Accuracy in training sentiment analysis models for large number of review datasets is affected by the correct classification of sentiment labels. Improving the accuracy of sentiment labels, and text representation als...
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The world health organization(WHO)terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific *** and accurate detection of spread in affected regions can save preciou...
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The world health organization(WHO)terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific *** and accurate detection of spread in affected regions can save precious *** the severity of the disease,a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text ***,the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data,as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are *** research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache *** method consists of two main parts:the first part collects data from social media using Apache Flume,while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache *** overcome the noisy and unstructured nature of the data,the process of extracting information is characterized by pre and post-filtration *** a result,only with the integration of Flume and Hive with filtration and polarity analysis,can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social *** introduce how the Apache Hadoop ecosystem–Flume and Hive–can provide a sentiment analysis capability by storing and processing large amounts of *** important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines.
Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a...
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Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software Engineering,and iTrust Electronic Health Care system.
Internet of Things is no longer a new topic. Long ago, the network of things was a rare thing which could be seen only in offices, industries, and labs. Nowadays it is approaching becoming a ubiquitous appliance as a ...
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Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. ...
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technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching metho...
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This study addresses a gap in the literature regarding the relationships between sleep quality, obsessive–compulsive disorder (OCD), fear of missing out (FoMO), psychological resilience, and problematic Instagram use...
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Automatic Speech Emotion Recognition (ASER) has recently garnered attention across various fields including artificial intelligence, pattern recognition, and human–computer interaction. However, ASER encounters numer...
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Abnormalities of the gastrointestinal tract are widespread worldwide ***,an effective way to diagnose these life-threatening diseases is based on endoscopy,which comprises a vast number of ***,the main challenge in th...
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Abnormalities of the gastrointestinal tract are widespread worldwide ***,an effective way to diagnose these life-threatening diseases is based on endoscopy,which comprises a vast number of ***,the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the ***,this led to the rise of studies on designingAI-based systems to assist physicians in the *** several medical imaging tasks,deep learning methods,especially convolutional neural networks(CNNs),have contributed to the stateof-the-art outcomes,where the complicated nonlinear relation between target classes and data can be learned and not limit to hand-crafted *** the other hand,hyperparameters are commonly set manually,which may take a long time and leave the risk of non-optimal hyperparameters for *** effective tool for tuning optimal hyperparameters of deep CNNis Bayesian ***,due to the complexity of the CNN,the network can be regarded as a black-box model where the information stored within it is hard to ***,Explainable Artificial Intelligence(XAI)techniques are applied to overcome this issue by interpreting the decisions of the CNNs in such wise the physicians can *** play an essential role in real-time medical diagnosis,CNN-based models need to be accurate and interpretable,while the uncertainty must be ***,a novel method comprising of three phases is proposed to classify these life-threatening *** first,hyperparameter tuning is performed using Bayesian optimization for two state-of-the-art deep CNNs,and then Darknet53 and InceptionV3 features are extracted from these fine-tunned ***,XAI techniques are used to interpret which part of the images CNN takes for feature *** last,the features are fused,and uncertainties are handled by selecting entropybased *** experimental results show that the
The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very *** is a trade-off in the objectives in the existing techniques of MultipleSequence A...
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The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very *** is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in th
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