Search

Due to the increasing use of the Internet and other online resources, there is tremendous growth in the data of text documents. It is not possible to manage this huge data manually. This has led to the growth of fields like text mining and text summarization. This paper presents a title prediction model for research papers using Recursive Recurrent Neural Network (Recursive RNN) and evaluates its performance by comparing it with sequence-to-sequence models. Research papers published between 2018 and 2020 were obtained from a standard repository, viz. Kaggle, to train the title prediction model. The performance of different versions of Recursive RNN and Seq2Seq was evaluated in terms of training and hold-out loss. The experimental results show that Recursive RNN models perform significantly better than the other models.

This article considers the severe co-channel interference caused by the efficient use of spectrum by multiple beams combined with full-frequency multiplexing. After establishing a forward link model that considers severe rainfall attenuation in higher frequency bands such as Ka, the classic low-complexity precoding algorithm for zero-forcing is improved, and a regularized zero-forcing precoding algorithm considering the influence of system noise is proposed. Based on the dirty paper coding idea, a low-complexity dirty paper regularization zero-forcing precoding algorithm is proposed, which maximizes the signal-to-interference and noise ratio, thereby increasing throughput.

Color 3D printing is an important advancement in 3D printing for lifelike personalization, but color reproduction in paper-based color 3D printers suffers from accuracy issues. In this study, 5 color 3D test charts containing 6 thickness-increasing colored stairs with 13 specific colored bars were developed based on a thickness tunable printed layers design. At the same time, color differences and surface images of tested charts with specific layers were both captured under standard conditions. Results demonstrated that the chromatic aberrations and Mean-SSIM values of color bars showed a slight downward trend as the stair thickness increases, while the linear correlations were not obvious. Combined with color SSIM maps analysis, color samples with small differences in color quality can be clearly detected to analyze the color reproduction performance of paper-based color 3D printers. This research provides new ideas for the development of powerful color 3D test chart to check the color reproduction performance of paper-based 3D printers and predict paper-based color 3D printing.

Point of care (POC) devices enable immediate diagnosis and treatment in settings where equipped facilities, trained expertise and organized transportation are lacking. Epidemics require rapid treatment to deter the spread of diseases. Thus, POC devices can be used to rapidly diagnose the disease, without the need for trained personnel or expensive equipment. Paper is an excellent option for POC applications, mainly due to its physical and chemical properties and cost. Paper has been used in various forms to detect proteins and nucleic acids from pathogens, as they are the most commonly targeted biomolecules for the detection of infectious diseases. Due to their specificity, a diverse range of biomolecules could be detected using methods such as ELISA, gold nanoparticles and electrochemistry. In spite of the aforementioned advantages, instability and cross-reactivity in protein detection and the complexity of nucleic acid detection deter the rapid replacement of conventional diagnostic methods. Thus, in this chapter, we present key technical progress in paper-based POC devices, identify major functional components and provide insight into important challenges faced to produce optimal diagnostic devices.

Paper has been used in analytical applications ranging from diagnostics to environmental monitoring. In 2007, Martinez et al. introduced the first microfluidics paper-based analytical device (μPAD). They patterned the paper with a hydrophobic reagent to form hydrophilic channels for transporting liquid samples from an inlet to a specific reaction area for diagnosis. To achieve an automatic sequential multistep assay on the μPAD, the control of the speed of wicking fluid in a paper is very important. In this chapter, we focus on new fabrication techniques and novel materials for paper-based devices with controllable wicking speeds.

The future of sensing and diagnostic devices for developing countries should be, as described by the World Health Organization (WHO), ASSURED: Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment free, and Deliverable to end users [1]. Paper-based sensors using colorimetric approaches are significant because they feature all key factors for ideal diagnostic devices. Conventional sensors, such as enzyme-linked immunosorbent assay (ELISA), are bulky and complex, requiring different functional blocks including transducers, processing units, detection units, and so on, which can delay sensor response [2]. Current technology based on colorimetry is concerned with the miniaturization of size, decreasing cost, permitting in situ use, and achieving freedom from additional instruments. Moreover, colorimetric sensors can be used for the instantaneous detection of analytes (e.g., protein, DNA, small molecules, etc.), by showing a visually detectable color change [3]. The color change or intensity is proportional to the analyte concentration, which indicates that colorimetric assays have capacities beyond binary sensing.

