Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enabl...
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In this study, the convergence of gait recognition and fingerprint identification was explored using heat map integration. Gait recognition, a biometric method that relies on distinct walking patterns, finds applicati...
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In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, faci...
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In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, facing challenges like task interference, limited adaptability, and difficulty in capturing nuanced linguistic expressions indicative of various conditions. In response to these challenges, our research presents three novel models employing multi-task learning (MTL) to understand mental health behaviors comprehensively. These models encompass soft-parameter sharing-based long short-term memory with attention mechanism (SPS-LSTM-AM), SPS-based bidirectional gated neural networks with self-head attention mechanism (SPS-BiGRU-SAM), and SPS-based bidirectional neural network with multi-head attention mechanism (SPS-BNN-MHAM). Our models address diverse tasks, including detecting disorders such as bipolar disorder, insomnia, obsessive-compulsive disorder, and panic in psychiatric texts, alongside classifying suicide or non-suicide-related texts on social media as auxiliary tasks. Emotion detection in suicide notes, covering emotions of abuse, blame, and sorrow, serves as the main task. We observe significant performance enhancement in the primary task by incorporating auxiliary tasks. Advanced encoder-building techniques, including auto-regressive-based permutation and enhanced permutation language modeling, are recommended for effectively capturing mental health contexts’ subtleties, semantic nuances, and syntactic structures. We present the shared feature extractor called shared auto-regressive for language modeling (S-ARLM) to capture high-level representations that are useful across tasks. Additionally, we recommend soft-parameter sharing (SPS) subtypes-fully sharing, partial sharing, and independent layer-to minimize tight coupling and enhance adaptability. Our models exhibit outstanding performance across various datasets, achieving accuracies of 96.9%, 97.
Increased radiation exposure to the patient is one of the most significant disadvantages of high-dose CT. The ionizing radiation used in CT scans entails the potential risk of damaging DNA and increasing the risk of c...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of r...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma
This research study performs content analysis of Twitter discussions associated with the earthquakes that occurred in Turkey and Syria in 2023. The authors investigated the main themes and topics of the discussions, e...
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This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojournin...
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This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison *** judgments cannot be given for real-life situations,as most of these include some level of fuzziness and *** t...
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This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison *** judgments cannot be given for real-life situations,as most of these include some level of fuzziness and *** these situations,judgments are represented by the set of fuzzy *** of the fuzzy optimization models derive crisp priorities for judgments repre-sented with Triangular Fuzzy Numbers(TFNs)*** do not work for other types of Triangular Shaped Fuzzy Numbers(TSFNs)and Trapezoidal Fuzzy Numbers(TrFNs).To overcome this problem,a sum of squared error(SSE)based optimization model is *** some other methods,the proposed model derives crisp weights from all of the above-mentioned fuzzy judgments.A fuzzy number is simulated using the Monte Carlo method.A threshold-based constraint is also applied to minimize the deviation from the initial *** Algorithm(GA)is used to solve the optimization *** have also conducted casestudiesto show the proposed approach’s advantages over the *** show that the proposed model outperforms other models to minimize SSE and deviation from initial ***,the proposed model can be applied in various real time scenarios as it can reduce the SSE value upto 29%compared to the existing studies.
Internet of Things (IoT) devices are often directly authenticated by the gateways within the network. In complex and large systems, IoT devices may be connected to the gateway through another device in the network. In...
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