Lightweight cryptography is now emerging as a new method for providing security to resource-constrained devices. Securing those devices from external hackers during data transmission is now becoming an important issue...
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Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM mod...
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Pesticides have become more necessary in modern agricultural ***,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the *** to a shortage of basic pesticide exposure awareness...
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Pesticides have become more necessary in modern agricultural ***,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the *** to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to *** residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly *** residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in *** is an obligation to provide a warning about this amount of pesticide use in *** technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential *** analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and ***,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.
Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information w...
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In autonomous driving systems, the monocular 3D object detection algorithm is a crucial component. The safety of autonomous vehicles heavily depends on a well-designed detection system. Therefore, developing a robust ...
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Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), wh...
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The severity of air pollution has prompted increased attention to air pollution monitoring. This article uses unmanned aerial vehicles (UAVs) to discover chimneys with excessive exhaust emissions (called 3E-chimneys) ...
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The computation of natural language enables a suitable transmission through the universe by retrieving the correct sense of each word. A word may be monosemous or polysemous. The use of polysemous words in an appropri...
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Breast cancer poses a threat to women’s health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challe...
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Breast cancer poses a threat to women’s health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challenges in early breast cancer detection due to poor image quality, traditional segmentation, and feature extraction. Therefore, this work addresses these issues and proposes an attention-based backpropagation convolutional neural network (ABB-CNN) to detect breast cancer from mammogram images more accurately. The proposed work includes image enhancement, reinforcement learning-based semantic segmentation (RLSS), and multiview feature extraction and classification. The image enhancement is performed by removing noise and artefacts through a hybrid filter (HF), image scaling through a pixel-based bilinear interpolation (PBI), and contrast enhancement through an election-based optimization (EO) algorithm. In addition, the RLSS introduces intelligent segmentation by utilizing a deep Q network (DQN) to segment the region of interest (ROI) strategically. Moreover, the proposed ABB-CNN facilitates multiview feature extraction from the segmented region to classify the mammograms into normal, malignant, and benign classes. The proposed framework is evaluated on the collected and the digital database for screening mammography (DDSM) datasets. The proposed framework provides better outcomes in terms of accuracy, sensitivity, specificity, precision, f-measure, false-negative rate (FNR) and area under the curve (AUC). This work achieved (99.20%, 99.35%), (99.56%, 99.66%), (98.96%, 98.99%), (99.05%, 99.12%), (0.44%, 0.34%), (99.31%, 99.39%) and (99.27%, 99.32%) of accuracy, sensitivity, specificity, precision, FNR, f-measure and AUC on (collected, DDSM datasets), respectively. This research addresses the prevalent challenges in breast cancer identification and offers a robust and highly accurate solution by integrating advanced deep-learning techniques. The evaluated re
Today for an organisation, data security is the most crucial topic. An organisation needs to protect its information against cyberattacks. Cryptography, DLT, and blockchain technology provide higher security for data ...
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