Escalating traffic congestion in large and rapidly evolving metropolitan areas all around the world is one of the inescapable problems in our daily lives. In light of this situation, traffic ...monitoring and analytics is becoming the need of the hour in today's world. Real-time traffic analysis requires processing of data streams that are being generated continuously in real time to gain quick insights. The challenge of analyzing streaming data for real-time prediction can be overcome by exploiting deep learning techniques. Taking this as a motivation, this work aims to integrate big data technologies and deep learning techniques to develop a real-time data stream processing model for vehicle traffic forecast using ensemble learning approach. Real-time traffic data from an API is streamed using a distributed streaming platform called Kafka into Apache Spark where it is processed, and the traffic flow is predicted by a neural network ensemble model. This will reduce the travel time, cost, and energy through efficient decision making, thus having a positive impact on the environment.
Despite the spontaneous regenerative properties of autologous bone grafts, this technique remains dilatory and restricted to fractures and injuries. Conventional grafting strategies used to treat ...bone tissue damage have several limitations. This highlights the need for novel approaches to overcome the persisting challenges. Tissue-like constructs that can mimic natural bone structurally and functionally represent a promising strategy. Bone tissue engineering (BTE) is an approach used to develop bioengineered bone with subtle architecture. BTE utilizes biomaterials to accommodate cells and deliver signaling molecules required for bone rejuvenation. Among the various techniques available for scaffold creation, 3D-printing technology is considered to be a superior technique as it enables the design of functional scaffolds with well-defined customizable properties. Among the biomaterials obtained from natural, synthetic, or ceramic origins, naturally derived chitosan (CS) polymers are promising candidates for fabricating reliable tissue constructs. In this review, the physicochemical-biological properties and applications of CS-based 3D-printed scaffolds and their future perspectives in BTE are summarized.
Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent ...reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.
The binding studies of DNA with small molecules have been an emerging field of research all the time since DNA as the genetic material is a major biological target for various drugs. Interpretation ...of small molecule-DNA binding helps in understanding their interactions with designing new drugs of greater medicinal activity. Posaconazole is an antifungal drug in the class of triazoles which are known to possess numerous pharmacological properties. In this work, the nature of the binding of posaconazole with calf-thymus DNA has been studied using spectroscopic techniques and molecular docking studies. A binding constant of the order of 103 M−1 was observed from UV–visible and fluorescence studies for the interaction between posaconazole and calf-thymus DNA. The fluorescence property of posaconazole was found to be quenched by calf-thymus DNA with a quenching constant of the order of 103 M−1. Competitive displacement of ethidium bromide and Hoechst 33258 by posaconazole using fluorescence technique suggested minor groove binding of posaconazole in calf-thymus DNA. Confirmation of the binding mode was further complemented by the viscosity measurement and DNA melting studies followed by KI quenching experiments. The studies on the effect of ionic strength on the binding suggested a possible role of electrostatic force in the interaction. Molecular docking studies reflected a crescent shape of the posaconazole within the minor groove of calf-thymus DNA validating the experimental findings showing the residues involved in the interaction.
In day-to-day life transportation plays a major role in cities. Present day traffic management is a complex task for transportation agencies through traditional approaches, hence Intelligent ...Transportation systems is applied to give traffic management solutions like parking, E-toll charge and traffic control by analyzing data from related sources. Data is collected from various sources for analyzing transportation need’s, yet transportation issues remain one of the major tribulations in cities. Unstructureddata gives enormous information load for big data analytics, but the unstructured content processing is a challenge in industry. Passive data like social media data is a major data sources for Intelligent Systems, social media applications such as Twitter, Facebook where user can share live comments based on their interaction with the world is a rich source for passive data. Social media data helps in analyzing traffic issues like traffic jam, accident locations, road condition etc. Major issue with social media data is processing and analysis of data is very complex because of volume and data format. Big data architecture helps in extracting, processing, loading in database and analyzing this unstructured data. To identify thesentimentalanalysis is majorly classified based onpositive, negative and neutral tweets. As the polarity of neutral tweets is zero it cannot be used for Opinion mining. So, this paper is focused on Neutral tweets classification based on feature selection. Part of Speech (PoS) tagging is used for labeling the words of the text in the tweets to find nouns example location, date and time are compared with the other attribute values for improving the classification of neutral tweets. Research work shown in this paper has taken social media speech data (Tweets) from twitter as input and preprocessing techniques are applied on the data collected, Methods such as feature selection are then used to extract the features related to tweets for classifying neutral tweets for better understanding on road condition, identification of traffic patterns and finally traffic behavior is analyzed by using Ensemble machine learning algorithm. In the proposed model to measure the sentimental analysis a new approach is provided based on feature selection. The findings disclose with SentiWordNetopinion lexicon approach gives 56% accuracy of positive or negative opinion using twitter dataset, the results of feature selection-based opinion mining proposed model increased substantially with 88% accuracy.
