Mobile learning (M-Learning) has become a popular and effective method of education that leverages the ubiquity of mobile devices. M-Learning has digitally transformed the process of teaching and learning. It has tremendous potential to empower all sections of society through education and training. This study presents a systematic literature review of M-Learning. The articles were retrieved from Scopus and Web of Science databases. After applying inclusion and exclusion criteria, a final selection of 161 articles published between 2016 and 2022 was included in the review. To analyze the articles, the researchers employed the TCCM (Theory, Context, Characteristics, Methods) framework, which facilitated addressing the research questions. This review identified various theories, such as behaviorism, constructivism, cognitivism, situated learning, problem-based learning, context awareness learning, socio-cultural theory, collaborative learning, conversational learning, lifelong learning, informal learning, activity theory, connectivism, navigation, and location-based learning, that are used to support and guide the implementation of M-Learning. In terms of context, developing countries contributed to 70.8% of the studies, while developed countries contributed to 29.1%. Further, a majority of the studies, 93%, involved students followed by faculty members and only two studies involved staff from higher education management. A total of 19 unique characteristic factors have been identified, such as personal, intention, attitude, usage, utility, ease of use, learnability, social, technological, pedagogical, anxiety, enjoyment, accessibility, knowledge, experience, trust, price, and habit. A quantitative research design was used in 90% of the studies, followed by mixed methods research design in 7% of the studies, and qualitative research design in only 3% of the studies. Further, this article synthesizes previous research findings and highlights gaps for future research. Overall, this review contributes to the understanding and advancement of M-Learning as a valuable educational platform.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%.

Nowadays, most applications hosted on public cloud data centers (DCs) disseminate data from a single source to a group of receivers for service deployment, data replication, software upgrade, etc. For such one-to-many data communication paradigm, multicast routing is the natural choice as it reduces network traffic and improves application throughput. Unfortunately, recent approaches adopting IP multicast routing suffer from scalability and load balancing issues, and do not scale well with the number of supported multicast groups when used for cloud DC networks. Furthermore, IP multicast does not exploit the topological properties of DCs, such as the presence of multiple parallel paths between end hosts. Despite the recent efforts aimed at addressing these challenges, there is still a need for multicast routing protocol designs that are both scalable and load-balancing aware. This paper proposes $\mathsf {Ernie}$ , a scalable load-balanced multicast source routing for large-scale DCs. At its heart, $\mathsf {Ernie}$ further exploits DC network structural properties and switch programmability capabilities to encode and organize multicast group information inside packets in a way that minimizes downstream header sizes significantly, thereby reducing overall network traffic. Additionally, $\mathsf {Ernie}$ introduces an efficient load balancing strategy, where multicast traffic is adequately distributed at downstream layers. To study the effectiveness of $\mathsf {Ernie}$ , we extensively evaluate $\mathsf {Ernie}$ ’s scalability behavior (i.e., switch memory, packet size overheads, and CPU overheads), and load balancing ability through a mix of simulation and analysis of its performances. For example, experiments of large-scale DCs with 27k+ servers show that $\mathsf {Ernie}$ requires a downstream header sizes that are $10\times $ smaller than those needed under state-of-the-art schemes while keeping end-host overheads at low levels. Our simulation results also indicate that at highly congested links, $\mathsf {Ernie}$ can achieve a better multicast load balancing than other existing schemes.

The multi-tenancy concept in cloud data center (DC) networks paves the way towards advancements and innovation in the underlying infrastructure such as network virtualization. Multicast routing is essential in leveraging multi-tenancy to its full potential. However, traditional IP multicast routing is not suitable for DC networks due to the need to support a massive amount of multicast groups and hosts. State-of-the-art DC multicast routing approaches aim to overcome these scalability issues by, for instance, taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets, thereby reducing the overhead resulting from the need to store the states of flows at the network switches. Although these approaches scale well with the number of multicast groups, they do not perform well with group sizes and, as a result, yield substantial traffic control overhead and network congestion. In this article, we present Bert, a scalable source-initiated DC multicast routing approach that scales well with both the number and size of multicast groups through the clustering of multicast group members where each cluster employs its own forwarding rules. Compared to the state-of-the-art approach, Bert yields much less traffic control overhead by significantly reducing packet header sizes and eliminating switch memory usage across the switches.

Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability. This article proposes an improved social media sentiment analytics technique to predict the individual state of mind of social media users and the ability of users to resist profound effects. The proposed estimation function tracks the counts of the aversion and satisfaction levels of each inter- and intra-linked expression. It tracks down more than one ontologically linked activity from different social media platforms with a high average success rate of 99.71%. The accuracy of the proposed solution is 97% satisfactory, which could be effectively considered in various industrial solutions such as emo-robot building, patient analysis and activity tracking, elderly care, and so on.

The widespread popularity of live streaming, cloud gaming, mobile video streaming, and many real-time applications relies on high-speed data to ensure low latency and seamless user experience. This large high-speed data demand has led to the development of next-generation or sixth-generation (6G) communication technology. It aims to offer high-speed communication support to multiple applications and interactive services simultaneously. But the vulnerability of node communication to the changing propagation environment often leads to call drops, data loss, and high latency. This paper presents a 6G-enabled wireless network that makes use of multiple intelligent reflecting surfaces (IRSs). The distributed IRSs enhance the robustness of transmission but the increased overhead owing to multiple IRSs is the main challenge. To overcome this, efficient resource control is introduced, which associates sets of IRSs to user equipment (UE). An algorithm, namely IUABP (IRS-UE association based on pilots), is proposed; it offers selective resource control. Furthermore, the performance of the distributed IRS system is evaluated based on the achievable sum rate for different IRS numbers, reflecting elements, and transmit powers. We observed that the proposed association scheme offers an improvement of 30% in the achieved sum rate using N = 50 and R = 5 at a transmit power of 12 dBm. We also discuss the comparison with two other association schemes, namely, distance-based association and random association.