Existing learning models partition the generated representations using hyperplanes which form well defined groups of similar embeddings that is uniquely mapped to a particular class. However, in practical applications...
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software defect prediction (SDP) is considered a dynamic research problem and is beneficial during the testing stage of the software development life cycle. Several artificial intelligence-based methods were avai...
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software defect prediction (SDP) is considered a dynamic research problem and is beneficial during the testing stage of the software development life cycle. Several artificial intelligence-based methods were available to predict these software defects. However, the detection accuracy is still low due to imbalanced datasets, poor feature learning, and tuning of the model's parameters. This paper proposes a novel attention-included Deep Learning (DL) model for SDP with effective feature learning and dimensionality reduction mechanisms. The system mainly comprises ‘6’ phases: dataset balancing, source code parsing, word embedding, feature extraction, dimensionality reduction, and classification. First, dataset balancing was performed using the density peak based k-means clustering (DPKMC) algorithm, which prevents the model from having biased outcomes. Then, the system parses the source code into abstract syntax trees (ASTs) that capture the structure and relationship between different elements of the code to enable type checking and the representative nodes on ASTs are selected to form token vectors. Then, we use bidirectional encoder representations from transformers (BERT), which converts the token vectors into numerical vectors and extracts semantic features from the data. We then input the embedded vectors to multi-head attention incorporated bidirectional gated recurrent unit (MHBGRU) for contextual feature learning. After that, the dimensionality reduction is performed using kernel principal component analysis (KPCA), which transforms the higher dimensional data into lower dimensions and removes irrelevant features. Finally, the system used a deep, fully connected network-based SoftMax layer for defect prediction, in which the cross-entropy loss is utilized to minimize the prediction loss. The experiments on the National Aeronautics and Space Administration (NASA) and AEEEM show that the system achieves better outcomes than the existing state-of-the-art models f
Frauds don’t follow any recurring *** require the use of unsupervised learning since their behaviour is continually ***-sters have access to the most recent technology,which gives them the ability to defraud people t...
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Frauds don’t follow any recurring *** require the use of unsupervised learning since their behaviour is continually ***-sters have access to the most recent technology,which gives them the ability to defraud people through online *** make assumptions about consumers’routine behaviour,and fraud develops *** learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other *** a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization pat-terns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony(ABC).Using real-time data and other datasets that are readily available,the ABC-Recurrent Neural Network(RNN)categorizes fraud behaviour and compares it to the current *** compared to the current approach,the findings demonstrate that the accuracy is high and the training error is minimal in ABC_*** this paper,we measure the Accuracy,F1 score,Mean Square Error(MSE)and Mean Absolute Error(MAE).Our system achieves 97%accuracy,92%precision rate and F1 score 97%.Also we compare the simulation results with existing methods.
Purpose: This paper presents a theoretical analysis of the DynaTrans algorithm, a novel approach for dynamic optimization of urban transportation networks. Design/methodology/approach: We introduce an Adaptive Closene...
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Background: In the wake of escalating cyber threats and the indispensability of ro-bust network security mechanisms, it becomes crucial to understand the evolving landscape of cryptographic research. Recognizing the s...
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Creating programming questions that are both meaningful and educationally relevant is a critical task in computerscience education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generat...
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The Internet of Things (IoT) has gained widespread use across various domains, necessitating robust trust management for safeguarding data integrity and device reliability. Existing trust management algorithms often o...
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Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can l...
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Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL me...
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Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.
This paper proposes a classification model for single label implicit discourse relation recognition trained on soft-label distributions. It follows the PDTB 3.0 framework and it was trained and tested on the DiscoGeM ...
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