Improving Environmental Sustainability: A Geolocation-Based Mobile Application to Optimize the Recycling Process

Environmental pollution caused by human activities is a global concern, and recycling is an effective strategy to reduce waste and minimize its negative impact on the environment, as these pollutants can have detrimental impact on ecosystems, human health, and the quality of life in general. Recycling avoids the accumulation of waste in landfills, which can contaminate soil, water, and air as well as reducing the production of new products. The objective of this work is to implement a mobile application to improve waste management by recycling companies. The Mobile-D methodology was used for the development of the project because it focuses on optimizing the efficiency and performance of mobile applications since it allows working in 5 phases which are: Exploration, Initialization, Production, Stabilization and Testing. In the first indicator (KPI-1), an improvement in customer retention was observed, with an increase of 114.29% in positive responses in the post-test. In the second indicator (KPI-2), there was a 39.92% decrease in response time, indicating a faster response in the collection service. In the third indicator (KPI-3), a significant increase of 86.86% in the volume of waste for recycling was observed. The results showed improvements in all indicators, indicating a positive impact of the implementation of the mobile application on waste management by the companies in the sector

Mobile support in CSCW applications and groupware development frameworks

Computer Supported Cooperative Work (CSCW) is an established subset of the field of Human Computer Interaction that deals with the how people use computing technology to enhance group interaction and collaboration. Mobile CSCW has emerged as a result of the progression from personal desktop computing to the mobile device platforms that are ubiquitous today. CSCW aims to not only connect people and facilitate communication through using computers; it aims to provide conceptual models coupled with technology to manage, mediate, and assist collaborative processes. Mobile CSCW research looks to fulfil these aims through the adoption of mobile technology and consideration for the mobile user. Facilitating collaboration using mobile devices brings new challenges. Some of these challenges are inherent to the nature of the device hardware, while others focus on the understanding of how to engineer software to maximize effectiveness for the end-users. This paper reviews seminal and state-of-the-art cooperative software applications and development frameworks, and their support for mobile devices

Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City

A smart city represents an advanced urban environment that utilizes digital technologies to improve the well-being of residents, efficiently manage urban operations, and prioritize long-term sustainability. These technologically advanced cities collect significant data through various Internet of Things (IoT) sensors, highlighting the crucial importance of detecting anomalies to ensure both efficient operation and security. However, real-time identification of anomalies presents challenges due to the sheer volume, rapidity, and diversity of the data streams. This manuscript introduces an innovative framework designed for the immediate detection of anomalies within extensive IoT sensor data in the context of a smart city. Our proposed approach integrates a combination of unsupervised machine learning techniques, statistical analysis, and expert feature engineering to achieve real-time anomaly detection. Through an empirical assessment of a practical dataset obtained from a smart city environment, we demonstrate that our model outperforms established techniques for anomaly detection

A New WRR Algorithm for an Efficient Load Balancing System in IoT Networks under SDN

The Internet of Things (IoT) connects various smart objects and manages a vast network using diverse technologies, which present numerous challenges. Software-defined networking (SDN) is a system that addresses the challenges of traditional networks and ensures the centralized configuration of network entities to manage network integrity. Furthermore, the uneven distribution of IoT network load results in the depletion of IoT device resources. To address this issue, traffic must be distributed equally, requiring efficient load balancing to be ensured. This requires the development of an efficient architecture for IoT networks. The main goal of this paper is to propose a novel architecture that leverages the potential of SDN, the clustering technique, and a new weighted round-robin (N-WRR) protocol. The objective of this architecture is to achieve load balancing, which is a crucial aspect in the development of IoT networks as it ensures the network’s efficiency. Furthermore, to prevent network congestion and ensure efficient data flow by redistributing traffic from overloaded paths to less burdened ones. The simulation results demonstrate that our N-WRR algorithm achieves highly efficient load balancing compared to the simple weighted round-robin (WRR), and without the application of any load balancing method. Furthermore, our proposed approach enhances throughput, data transfer, and bandwidth availability. This results in an increase in processed requests

Machine Learning Models Monitoring in MLOps Context: Metrics and Tools

In many machine learning projects, the lack of an effective monitoring system is a worrying issue. This leads to a series of challenges and risks that compromise the quality, reliability and sustainability of models deployed in production. As Machine Learning gains importance in various fields, poorly implemented monitoring represents a major obstacle to realizing its full potential. This article presents a comprehensive guide of machine learning models monitoring metrics and tool used in the MLOps context. The monitoring of metrics is important to evaluate and validate the performance of a machine-learning model, not only throughout the development phase but also during its deployment in the production environment. It enables real-time data to be collected on various metrics. The purpose of monitoring in MLOps context is to identify potential issues and adjustments made accordingly, guaranteeing consistent model quality and reliability. This article provides a comprehensive guide that introduces and explains a wide range of metrics used for continuous monitoring of ML systems at various stages of the MLOps lifecycle. Additionally, it presents a comparative analysis of available monitoring tools, enabling organizations to optimize their performance and ensure the seamless deployment of their machine learning applications. In essence, it underscores the critical importance of continuous monitoring and tailored metrics for ensuring the success and reliability of machine learning systems

