This paper contributes by first describing industrial types of intercropping harvests and second deriving necessary harvesting machine/robot functionalities from the types. These findings are important to design the needed machinery in order to realize industrial intercropping use cases.
Providing students with effective academic advising using low-energy consumption by fine-tuning pretrained model.
The COVID-19 pandemic has highlighted the critical need for robust and efficient vaccination record-keeping systems. Traditional paper-based methods are fraught with issues such as inefficiency, susceptibility to fraud, and interoperability challenges across different regions. This paper explores the potential of integrating blockchain technology, Non-Fungible Tokens (NFTs), and smart contracts to develop a secure, transparent, and universally recognized system for managing pediatric vaccination records. By leveraging the decentralized and immutable nature of blockchain, each vaccination record can be uniquely represented as a tamper-proof digital certificate. Smart contracts are employed to automate various processes within the vaccination system, ensuring data accuracy and integrity. This study presents a theoretical framework and a proof-of-concept implementation, demonstrating the adaptability of the proposed system across multiple EVM-supported blockchain platforms, including Binance Smart Chain, Polygon, Fantom, and Celo. The system aims to enhance the security, integrity, and accessibility of vaccine records, providing a scalable solution for pediatric healthcare.
Our system integrates AI with EEG applications to diagnose neurological disorders. It is able to classify the specified mental health disorder and wirelessly transmit the data obtained from the EEG device to a remote server.
This article studies the effectiveness of deep learning (DL) algorithms in detecting brain tumors, focusing on disorders such as “Glioma-Tumor,” “Meningioma-Tumor,” “Pituitary-Tumor,” and “No-Tumor.” Magnetic Resonance Imaging (MRI) is the primary tool for identifying brain tumors, and the paper proposes a convolutional neural network (CNN) architecture for efficient tumor detection. The study explores various CNN models, including DenseNet121, ResNet50V2, DenseNet201, EfficientNetB2, VGG16, and MobileNet, which enhance classification accuracy. The models demonstrate high precision, recall, F1-score, sensitivity, and specificity in predicting brain tumor conditions.
Hate Speech and Abusive Language In Indonesia Twitter
Blockchain provides a decentralized ledger for
documenting transactions and monitoring items across the supply
chain. Using a case study methodology, this paper investigates
blockchain adoption in Fijian sugarcane supply chains,
concentrating on the influence on traceability, quality control, and
stakeholder participation. The findings indicate that blockchain
integration improves product information authenticity, lowers
fraud, and boosts market access for Fijian sugarcane goods.
Despite achievements, difficulties such as technological complexity
and regulatory compliance remain. The study suggests increasing
blockchain integration, building industry standards, and
promoting policy frameworks. Future studies should look at
larger agricultural supply chain concerns and encourage
sustainable development in Fiji.
Heart attacks have been a leading cause of mortality around the globe,
contributing towards premature deaths. The burden of this disease has been significantly impacted due to the late deduction of the disease. Machine learning has been used to curb the late deduction by implementing models in technologies such as websites, software and mobile applications. However, the major challenge lies in developing technology which is widely used for practical applications. Most machine learning applications remain in the prototype stage and are not generally accepted in real-world practice. This paper defines the necessary means and methodologies to design and develop a machine learning-based application which is acceptable in the real world using the design science research methodology
This research work assists in analysing accurate data and modelling a suitable model for integrating floating solar PV with hydropower plants. As a result, greenhouse gas emissions are reduced, and the best or most effective/proper system configuration is achieved. It can also benefit stakeholders and investors in implementing a hybrid system design and development.
According to the National Renewable Energy Laboratory (NREL), the critical differences between ground-mounted photovoltaic solar plants and floating photovoltaic solar plants
ABSTRACT: Keeping track of early indications regarding students’ progress helps academics optimize their learning tactics and focus on varying educational practices to make the learning experience successful. Machine learning applications can help academics to predict the expected weaknesses in learning processes and as a result, they can proactively engage such students in better learning experiences. This paper examines the effectiveness of the integrated approach of machine learning (ML) techniques in predicting students’ academic progress. Predicting student accomplishment is crucial in matters of higher education, as well as machine learning, deep learning, and its connections to educational data. The proposed idea not only predicts student accomplishment but also makes it simpler for educators and administrators to monitor students so that they can provide assistance and incorporate the training for the best results. This study presents the view of students’ performance prediction models and explores several clustering and classification strategies that significantly enhance the accuracy of classification, particularly when a training dataset is accessible. Through the use of machine learning clustering and classifiers, such as Fuzzy C-Means, Stochastic Gradient Descent, Support Vector Machine, XGBoost, Gradient Boosting and K-Nearest Neighbors algorithms, we categorize instances as either indicative of a good or bad condition. As a result, our classification models demonstrate high accuracy in predicting students’ performance disorder outcomes.