Volume 9, Issue 1

Advances in Internet of Things (IoT) and wireless technologies are revolutionizing various sectors, including environment, education, healthcare, industry, etc. In the same dynamic, as the world population constantly evolves, solutions based on such technologies need to be proposed to improve the agricultural sector. Senegalese agriculture, primarily rain-fed and based on both cash crops and subsistence food crops, faces challenges from climate change, environmental impacts, and traditional methods. Lack of information on good practices, new efficient and low-cost farming methods, and access to seeds, along with the COVID-19 pandemic, further complicates the situation. This research work proposes a smart farming management system that uses IoT technology, named MbaïMi, whose main objective is to improve Senegalese agriculture through new digital technologies for sustainable agriculture, environmental benefits and food self-sufficiency. It provides a real- time decision support tool that offers farmers best seeds to choose at the beginning of work and helps optimize water resources. Additionally, the system includes features like sending SMS/advice, graphical visualizing of temperature and humidity changes for greenhouse cultivation control. The development of a web and mobile application, deployment of a prototype in southern Senegal, and validation case studies demonstrate the system’s potential to improve farmers’ yields and daily hard work to meet the challenges of sustainable agriculture, environmental benefits and food self-sufficiency.

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In this study, two healthy women in their 40s wore Oura Rings on both fingers and verified left and right differences in sleep index. Bed time, sleep time, rem sleep, sleep score, and heart rate had small left and right differences in both subjects, and a high correlation of 0.8 or more was observed(subjectA:P<0.001,subjectB:P<0.01). Subject B showed a significant difference in the mean values of sleep latency, awakening time and sleep efficiency, and one finger was overestimated(P<0.05). Although the detection accuracy of sleep stages differs depending on the stage, it was shown that the influence of left and right differences is particularly small for bed time, sleep time, rem sleep, sleep score and heart rate.

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A resume is a prevalent and generally employed method for individuals to showcase their proficiency and qualifications. It is typically composed using diverse customized, personalized methods in multiple inconsistent formats (such as pdf, txt, doc, etc.). Screening candidates based on the alignment of their resume with a set of job requirements is typically a labori- ous, challenging, time-intensive, and resource-intensive endeavor. This work is crucial for extracting pertinent information and valuable attributes that indicate acceptable applicants. This study aims to improve the candidate career-matching process for human resource (HR) departments by implementing automation and increasing efficiency. Machine learning (ML) and natural language processing (NLP) techniques are applied to infer and analyze comparative semantic resume attributes. Using semantic data comparisons, the ranking support vector ma- chine (RankSVM) algorithm is subsequently applied to rank these resumes based on attributes. RankSVM detects tiny differences among candidates and assigns unique scores, resulting in an improved ranking of candidates based on their suitability for job requirements and from the best to worst match for the vacancy. The experiment and performance comparison results show that the proposed comparative ranking, which relies on semantic descriptions, outperforms the standard ranking based on regular scores in distinguishing candidates and distributing resumes across the ranks with an accuracy of up to 92%. Eventually, we obtained a list of the top ten candidates out of 228 technical specialists’ resumes.

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This work enhances the LoRaWAN server framework, focusing on an innovative approach for robust security and dynamic data visualization in network management. Migrating from RVC4 to AES encryption, it fortifies the network’s defense against cyber threats, a crucial advancement in IoT security. Furthermore, the integration with Grafana’s mapping plugin capitalizes on geolocation data, a strategic element for network oversight and IoT data analysis. In-depth configuration and application of this plugin are explored, revealing substantial benefits for network administrators and end-users. Expanded discussions, backed by new experimental data, illustrate the real-world efficacy of these technological improvements. The research substantially enriches the understanding of LoRaWAN’s technological evolution, addressing vital aspects of IoT security and geolocation integration. The outcomes are expected to resonate significantly within academic circles and practical domains, particularly in reinforcing IoT and network security. This work marks a significant stride in the progression of LoRaWAN technologies, with implications that extend well into the broader landscape of network management and security in the digital age.