Student Behavior Simulation in English Online Education Based on Reinforcement Learning

In class, every student's action is not the same. In this era, most courses are taken online; tracking and identifying students’ behavior is a significant challenge, especially in language classes (English). In this study, Student Behaviors’ Simulation-Based on Reinforcement Learning Framework (SBS–BRLF) has been proposed to track and identify students’ online class behavior. The simulation model is generated with various trained sets of behavior that are categorized as positive and negative with Reinforcement Learning (RL). Reinforcement learning (RL) is a field of machine learning dealing with how intelligent agents act in an environment for cumulative rewards. With a web camera and microphone, the students are tracked in the simulation model, and collected data is executed with RL’s aid. If the action is assessed as good, the pupil is praised, or given a warning three times, and then, if repeated, suspended for a day. Hence, the pupil is monitored easily without complications. The research and comparative analysis of the proposed and the current framework have proved that SBSBRLF works efficiently and accurately with the behavioral rate of 93.2%, the performance rate of 96%, supervision rate of 92%, reliability rate of 89.7 % for students, and a higher action and reward acceptance rate of 89.9 %

A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development

The Impact of Implementing a Moodle Plug-in as an AI-based Adaptive Learning Solution on Learning Effectiveness: Case of Morocco

This article presents feedback on the implementation of an Artificial Intelligence-based adaptive learning Moodle plugin aimed at enhancing the engagement levels and academic performance of 102 Moroccan high school students. The primary objective of this study was to assess and compare the performance of students utilizing the adaptive learning system with those employing conventional learning methods. To guarantee the efficacy of this approach, a participant satisfaction survey and a comprehensive summative evaluation were conducted, revealing the positive impact of AI-based adaptive learning on the participants. The results of this study highlight the potential benefits of integrating AI-driven adaptive learning into high school computer science curricula, emphasizing how it may raise student engagement and academic performance. These results strengthen the determination to use this teaching methodology with students in future educational activities

AUGMENTED REALITY ENGINE MANAGEMENT SYSTEM YANG BERBASIKAN ANDROID PADA PELAJARAN KELISTRIKAN SISTEM KONTROL INJEKSI

Berdasarkan hasil observasi diperoleh bahwa tidak banyak siswa yang lulus kompetensi perawatan system kontrol injeksi. Kompetensi kelistrikan memerlukan kemampuan mengidentikasi letak komponen kelistrikan, nama komponen, dan cara kerja sistem injeksi. Platform android dapat dipergunkan sebagai media pembelajaran sistem kontrol mesin injeksi yang berbasiskan augmented reality. Perkembangan pendidikan berubah seiring dengan perkembangan teknologi saat ini dan mempengaruhi proses pembelajaran, metode pembelajaran. Teknologi Augmented Reality (AR) menjadi tren di pendidikan dengan menggabungkan dunia digital dan dunia nyata, sehingga meningkatkan kualitas kegiatan belajar mengajar. siswa yang memanfaatkan aplikasi AR berhasil menyelesaikan masalah. Penelitian ini menggunakan model pengembangan ADDIE. Instrument yang digunakan untuk mengumpulkan data penelitian adalah angket yang diisi oleh ahli, praktisi, dengan menggunakan skala Likert. Augmented reality di validasi oleh ahli media, ahli otomotif, guru dan siswa. Hasil validasi menujukkan AR valid dilihat dari aspek Tampilan, Pengetahuan, Kemudahan, dan Kelengkapan

Alternative Computer Assisted Communicative Task-based Language Testing: New Communicational and Interactive Online Skills

[EN] Computer-assisted language learning knowledge tests should no longer be designed on traditional skills to measure individual competence through traditional skills such as reading, comprehension and writing, but instead, it should diagnose interactive and communication skills in foreign languages. In recent years in online education, it has been necessary to review the concept of interactive competence in digital environments in a complementary way to its traditional use. It is important to promote a new typology of alternative tasks and items in tests where examinees can prove a real interactive performance in communication and interaction through the digital scenario. This should be done through tools that facilitate oral negotiation, the management and understanding of the information extracted from online repositories, the search for suitable online digital material, and the use of new modes of audio-visual communication. Although some of these tasks have been used in a complementary way in the design of language tests previously: it is true that they have not been applied in a coherent way to be used as an assessment tool. A first approach was made by Miguel Alvarez, Garcia Laborda & Magal-Royo (2021) in the development of oral negotiation skills through the use of interactive tools. The current online assessment models analyzed by Garcia Laborda & Alvarez Fernandez (2021) indicate the need to seek new ways of assessing foreign languages through the design of tests that fit in the current digital and interactive world.Magal-Royo, T.; García Laborda, J.; Mora Cantallops, M.; Sánchez Alonso, S. (2021). Alternative Computer Assisted Communicative Task-based Language Testing: New Communicational and Interactive Online Skills. International Journal of Emerging Technologies in Learning (Online). 16(19):251-259. https://doi.org/10.3991/ijet.v16i19.26035S251259161