporating experts into formal learning activities such as scaffolding problem-solving tasks through social media, allows students to understand how experts solve real-world problems. However, studies that evaluate experts’ problem-solving styles on social media in relation to the tie strength of the experts with the students are scarce in the extant literature. This study aimed to explore the problem-solving styles that the experts portrayed based on their ties with the students in problem-based learning (PBL) on Facebook. Methods: This study employed a simultaneous within-subject experimental design which was conducted in three closed Facebook groups with 12 final year management students, six business experts, and one instructor as the participants. The experts were invited by the students from the weak and strong ties in their PLE. Hinging on the Strength of Weak Ties Theory (Granovetter, 1973) and problem-solving styles (Selby et al., 2004), this study employed thematic analysis using the ATLAS.ti qualitative data analysis software to map the experts’ comments on Facebook. Results: The experts from strong and weak ties who had a prior relationship with the students showed people preference style by being more sensitive to the students' learning needs and demonstrating firmer scaffolding compared to the weak ties' experts who had no prior relationship with the students. Regardless of the types of ties, all experts applied all manner of processing information and orientation to change but the degree of its applications are correlated with the working experience of the experts. Conclusion: The use of weak or strong ties benefited the students as it expedited their problem-solving tasks since the experts have unique expertise to offer depending on the problem-solving styles that they exhibited.

With the advent of the 5G digital era, cell phones are becoming ubiquitous in all aspects of our lives, and the increasing demand for remote interaction makes the app interaction experience an indispensable part of our lives. Due to the operational characteristics of gesture interaction in the interface of a smart terminal application (app), this mode of human-computer interaction has become the mainstream mode of human-computer interaction. Educational app is the result of a combination between mobile Internet technology and education, which not only provides a more efficient and convenient method of learning for each subject but also expands the possibilities for teaching each subject through intelligent interaction. On this basis, this paper proposes an educational app design method based on collaborative filtering recommendations and investigates ways to improve the use of mobile apps to create an interactive teaching mode. Simultaneously, this paper combines user activity, item popularity, and time factors to comprehensively measure user visibility of items and incorporates them into the collaborative filtering recommendation algorithm in order to effectively mitigate the effects of data sparsity and user selection bias and improve recommendation results.

The Internet of Things (IoT) is an interconnected system of physical objects that are embedded with different sensors (for receiving information), chips, software, and other techniques, which allow connecting and transferring of data to other devices via the Internet without human involvement. Since the number of IoT devices is increasing, large amounts of data are being generated from different sources in different formats. This information needs to be used effectively to gain useful insights for enhancing IoT security. Hence, big data techniques are proposed for managing the data to overcome different issues of IoT. Despite the outstanding achievements in IoT security, a systematic literature review (SLR) on implementing big data for securing IoT is lacking. The number of existing related SLRs is limited. Hence, this paper provides a systematic literature review on the use of big data for securing IoT devices. It summarizes the relevant literature produced during the last six years and provides meaningful insights gained by these existing studies. Moreover, it provides a discussion on the sources of IoT big data, the techniques and approaches of big data for securing IoT systems. Current challenges and future research directions are discussed. [ABSTRACT FROM AUTHOR]

In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge - the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology. [ABSTRACT FROM AUTHOR]

This paper presents a pioneering 2D spatial Optical Code-Division Multiple Access (OCDMA) encoding system that exploits Mode Division Multiplexing (MDM) and Multicore Fiber (MCF) technologies. This innovative approach utilizes two spatial dimensions to enhance the performance and security of OCDMA systems. In the first dimension, we employ Hermite-Gaussian modes (HG00, HG01, HG11) to modulate each user's signal individually. This unique approach offers a robust means of data transmission while ensuring minimal interference among users. The second-dimension leverages MCF encoding, introducing two incoherent OCDMA codes: the Zero Cross Correlation (ZCC) code (λc=0) and the ZFD code (λc=1). These codes are thoughtfully designed and simulated, taking into account their cross-correlation properties to guarantee minimal interference and heightened data security. To assess the efficiency of this novel OCDMA encoding system, we implemented simulations with three active users using the Opti system software. At the transmitter end, each user's signal is modulated individually by their designated HG mode (HG00, HG01, HG11), resulting in separate channels. Subsequently, at the multicore fiber, each user's data is encoded with a unique code-word, and they are directed through specific core groups, ensuring data isolation and integrity. In this paper, the BER and eye pattern are examined with respect to different parameters such as data rate and distance. At a distance of 5 km and data rate of 10 Gbit/s, a BER value around 10-70 is achieved. [ABSTRACT FROM AUTHOR]

