Bulletin of Electrical Engineering and Informatics
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Enhancing fruit recognition with robotic automation and salp swarm optimization for random forest classification
Full text linkIn response to the growing demand for automation and labor-saving solutions in agriculture, there has been a noticeable lack of advancements in mechanization and robotics specifically tailored for fruit cultivation. To address this gap, this work introduces a novel method for fruit recognition and automating the harvesting process using robotic arms. This work employs a highly efficient and accurate model utilizing a single shot multibox detector (SSD) for detecting the precise fruit position. Once the fruit's position is identified, the angles of the robot arm's joints are calculated using inverse kinematics (IK). Finally, the optimal path planning is ensured by the salp swarm optimization (SSO) assisted random forest (RF) classification. This approach enables the precise management of robotic arms without any interference with either the fruits themselves or other robotic arms. Through meticulous consideration of these factors, our method ensures seamless operation in agricultural environments. Experimental validation demonstrates the effectiveness of these techniques in detecting apple fruits outdoors and subsequently automating their harvesting using robotic arms. This successful implementation underscores the potential for widespread application of our approach in enhancing efficiency and productivity in fruit cultivation
Sliding mode control for speed loop combined with MTPA strategy of IPMSM applied in electric vehicles
Full text linkThe interior permanent magnet synchronous motor (IPMSM) 's outstanding features, such as quick torque mobility capability, broad speed adjustability, robust mechanical structure, and high efficiency, make it particularly suitable for electric vehicle propulsion systems. This paper proposes a speed loop utilising the sliding mode control (SMC) with exponential reaching law and proportional-derivative term-ks, facilitating quicker transient responses and enhancing system stability. Moreover, coupling with the maximum torque per ampere strategy (MTPA) on current to improve motor torque in flux weakening region and to extend the adjustable range of motor speed for electric vehicle propulsion systems is discussed. Furthermore, with the proposed control methods and strategies, the system achieves stability despite environmental noise and uncertainties caused by uncertain parameters. Finally, simulation results conducted on MATLAB/Simulink software verify the correctness of the proposed control methods
Developing digital capabilities through IT governance: a PLS-SEM analysis in Moroccan higher education institutions
Full text linkThis study examines the impact of information technology governance (ITG) on digital transformation (DT) in Moroccan higher education institutions, particularly emphasising the mediating role of absorptive capacity. Utilising a rigorous methodological framework, the research analyzes data collected from 110 staff members using structural equation modelling with the SmartPLS tool. The goal is to explore the complex dynamics between ITG practices and DT capability. The findings reveal a positive and statistically significant relationship between ITG mechanisms and absorptive capacity (AC) and between the latter and the success of DT. The study also identifies AC as a crucial mediator between ITG and digital capability (DC). It suggests universities should strengthen their AC and adopt open policies to increase their innovative potential. This contribution enriches the existing literature by empirically confirming the influence of certain IT governance variables on DC within Moroccan universities, offering valuable insights for academic researchers and practitioners involved in IT governance strategies and DT
The impact of BERT-infused deep learning models on sentiment analysis accuracy in financial news
Full text linkThis study delves into the enhancement of sentiment analysis accuracy within the financial news domain through the integration of bidirectional encoder representations from transformers (BERT) with traditional deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM) networks, gated recurrent units (GRU), and convolutional neural networks (CNN). By employing a comprehensive method encompassing data preprocessing, polarity analysis, and the application of advanced neural network architectures, we investigate the impact of incorporating BERT’s contextual embeddings on the models’ sentiment classification performance. The findings reveal significant improvements in model accuracy, precision, recall, and F1 scores when BERT is integrated, surpassing both traditional sentiment analysis models and contemporary natural language processing (NLP) transformers. This research contributes to the body of knowledge in financial sentiment analysis by demonstrating the potential of combining deep learning and NLP technologies to achieve a more nuanced understanding of financial news sentiment. The study’s insights advocate for a shift towards sophisticated, context-aware models, highlighting the pivotal role of transformer-based techniques in advancing the field
Performance evaluation of feature extraction to improve the classification of PTM in C-glycosylation using XGBoost
Full text linkProtein function is regulated by an important mechanism known as post-translational modification (PTM). Covalent and enzymatic protein modifications are added during protein biosynthesis, and such alterations significantly influence the regulation of gene activity and the functionality of proteins. Glycosylation, one type of PTM, involves adding sugar groups to a protein's structure. Numerous illnesses, such as diabetes, cancer, and the flu, have been linked to glycosylation. Therefore, it is critical to predict the presence of glycosylation, whether it occurs or not. Currently, predicting glycosylation sites is still done manually using biological methods, which require repeated experiments and a significant amount of time. To address these challenges, it is essential to rapidly develop computational data models using machine learning methods. In this study, the extreme gradient boosting (XGBoost) method is implemented, and C-glycosylation data is obtained from the publicly accessible UniProt website. The objective is to enhance the accuracy of C-glycosylation prediction using the XGBoost method. Feature extraction is performed using amino acid index (AAindex), composition, transition, and distribution (CTD), solvent AccessiBiLitiEs (SABLE), hydrophobicity, and pseudo amino acid composition (PseAAC) to improve accuracy. The minimum redundancy maximum relevance (MRMR) method is applied for feature selection. The findings of the study demonstrate that the PTM C-glycosylation prediction achieved 100%
