Gongcheng Kexue Yu Jishu/Advanced Engineering Science

Abstract : Maintaining nutrition, increasing yield, and minimizing an overabundance all depend on prompt identification and accurate classification of pests and diseases in tropical fruit crops. However, current deep learning approaches frequently have significant computing expenses and little flexibility to actual farming operations. In order to accomplish excellent classification precision with considerably fewer processing necessities, this study proposes ESA-ResNet34-Lite+, a novel lightweight, attention-enhanced deep learning framework that can be used in mobile and Internet of Things-based farming systems. Optimizing extracting features and concentrating on disease-specific behaviours while lowering the complexity of the model is achieved by integrating separable convolutions by depth and Efficient Spatial Attention (ESA) modules within a modified ResNet34 foundation. Furthermore, greater levels utilize Convolutional Block Attention Modules (CBAM) to improve multi-scale visualization of features and accuracy in classification. To train and validate the approach, a carefully selected dataset of roughly 3,500 tagged photos of tropical fruit crops including important guava diseases like Phytophthora, Scab, and Styler was utilized. Investigations show that ESA-ResNet34-Lite+ outperforms standard models like VGG16, MobileNet, and ResNet34 by 2–4% in classification accuracy, with an overall accuracy of 95.7%, precision, recall, and F1-scores of 95.6%, 95.8%, and 95.7%, respectively. The model also achieves a 70% reduction in FLOPs and an 85% reduction in specifications when compared to traditional architectures, highlighting its effectiveness and appropriateness for real-time field implementation. These results demonstrate that ESA-ResNet34-Lite+ offers an adaptable and economical intelligent agricultural approach by striking a good balance between accuracy and computational cost. Through the demonstration of exceptional detection performance on a difficult tropical crop dataset, this study creates a useful and trustworthy foundation for precision gardening, facilitating prompt disease control and encouraging ethical farming practices..
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Abstract : Effectively processing range queries for big, distributed data sets remains a perennial problem throughout today’s data systems, when queries are multifeatured in particular. Traditional indexes, such as B-Trees, KD-Trees, and R-Trees, often don’t work well in distributed or high-dimensional environments due to scalability and integrative limitations. This research paper proposes AVL Tree based Multi-Feature Query Engine framework for optimal multi-feature range queries. Maintaining logarithmic time per query when coupled with collection size, AVL trees represent a highly efficient indexing model, most prominently when resultant sets are modest in size. It’s deployed in Python and describes building local AVL trees on partitioned data and mapping it to a distributed environment utilizing MapReduce’s Hadoop implementation. It suggests a highly efficient filtering of big collections by performing range criteria in a staged mode across features, which severely reduces execution time when compared to linear scans. It describes the usability of balancing trees within distributed querying systems and spans scalability between in-memory indexes and big data systems..
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Abstract : In this paper, we propose a novel logistic regression-based framework for classifying nodes in Internet of Things (IoT) networks as either trusted or blackhole nodes. The model leverages multiple behavioral features of nodes, including packet delivery ratio, packet loss, energy efficiency, responsiveness, cooperation, and reliability. These features are linearly combined using model coefficients and processed through a sigmoid function to yield a probability score between 0 and 1. Based on a predefined threshold, nodes are classified as trusted if the probability exceeds the threshold, and as blackhole nodes otherwise. A detailed mathematical example is provided to illustrate the implementation of the model. To evaluate its effectiveness, the proposed framework is tested under simulated IoT network conditions, and its performance is analyzed using key metrics such as packet delivery ratio, packet loss, end-to-end delay, detection accuracy, and routing overhead. Results demonstrate that the model efficiently identifies malicious nodes and enhances overall network reliability and security, offering a lightweight and effective solution for real-time threat detection in resource-constrained IoT environments..
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Abstract : Social media platforms are crucial spaces for discussing mental health issues. They can provide support systems but also act as risk factors for individuals experiencing distress. This paper proposes a system utilizing Bidirectional Encoder Representations from Transformers (BERT) to classify Reddit posts into mental health categories such as bipolar disorder, depression, anxiety, suicide, and others. The classifier is trained on posts collected from relevant subreddits, with preprocessing and feature extraction handled using NLP techniques. The results demonstrate the feasibility of using BERT-based models for classifying mental health indicators from text data. Furthermore, the paper proposes an application where social media platforms subtly adjust users’ feeds to promote positive content when signs of mental health distress are detected..
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Abstract : This paper focuses on 150KW induction furnace employed with a reconfigured Series Resonant Converter (SRC) under faulty conditions. Resonant converter topologies have been popular for a few years in high-power transmission viz. medical, biotechnology, nuclear fusion. This network design is frequently utilized in solid-state transducers, where fault tolerance is an important feature that may be achieved through redundancy. Fault tolerance is an important element of power applications since it makes the system more reliable and available. A critical feature of any fault tolerance topology is the early detection of faults and their rectification or prevention. There are a variety of fault-tolerant topologies available in industry; however most of them rely on redundancy, which raises the cost and maintenance work. In an application with little or minimal redundancy, there is still room for improvement in terms of failure tolerance. The goal of the suggested topology is to increase efficiency while decreasing redundancy. It is frequently designed to accomplish converter fault tolerance by resetting the network during each failure, which may weaken the converter's performance but stops it from working. For example, induction furnaces are frequently used in a variety of industrial processes, including waste energy generation, metal melting, welding, and hardening. because fault-resistant topologies and high power under all circumstances are necessary for these jobs. Reconfiguration of the circuit under faulty conditions is examined in this project, so that a full bridge under faulty conditions is reconfigured as a half bridge for specialized applications such as an induction furnace, etc..
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