The healthcare sector produces an enormous amount of complicated data from several sources, such as health monitoring systems, medical devices, and electronic health records. Big data analytics may improve healthcare by enabling more effective decision-making, improving patient outcomes, and reducing costs. To improve the operational efficiency of healthcare organizations, scientific studies must search for the standardization and integration of data analysis equipment and methods. This systematic literature review aims to provide current insights on the topic by analyzing a total of 60 relevant articles published between 2017 and 2023. The review explores the challenges and opportunities in using big data in healthcare, including data security, privacy, data quality, interoperability, and ethical considerations. The article also explores big data analytics' potential uses in healthcare, such as personalized treatment, disease prediction and prevention, and population health management. It provides significant insights for healthcare providers, researchers, and practitioners to make evidence-based decisions, as well as underlines the need for more research in this area to fully realize the promise of big data in healthcare. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a natural language processing (NLP) technique used to decide if the underlying sentiment is positive, negative, or neutral. Subjective information from the text can be extracted using sentiment analysis by recognizing its context and position. Data from a variety of sources, like social network comments, news stories, consumer reviews, and more, can be used for sentiment analysis. Sentiment analysis uses different algorithms to analyze words, phrases, and context available in text and different procedures to determine the overall sentiment communicated. There are various ways in which sentiment analysis is performed, ranging from rule-based methods that use lists of positive and negative terms as labeled data for training machine learning algorithms to building classifiers. Understanding social sentiment, underlying intents, and responses to various characteristics of humans can be done with the help of sentiment analysis, which helps in decision-making. The primary goal of this work is to provide the audience with the knowledge needed to understand sentiment analysis, highlight potential opportunities and challenges, and investigate recent studies that have been published in reputable resources focusing on the field of sentiment analysis in NLP. [ABSTRACT FROM AUTHOR]

Scholars propose that students should be empowered when using social media in problembased learning (PBL) as it is expected to provide positive learning experiences. Nonetheless, even though the use of social media has been described as empowering in PBL, not much research has measured students' experiences of the empowerment offered by multiple scaffolders through social media. In this study, 84 final-year undergraduate management students from a private university in Malaysia participated in a simultaneous within-subject experimental design to solve a business decision-making problem on Facebook. For eight weeks, multiple scaffolders, including an instructor, two business experts, and three to four peers, were present in each closed Facebook group. The students then answered a questionnaire following the PBL activity. Anchored on the Structural Empowerment (SE) Theory, this study evaluated the students' perceptions of the SE provided by these scaffolders. Subsequently, the relationships among the scaffolders were measured. The results were analysed descriptively and inferentially. Overall, the students reported positive learning experiences but perceived that the instructor and peers provided more SE than the business experts. However, the business experts' comments on the students' work and discussion points accelerated their problem-solving tasks. Furthermore, the SE provided by peers, instructors, and business experts correlated significantly, suggesting each scaffolder relied on each other when scaffolding. The findings indicate that all scaffolders, including the more experienced ones, should depend on the SE provided by other scaffolders to enable students to complete the complex business problem-solving tasks in PBL. [ABSTRACT FROM AUTHOR]

The healthcare sector produces an enormous amount of complicated data from several sources, such as health monitoring systems, medical devices, and electronic health records. Big data analytics may improve healthcare by enabling more effective decision-making, improving patient outcomes, and reducing costs. To improve the operational efficiency of healthcare organizations, scientific studies must search for the standardization and integration of data analysis equipment and methods. This systematic literature review aims to provide current insights on the topic by analyzing a total of 60 relevant articles published between 2017 and 2023. The review explores the challenges and opportunities in using big data in healthcare, including data security, privacy, data quality, interoperability, and ethical considerations. The article also explores big data analytics' potential uses in healthcare, such as personalized treatment, disease prediction and prevention, and population health management. It provides significant insights for healthcare providers, researchers, and practitioners to make evidence-based decisions, as well as underlines the need for more research in this area to fully realize the promise of big data in healthcare.