Plant Leaf Disease Detection Using CNN Algorithm Deepalakshmi P; Prudhvi Krishna T; Siri Chandana S ...
International journal of information system modeling and design,
01/2021, Letnik:
12, Številka:
1
Journal Article
Recenzirano
Agriculture is the primary source of economic development in India. The fertility of soil, weather conditions, and crop economic values make farmers select appropriate crops for every season. To meet ...the increasing population requirements, agricultural industries look for improved means of food production. Researchers are in search of new technologies that would reduce investment and significantly improve the yields. Precision is a new technology that helps in improving farming techniques. Pest and weed detection and plant leaf disease detection are the noteworthy applications of precision agriculture. The main aim of this paper is to identify the diseased and healthy leaves of distinct plants by extracting features from input images using CNN algorithm. These features extracted help in identifying the most relevant class for images from the datasets. The authors have observed that the proposed system consumes an average time of 3.8 seconds for identifying the image class with more than 94.5% accuracy.
In the past decade, social media networks have received much attention among ordinary people, agencies, and research scholars. Twitter is one of the fastest-growing social media tools. By means of ...the Twitter application on smartphones, users are able to immediately report events happening around them on a real-time basis. The information disseminated by millions of active users every day generates a new version of a dynamic database that contains information about various topics. Twitter data can be utilized as a major traffic data source along with conventional sensors. In this aspect, this paper presents a novel firefly algorithm-based feature selection with a deep learning model for traffic flow analysis (FFAFS-DLTFA) using Twitter data. The goal of FFAFS-DLTFA is to determine the class labels for tweets as relevant to traffic events. The proposed FFAFS-DLTFA encompasses several processes, such as preprocessing, feature extraction, feature selection, and classification. Primarily, tweets are preprocessed in several ways, such as tokenization, removal of stop words, and stemming. At the same time, three types of embedding vectors, unigram, bigram, and POS features, are used. In addition, the firefly algorithm (FFA) is applied for the optimal selection of feature subsets. Finally, a deep neural network (DNN) model is applied for the identification of tweets into three classes, namely, positive, neutral, and negative. The performance validation of FFAFS-DLTFA takes place using the benchmark Kaggle repository, and the results are inspected under different aspects. The experimental values demonstrate the better performance of FFAFS-DLTFA on the other techniques with the maximum accuracy of 98.83%.
The long-term stability of protein therapeutics in the solid-state depends on the preservation of native structure during lyophilization and in the lyophilized powder. Proteins can reversibly or ...irreversibly unfold upon lyophilization, acquiring conformations susceptible to degradation during storage. Therefore, characterizing proteins in the dried state is crucial for the design of safe and efficacious formulations. This review summarizes the basic principles and applications of the analytical techniques that are commonly used to characterize protein structure, dynamics and conformation in lyophilized solids. The review also discusses the applications of recently developed mass spectrometry based methods (solid-state hydrogen deuterium exchange mass spectrometry (ssHDX-MS) and solid-state photolytic labeling mass spectrometry (ssPL-MS)) and their ability to study proteins in the solid-state at high resolution.
DNA is the major target for a number of pharmaceutical drugs. The interaction of drug molecules with DNA plays a major role in pharmacokinetics and pharmacodynamics. Bis-coumarin derivatives have ...diverse biological properties. Here, we have explored the antioxidant activity of 3,3′-Carbonylbis (7-diethylamino coumarin) (CDC) using DPPH, H2O2, and superoxide scavenging studies followed by its binding mode in calf thymus-DNA (CT-DNA) using several biophysical methods including molecular docking. CDC exhibited comparable antioxidant activity to standard ascorbic acid. The UV–Visible and fluorescence spectral variations indicate the CDC-DNA complex formation. The binding constant in the range of 104 M−1 was obtained from spectroscopic studies at room temperature. The fluorescence quenching of CDC by CT-DNA suggested a quenching constant (KSV) of 103 to 104 M−1 order. Thermodynamic studies at 303, 308, and 318 K revealed the observed quenching as a dynamic process besides the spontaneity of the interaction with negative free energy change. Competitive binding studies with site markers like ethidium bromide, methylene blue, and Hoechst 33258 reflect CDC's groove mode of interaction. The result was complemented by DNA melting study, viscosity measurement, and KI quenching studies. The ionic strength effect was studied to interpret the electrostatic interaction and found its insignificant role in the binding. Molecular docking studies suggested the binding location of CDC within the minor groove of CT-DNA, complementing the experimental result.
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