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The aim of this study is to develop an advanced framework that not only recognize the dominant facial emotion, but also contains modules for gesture recognition and text-to-speech recognition. Each module is meticulously designed and integrated into unified system. The implemented models have been revised, with the results presented through graphical representations, providing prevalent emotions in facial expressions, body language, and dominant speech/voice analysis. Current research, to identify the dominant facial emotion, involves two distinct approaches that autonomously determine the primary emotional label among seven fundamental emotions found in the input data: anger, disgust, happiness, fear, neutral expressions, sadness and surprise. The dataset utilized comprises over 292680 images sourced from the benchmark datasets FER-2013 and CK, enriched by images sourced from the Google search engine, along with 80 videos obtained during dedicated sessions, used for training and testing purposes. The Residual Masking Network (resmasknet) and CNN architectures are used as pre-trained models in this analysis. Resmasknet and CNN were chosen considering their superior performance compared to other algorithms found in the literature. The CNN architecture comprises 11 blocks, with each block containing a linear operator followed by ReLU or max-pooling layer. Starting with a convolutional layer that uses 32 filters and an 11x11x3 input, followed by a 3×3 max-pooling layer with a step of 2, the next layer includes a convolutional layer that uses 16 filters of size 9x9x16. The Residual Masking Network, contains four residual masking blocks operating on different feature sizes, each consisting a residual layer and masking block. The network initiates with a 3×3 convolution layer, followed by 2×2 max-pooling, effectively downsizing the image to 56×56. Successive transformations within four residual masking blocks generate different maps of sizes 56×56, 28×28, 14×14, and 7×7, culminating in an average pooling layer and a fully connected SoftMax output layer. The significance of this project lies in its focus on a comprehensive analysis of emotions and movements characteristic of Asian and European facial expressions. Showing promising accuracy rates, the proposed solutions achieve 75.2% accuracy for Asian and 86.6% for European individuals. This performance demonstrates the potential of this multidisciplinary framework in understanding and interpreting different facial expressions in different cultural settings.

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South African Drainage and Stormwater Systems in urban roads has been of great concern, more so with recent flash floods in Gauteng and KwaZulu Natal Province in South Africa. Pervious concrete can be used to mitigate these challenges for urban roads. Pervious concrete is a concrete that contains no fines or only a small amount is added for binding. It consists of single sized rounded aggregates between 9.5mm to 12.5mm and bonded by cement and water at points of contact to create a high porosity system that drains water. The research focused on developing different structural pervious concrete mixes, with varying (a) aggregate sizes (7mm, 9.5mm and 19mm) (b) including (river fine aggregates, rubber flakes, fly ash and steel reinforced fiber) and combinations thereof. Results indicated that strong pervious concrete mixes can be developed using a combination of 9.5mm aggregate, river fine aggregates and fly ash, with a recorded maximum yield of 25MPa, and an accompanying Voids Content of 16%. Furthermore, against this performance, it was found that final compressive yield strength of 25MPa falls shy of the 35MPa COTO standard specifications for road and bridge works for south African road authorities (2020 edition). The derived pervious concrete mix was found to satisfy Class 4b Minor Collector Arterial in urban zones, with capacity to withstand up to 10 000 Average Daily Traffic.

