Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machi...
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In recent years, the integration of sentiment analysis into financial markets has gained considerable attention, particularly through the use of machine learning and deep learning techniques. This survey explores the ...
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This research presents a novel methodology for cloudburst forecasting using the XGBoost (Extreme Gradient Boosting) machine learning algorithm. With the escalating impact of climate change, accurately predicting extre...
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Purpose-The paper aims to introduce an efficient routing algorithm for wireless sensor networks(WSNs).It proposes an improved evaporation rate water cycle(improved ER-WC)algorithm and outlining the systems performance...
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Purpose-The paper aims to introduce an efficient routing algorithm for wireless sensor networks(WSNs).It proposes an improved evaporation rate water cycle(improved ER-WC)algorithm and outlining the systems performance in improving the energy efficiency of *** proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are ***/methodology/approach-This proposed improved ER-WC algorithm is used for analyzing various factors such as network cluster-head(CH)energy,CH location and CH density in improved *** proposed study will solve the energy efficiency and improve network throughput in ***-This proposed work provides optimal clustering method for Fuzzy C-means(FCM)where efficiency is improved in *** evaluations are conducted to find network lifespan,network throughput,total network residual energy and network *** limitations/implications-The proposed improved ER-WC algorithm has some implications when different energy levels of node are used in *** implications-This research work analyzes the nodes’energy and throughput by selecting correct CHs in intra-cluster *** can possibly analyze the factors such as CH location,network CH energy and CH ***/value-This proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.
The COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COV...
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Sentiment analysis is an analytical subfield of Natural Language Processing (NLP) to determine opinion or emotion associated with the body of the text. The requirement for social media sentiment analysis has exception...
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Multi-label Text Classification (MTC) is a challenging task in Natural Language Processing (NLP). The goal of the MTC task is to label a document with a set of labels. By incorporating various term weighting schemes i...
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Multi-label Text Classification (MTC) is a challenging task in Natural Language Processing (NLP). The goal of the MTC task is to label a document with a set of labels. By incorporating various term weighting schemes in MTC, high dimensional feature space has been generated;due to that, multi-label learning algorithms face substantial problems in performing MTC tasks. To deal with these issues, Feature Selection (FS) approaches are effective solutions. This paper proposes a Lightweight Term-weighting FS (LwTwFS) approach based on a modified Chi-square (CHI) filter-based FS method to deal with this issue. The modified CHI approach works for Inter-Class Concentration (ICC) and Intra-Class Dispersion (ICD), and its strength has been increased by adding positive and negative correlations. A novel modified equation has been introduced to distribute the features among the categories (i.e., here, multi-label) in the corpus. The proposed modified CHI-based FS approach works on the term weighting-based Feature Extraction (FE) approach. Multi-Layer Perceptron (MLP) has been used in the classification phase due to the adaptive learning property, which refers to learning how to do tasks based on data provided during training or prior experience. We have used two publicly available multi-label corpora for experimental verification: the Arxiv Academic Paper Dataset (AAPD) and the Reuters Corpus Volume I (RCVI-V2). According to the results, in terms of performance, the LwTwFS methodology combined with the MLP classifier surpasses other combinations in terms of Jaccard Score (JS), Hamming Loss (HL), Ranking Loss (RL), Precision (Pr), Recall (Re), and F-micro and F-macro. For the AAPD corpus, the LwTwFS method achieves the best JS, HL, RL, Pr, F-micro, and F-macro values, which are 0.9636, 0.0121, 0.0303, 0.9636, 0.9882, and 0.9894. For the RCVI-V2 corpus, the LwTwFS method achieves the best JS, Pr, Re, F-micro, and F-macro values of 1.0000, and HL, RL values of 0.0000. Empirical res
In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT...
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Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to t...
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Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alte...
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Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.
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