Advancing palm oil fruit ripeness classification using transfer learning in deep neural networks
Full text linkThe palm oil industry is a significant component of Indonesia’s economy, driven by increasing global demand across various industries. Manual identification of palm oil fruit ripeness is often subjective and labor-intensive, creating a need for a faster and more accurate solution. This study proposes the use of deep learning models based on transfer learning to enhance the classification of palm oil fruit ripeness. Our research evaluates several models, finding that ResNet152V2 achieves the highest performance with superior accuracy and the lowest validation loss. DenseNet201, MobileNet, and InceptionV3 also deliver strong results, each demonstrating an accuracy above 0.99 and a validation loss below 0.04. Cross-validation confirms that ResNet152V2, DenseNet201, and MobileNet maintain high and consistent performance across different folds, showcasing their stability and reliability. This approach provides a promising alternative to manual methods, offering a more efficient and precise means for determining palm oil fruit ripeness, which could significantly benefit the industry by streamlining quality control processes
Conceptual design for traceability and transparency in halal self-declared with blockchain
Full text linkHalal self-declared is a halal certification procedure for small micro enterprises (SMEs). However, sufficient technology support is required to ensure the halal process’s transparency and traceability. By integrating blockchain-oriented software engineering (BOSE) technology with smart contracts and electronic product code information services (EPCIS), this study aims to deliver a conceptual design for traceability and transparency in halal self-declared. The effectiveness of the blockchain in storing data and disclosing private information to interested parties can be circumvented by utilizing both off-chain and on-chain technology. The effectiveness of blockchain data storage and the ability to disclose sensitive information to interested parties can be advantageous for both on-chain and off-chain applications
Exploring 5G network performance: comparison of inner and outer city areas in Phetchaburi Province
Full text linkThe advancement of 5G technology has transformed various aspects of life, including tourism, by enabling people worldwide to communicate and travel with ease. Traveling to different places and countries is now seamless, removing language barriers and facilitating easy access to information on culture, accommodation, and tourist attractions. Additionally, access to applications that facilitate quicker language translation further enhances the travel experience. Phetchaburi Province holds significant importance as a global tourist destination. United Nations Educational, Scientific, and Cultural Organization (UNESCO) has recognized Phetchaburi as a member of the UNESCO creative cities network (UCCN), comprising one of 49 cities worldwide acknowledged for their creative city initiatives. Phetchaburi Province stands as the 5th city in Thailand to receive this designation. This research investigated 5G performance in Phetchaburi Province, both the inner and outer city, focusing on download and upload speeds. The results indicate that there is widespread 5G coverage throughout Phetchaburi Province, including urban and rural areas, especially for the 5G network with a good performance provided by one of the mobile network operators (MNOs). In addition, the statistical analysis reveals differences in 5G performances between the inner city and the outer city of Phetchaburi Province, particularly for download speeds (p-value0.001)
Evaluate of vest massage therapy with rotating pressure based on pre-experimental methods
Full text linkMany postpartum mothers complain that their milk production is too low to supply the baby’s needs. There are two essential substances in the milk: the prolactin hormone and the oxytocin hormone. Consequently, there are two ways to stimulate these hormones: massage techniques such as breast care and oxytocin massage. This study aims to design vest therapy devices to expedite breast milk production. With the use of vest therapeutic devices, it can be observed that the amount of breast milk production increases. This research uses a pre-experimental method in postpartum mothers, which uses the vest massage therapy and does not use the vest massage therapy. Accidental sampling was used as the sampling method for this study, and the data were analyzed using the independent t-test. It is hoped that making Vest therapy devices can facilitate breastfeeding for postpartum mothers with the aim that they can increase the amount of breast milk and supply the milk for the babies in the early stage of their life. The test result discovered an increase in breast milk volume in breastfeeding mothers by an average of 7.3 ml in postpartum mothers who used vest therapy equipment compared to the previous amount of milk produced
Conceptualizing the ‘All You Can Eat’ game to promote healthy eating habits among young children
Full text linkChildhood obesity is a growing concern globally, with unhealthy eating habits being one of the leading causes. In response, researchers and game designers have investigated the use of serious games to encourage healthy eating among young children. Creating successful serious games to encourage children's good eating habits involves thoughtful consideration of elements such as age-appropriate content, game mechanics, and motivator strategies. The aim of this project is to create a serious game design that promotes and supports healthy eating habits in youngsters. This study evaluates children's existing understanding of nutrition by gathering their comments using a serious game as an example. Various gaming elements are recognized, leading to the creation of a board game named "All You Can Eat" (AYCE). The design evaluation process involves conducting questionnaire surveys and gathering feedback from both parents and children. The results will assist future research in creating and bringing to realisation the AYCE game. This research can be extended to a range of health topics beyond healthy eating habits, such as serious games for learning about cultures and ethics. Researchers, educators, and game designers collaborate to produce unique and interesting games aimed at promoting good eating habits and preventing youngsters’ obesity