The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study prop...
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The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.
The article explores the possibility of improving the reliability of a network with multipath routing. A feature of the proposed study is the analysis of the influence of the placement of switching nodes that switch p...
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Nowadays, IoT is being used in several applications, such as smart cities, health care and innovating agriculture and other applications. Moreover, the evolution of IoT technologies such as LoRaWAN, SIGFOX, ZigBee, an...
Nowadays, IoT is being used in several applications, such as smart cities, health care and innovating agriculture and other applications. Moreover, the evolution of IoT technologies such as LoRaWAN, SIGFOX, ZigBee, and others drives the industry to convert their traditional systems to IoT systems due to the need to achieve better performance. One of the most challenging applications in an IoT system is smart agriculture due to the need for high energy and water consumption to reach the desired crop yield. Several related works in different countries tried to solve this challenge for a better water consumption in irrigation process to get the maximum crop yield production. In this paper, we used artificial intelligence and deep learning to predict the crop yield and the needed parameters of the soil to achieve the best performance with minimum power consumption. Also, we used convolutional neural networks to detect the crop deficit needed for real-time monitoring. The performed simulation has shown that the proposed model outperforms other related works and will positively increase both saving resources and the performance.
This paper presents a software-defined 1TX/2RX frequency-division duplex (FDD) transceiver front-end that allows simultaneous transmit-and-receive operation in close proximity wireless channels using frequency-selecti...
This paper presents a software-defined 1TX/2RX frequency-division duplex (FDD) transceiver front-end that allows simultaneous transmit-and-receive operation in close proximity wireless channels using frequency-selective dual-band self-interference cancellation (SIC). The proposed front-end includes a tunable RF tap and a multi-tap baseband FIR filter to suppress the Tx self-interference in the Tx band and in two symmetric adjacent channels where reception takes place, respectively. The FIR filter’s weights are computed using a dual-band least squares algorithm, and closed-form expressions are derived in detail. Measurement results from an FDD front-end prototype show a total of 90 dB cancellation in both Rx bands simultaneously, with 40 dB of passive isolation due to the leakage channel’s attenuation and 50 dB by the active FIR canceller.
IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural c...
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Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with na...
Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual T5 (MT5) model AASMT5 to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MT5 architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MT5.
Semantic segmentation of Light Detection and Ranging (LiDAR) is a task that requires high efficiency and accuracy. To our knowledge, this work is the first to apply model soups to the LiDAR semantic segmentation task,...
Semantic segmentation of Light Detection and Ranging (LiDAR) is a task that requires high efficiency and accuracy. To our knowledge, this work is the first to apply model soups to the LiDAR semantic segmentation task, showcasing their potential impact on the domain. Our contributions in this work are twofold: First, we successfully extend the application of model soups to LiDAR semantic segmentation. Second, we introduce an optimized and efficient version of the existing greedy soup, further enhancing the overall performance of the approach. In our method, We augment the state-of-the-art open-source code for 2DPASS semantic segmentation with our technique, retaining the original model structure and ensuring no increase in prediction time. The efficiency of our approach is demonstrated using Mean Intersection Over Union (MIoU) as the primary evaluation metric. Our experiments on the SemanticKITTI and NuScenes datasets demonstrate significant improvements. Specifically, we achieve a higher MIoU without any increase in prediction time. Our results, through comprehensive experiments and rigorous evaluations, open up new possibilities for enhanced perception systems in autonomous driving and related fields, highlighting the significance of our iterative uniform greedy model soup in advancing LiDAR semantic segmentation.
A hashtag on X (Twitter) can attract a range of stakeholders with diverse perceptions on the hashtag. Under-standing and considering the differences between these stake-holders' perceptions on specific hashtags ca...
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The article proposes models of the structural reliability of a multipath routing network with the possibility of its reconfiguration when switching path segments. The models are focused on optimizing the placement of ...
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Security of smart power systems data points has become a critical issue in recent years. The phasor measurement units transfer the measured data to the central unit for various purposes such as state estimation. Since...
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