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The cybercriminal utilized the skills and freely available tools to breach the networks of internet-connected devices by exploiting confidentiality, integrity, and availability. Network anomaly detection is crucial for ensuring the security of information resources. Detecting abnormal network behavior poses challenges because of the extensive data, imbalanced attack class nature, and the abundance of features in the dataset. Conventional machine learning approaches need more efficiency in addressing these issues. Deep learning has demonstrated greater effectiveness in identifying network anomalies. Specifically, a recurrent neural network model is created to recognize the serial data patterns for prediction. We optimized the hybrid model, the convolutional neural network combined with Bidirectional Long-Short Term Memory (BLSTM), to examine optimizers (Adam, Nadam, Adamax, RMSprop, SGD, Adagrad, Ftrl), number of epochs, size of the batch, learning rate, and the Neural Network (NN) architecture. Examining these hyperparameters yielded the highest accuracy in anomaly detection, reaching 98.27% for the binary class NSL-KDD and 99.87% for the binary class UNSW-NB15. Furthermore, recognizing the inherent class imbalance in network-based anomaly detection datasets, we explore the sampling techniques to address this issue and improve the model’s overall performance. The data imbalance problem for the multiclass network anomaly detection dataset is addressed by using the sampling technique during the data preprocessing, where the random over-sampling methods combined with the CNN-based BLSTM model outperformed by producing the highest performance metrics, i.e., detection accuracy for multiclass NSL-KDD and multiclass UNSW-NB15 of 99.83% and 99.99% respectively. Evaluation of performance, considering accuracy and F1-score, indicated that the proposed CNN BLSTM hybrid network-based anomaly detection outperformed other existing methods for network traffic anomaly detection. Hence, this research contributes valuable insights into selecting hyperparameters of deep learning techniques for anomaly detection in imbalanced network datasets, providing practical guidance on choosing appropriate hyperparameters and sampling strategies to enhance model robustness in real-world scenarios.

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Machine Translation (MT) has come a long way toward reducing linguistic gaps. However, its progress in efficiently handling low-resource languages—such as the Wa language in the Myanmar-Wa corpus—has not received enough attention. This study begins with a thorough investigation of the historical development of MT systems, painstakingly following their development against the complex background of the Myanmar-Wa language region. Using an interdisciplinary methodology that integrates linguistics, technology, and culture, this investigation reveals the transformative journey of Machine Translation (MT) in its pursuit of overcoming linguistic barriers. It offers a thorough study that clarifies the opportunities and limitations present in MT’s progress. More broadly, by clarifying the complex relationship between technology and linguistic diversity, our work not only advances our understanding of MT’s evolutionary history but also supports the conservation of endangered languages, like Wa language. The research’s conclusions have implications that go beyond machine translation to the larger conversation about language preservation and how technology development coexists harmoniously. Notably, this paper is an extension of work originally presented in “2023 IEEE Conference on Computer Applications (ICCA)”, acknowledging its foundation and presenting substantial advancements.

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The air compressor of a railroad vehicle is an important equipment that produces compressed air used in braking systems. New visual interaction techniques were proposed and evaluated to develop effective augmented reality content for maintenance support and training of this device. To this end, modeling techniques capable of fast animation, storyboard production to support light maintenance, and visualization algorithms that implement fluid flow have been developed. In the case of air compressors using compressed air, 2D-3D line matching distance calculation algorithm and modified flow generation algorithm were proposed to implement fluid flow for visualization. If two algorithms are used at the same time, the educational effect can be enhanced by visualizing the air flow in the 3D object simultaneously on a 2D air pipe diagram. In addition, the use of flow generation algorithm alone can visualize fluid flow or quantity control, such as air discharge or lubricant supplementation. As a result of usability evaluation of 100 users of the developed air compressor augmented reality content, the system usability scale score was 76.65, which was good. Similarly, the user experience score using six questionnaires was very good with an average of 4.12. Therefore, it was found that this training content could be used very effectively for air compressor maintenance support and training at the site.

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Diabetes presents a significant public health issue for Native Americans, exacerbated by cultural nuances often ignored by conventional healthcare. To address this, we introduce a mobile app designed with the cultural context of Native American populations in mind. The app’s development followed participatory design principles, with direct input from Native American stakeholders through focus groups and interviews. The app features culturally tailored nutrition plans incorporating traditional foods, community-based support systems, and engagement with tribal health resources. An interface highlighting Native American heritage, along with gamification using cultural storytelling, aims to enhance user engagement and educational content is provided within a culturally relevant framework. This innovative integration of technology and cultural heritage in health management is anticipated to improve engagement, self-efficacy, and health outcomes for Native Americans with diabetes, serving as a blueprint for culturally sensitive health interventions.

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