An accurate image retrieval technique is required due to the rapidly increasing number of images. It is important to implement image annotation techniques that are fast, simple, and, most importantly, automatically annotate. Image annotation has recently received much attention due to the massive rise in image data volume. Focusing on the agriculture field, this study implements automatic image annotation, namely, a repetitive annotation task technique, to classify the ripeness of oil palm fruit and recognize a variety of fruits. This approach assists farmers to enhance the classification of fruit methods and increase their production. This study proposes simple and effective models using a deep learning approach with You Only Look Once (YOLO) versions. The models were developed through transfer learning where the dataset was trained with 100 images of oil fruit palm and 400 images of a variety of fruit in RGB images. Model performance and accuracy of automatically annotating the images with 3500 fruits were examined. The results show that the annotation technique successfully annotated a large number of images accurately. The mAP result achieved for oil palm fruit was 98.7% and the variety of fruit was 99.5%.

The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.

This study investigates the control performance of a structural building system during a seismic scenario using an adaptive nonsingular terminal sliding mode control. To realize the structural integrity of a building, it is necessary to equip the building with a structural control device. This research is focused on a hybrid control device that has excellent characteristics of passive and active control devices and implemented in a three degree-of-freedom system. The system, actuator, and controllers are designed by using the mathematical model developed in MATLAB/Simulink. The input excitation to the structure is taken from the El Centro earthquake that occurred in the 1940s with a magnitude of 6.9 M_w and the Southern Sumatra earthquake that occurred in 2007 with a magnitude of 8.4 M_w. Adaptive nonsingular terminal sliding mode control is the new proposed control strategy to be applied in structural control field is investigated in terms of controller performance in suppressing the vibrations, and then, compared with sliding mode control and fuzzy logic controller strategies. Sliding mode control is chosen to be compared with adaptive nonsingular terminal sliding mode control because of its advantages of robust performance, whereas fuzzy logic controller is chosen because of its intelligent control base. The effectiveness of the proposed controllers is evaluated based on the displacement response, performance indices, and the probability of building damage. The results have shown that the new proposed controller, an adaptive nonsingular terminal sliding mode control, reduced vibrations better and has superior performance compared with fuzzy logic controller and sliding mode control.

Teachers often encounter a massive volume of administrative tasks that may affect their productivity and overall satisfaction with their work. Due to excessive workloads, teachers may encounter high levels of stress and a decline in the quality of their teaching. An advanced solution technique that incorporates Robotic Process Automation (RPA) offers a possible resolution to optimize teachers’ workload. Therefore, this study investigates the influence of RPA in optimizing administrative workload and job satisfaction. Moreover, this study has the goal of identifying key considerations and challenges in developing RPA projects within educational settings. The research included a sample of 30 teachers selected from several schools, ensuring a diverse sample in terms of experience, subject specialization, and school type which are public and private. The surveys are conducted to allow for a comprehensive understanding of the varying impacts and perceptions of RPA across different educational contexts. The results demonstrate a significant integration of RPA in terms of minimizing administrative workload, challenges of adaptation, enhanced job satisfaction, and increased efficacy and productivity. Teachers also expressed a greater positive impact of the implementation of RPA with 76.7% influence on interaction with students and 46.7% able to improve job satisfaction. The integration of RPA into administrative workload not only streamlines administrative tasks but also allows teachers to dedicate more time to instructional and student-centered activities. Future research could focus on long-term impacts and exploration of RPA across different educational contexts.

For the reason of the widespread of technologies in daily life, the rising rate of internet and social media usage among children has emerged as an important area of research. The purpose of this study is to enhance our knowledge of the impact of unsupervised internet and social media use on children's development and well-being. Data was collected via an online questionnaire divided into two sections. Section A dealt with demography, while Section B discussed the impact of the internet and social media on children. This study surveyed 36 parents to investigate their children's access to various internet information, its consequences on mental and physical health such as eye strain and sleep difficulties, and implications for academic achievement. According to the findings, unsupervised internet interaction can result in behavioural abnormalities in roughly 70% of cases, academic difficulties in around 30.6%, and health issues in about 58.3%. However, parents are using a variety of strategies to mitigate these risks. The survey reveals that 70% of respondents set limits on their children's gadget usage, while 22.2% utilize parental control software. These findings highlight the importance of taking proactive actions to overcome the issues posed by unsupervised digital interaction and create a safer and more beneficial online environment for children.

Seismic events provide significant hazards to the safety and structural integrity of building structures, requiring efficient mitigation techniques. Conventional approaches to mitigating earthquake risks may lack the capacity for prompt adaptation and response. The integration of artificial intelligence (AI) controllers offers an attractive way to improve building safety in earthquake-prone regions. The fundamental unpredictability and complexity of seismic forces can undermine conventional safety measures, resulting in deficiencies in effectiveness and responses. A solution that can dynamically respond and proactively mitigate hazards must be developed to solve these concerns. This review attempts to analyze the possible impact of AI controllers in substantially mitigating seismic risks for structures. This study investigates the efficacy of intelligence structural security systems in enhancing resilience and reducing damage during seismic events through the analysis of AI-driven techniques, methodology, applications, and performance metrics. A systematic analysis of the literature is performed to identify and evaluate prior research on AI controllers employed to reduce seismic risk in structures. The research highlights the influence of integrated AI controllers on control systems, examining several AI controllers, including machine learning algorithms, neural networks, and evolutionary algorithms concerning structural safety. A case study is carried out on a conventional controller, specifically the sliding mode controller (SMC), fuzzy logic controller (FLC), and radial basis function neural network nonsingular terminal sliding mode controller (RBFNN-NTSMC). The findings reveal that the RBFNN-NTSMC effectively reduces building vibrations by up to 63% compared to an uncontrolled structure, significantly outperforming the FLC and SMC, which achieved reductions of 13% and 12%, respectively. This case study illustrates how AI-driven techniques enhance structural resilience and reduce seismic vulnerability. The integration of AI controllers has the potential to enhance the safety and durability of structures by employing advanced computational methods to limit hazards, facilitate real-time response, and optimize structural performance during earthquakes.