MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model.

Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning technique, termed Improved Supervised Contrastive Deep Regression (SCDR), which is adept at effectively capturing the nuanced relationships between samples and labels in the feature space, thereby mitigating this limitation. Simultaneously, we propose the U-like Hierarchical Residual Fusion Network (BioUMixer), a bespoke biomass prediction network tailored for image data. BioUMixer enhances feature extraction from biomass image data, facilitating information exchange and fusion while considering both global and local features within the images. The efficacy of the proposed method is validated on the Pepper_Biomass dataset, which encompasses over 600 original images paired with corresponding biomass labels. The results demonstrate a noteworthy enhancement in deep regression tasks, as evidenced by performance metrics on the Pepper_Biomass dataset, including RMSE = 252.18, MAE = 201.98, and MAPE = 0.107. Additionally, assessment on the publicly accessible GrassClover dataset yields metrics of RMSE = 47.92, MAE = 31.74, and MAPE = 0.192. This study not only introduces a novel approach but also provides compelling empirical evidence supporting the digitization and precision improvement of agricultural technology. The research outcomes align closely with the identified problem and research statement, underscoring the significance of the proposed methodologies in advancing the field of biomass prediction through state-of-the-art deep learning techniques.

Revealing the Effect of the [CoO]6 Microstructure in Pseudocapacitance by Controlled Delithium of LiCoO2.

Revealing the in-depth structure-property relationship and designing specific capacity electrodes are particularly important for supercapacitors. Despite many efforts made to tune the composition and electronic structure of cobalt oxide for pseudocapacitance, insight into the [CoO]6 octahedron from the microstructure is still insufficient. Herein, we present a tunable [CoO]6 octahedron microstructure in LiCoO2 by a chemical delithiation process. The c-strained strain of the [CoO]6 octahedron is induced to form higher valence Co ions, and the (003) crystalline layer spacing increases to allow more rapid participation of OH- in the redox reaction. Interestingly, the specific capacity of L0.75CO2 is nearly four times higher than that of LiCoO2 at 10 mA g-1. The enhanced activity originated from the asymmetric strain [CoO]6 octahedra, resulting in enhanced electronic conductivity and Co-O hybridization for accelerated redox kinetics. This finding provides new insights into the modification strategy for pseudocapacitive transition metal oxides.

A Ring Learning with Errors-Based Ciphertext-Policy Attribute-Based Proxy Re-Encryption Scheme for Secure Big Data Sharing in Cloud Environment.

Owing to the huge volume of big data, users generally use the cloud to store big data. However, because the data are out of the control of users, sensitive data need to be protected. The ciphertext-policy attribute-based encryption scheme can not only effectively control the access of big data, but also decrypt the ciphertext as long as the user's attributes satisfy the access structure of ciphertext, so as to realize one to many big data sharing. When the user's attributes do not satisfy the access structure of ciphertext, the attribute-based proxy re-encryption scheme can be used for big data sharing. The ciphertext-policy attribute-based proxy re-encryption (CP-ABPRE) scheme combines the characteristics of the ciphertext-policy attribute-based encryption scheme and proxy re-encryption scheme. In a CP-ABPRE scheme, on the one hand, the data owner can use the ciphertext-policy attribute-based encryption scheme to encrypt the big data for cloud storage, to realize the access control of the big data. On the other hand, the proxy (cloud service provider) can convert ciphertext under one access structure into ciphertext under another access structure, thus realizing big data sharing between users of different attribute sets. In this article, we modify the existing attribute-based encryption scheme based on Ring Learning With Errors (RLWE), add re-encryption key generation algorithm, re-encryption ciphertext generation algorithm, and re-encryption ciphertext decryption algorithm, and construct CP-ABPRE scheme. In the construction of the re-encryption key, we introduce a random vector and hide the vector in the key by threshold technology. Finally, a CP-ABPRE scheme supporting threshold access structure is constructed based on RLWE. Compared with the existing attribute-based proxy re-encryption schemes, our scheme has smaller public parameters, can encrypt multiple plaintext bits at a time, and can resist selective access structure and chosen plaintext attack, so it is more suitable for big data sharing in cloud environment.

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections.

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.