Blockchain technology and decentralized learning are attracting growing attention. Most existing methods of machine learning is in the centralized form which relies upon the third party in terms of the raw datasets and mining resources. Blockchain solves world centralization problems that keep the system secure through complex mathematical computations puzzle solved by blockchain miners. Concurrently, decentralized learning such as federated model allows the user to collaboratively access the updated prediction model without revealing the training data to the public. By doing so, it provides less power consumption, lower latency that respects the user’s privacy concern. Therefore, we study the extent to which these two technologies can be applied in the real world for faster convergence without compromising user’s security.

Nucleic acid detection has widely been used in key biosensing applications starting from diagnostics, food and water safety, and forensics. Traditionally operated in the laboratory, it has become an essential method to detect nucleic acids sensitively and reliably. With the increasing need of performing the nucleic acid detection outside of the traditional laboratory settings, the concept of “Lab-on-a-Chip”, colloquially known as microfluidic chips, has emerged to offer a portable platform for the field-based diagnostics. Using a fraction of the reagents and short travel length of fluids, it has enabled a rapid progress in point-of-care diagnostics. However, there are still formidable challenges lie ahead. One of the key bottlenecks for a wider acceptance of microfluidics in the main stream diagnostics has been the material of devices and the peripherals required to run the diagnostic device. Currently, polydimethylsiloxane (PDMS) material is a commonly used material for microfluidic applications. Despite its outstanding optical properties and easiness of molding and bonding, this material has intrinsically hydrophobic properties that require a pressure source to drive the sample fluid into the microchannel unless it has been treated prior to filling to become hydrophilic temporarily. The material cost is another prohibiting factor for a wide-spread use of PDMS.

Nucleic acids have been widely recognized as ideal biomarkers in various applications, such as microorganism detection, [50] medical diagnostics [15, 24, 25], food safety [9, 54], environmental monitoring [73], and science forensics [53]. In these areas, nucleic acid testing (NAT) is a widely used analytical tool, as the direct detection of nucleic acid is a highly sensitive and specific technology among early diagnostic methods [56]. NAT is also called molecular diagnostics or genetic testing, which refers to the detection of specific DNA or RNA sequences in the target samples. Based on its outstanding sensitivity [30], NAT has become the standard diagnostic method for the detection of harmful microorganisms, [13] representing a significant index recorded in many types of market reports.

In order to realize Green and Environmental Protection and sustainable development, a particular emphasis is put on NOMEX (aromatic polymer fiber) honeycomb paper based material, which replaces the metal tray of AGV car. With the results of the related finite element simulation analysis of ABAQUS, the mechanical properties and the carrying capacity of NOMEX honeycomb paper-based new material trays are analyzed. It is effectively to reduce costs for the purpose of green environmental protection and this design is also referenced for further research in this area.

Education is a process of gaining knowledge and is important for all as it plays a vital role in shaping the life of a student. The knowledge that we gain through education is evaluated through examinations that the teachers conduct periodically. Performance in an examination is an indication of a student’s proficiency in a particular subject. Courses of an educational curriculum are defined with learning objectives. Question papers consist of questions belonging to different cognitive levels. Generation of a quality question paper is essential as performance in it can influence the career decisions the students take in their life. It is difficult for the teachers to maintain the same level of complexity across the set of question papers that are generated. In this paper, we present a model where the questions are tagged automatically to their respective cognitive levels. It also helps in creating different sets of question papers containing unique questions and also provides a pictorial representation of the percentage of various cognitive levels present in the question paper. The pictorial representation helps the evaluation panel to have an overview of the distribution of cognitive levels in the question paper.

Paper-based sensor is a new analytical technique applicable in healthcare monitoring, environmental monitoring, food quality control, etc. Currently, these technologies are drawing great attention due to ease of fabrication, cost-effectiveness, and their simple, portable, and disposable nature. The inherent qualities of paper, such as availability in large quantities, cheap, lightweight, and biodegradability, are being exploited to develop sustainable devices. The application of paper as a substrate material in litmus paper was dated back to seventeenth century. The semiquantitative detection of glucose in urine and immune chromatographic paper test strips for pregnancy test kits are examples of paper-based sensing devices. Today, microfluidic paper-based sensors with advanced designs involving complex 3D geometrics for handling multiples analytes are under investigation. It is foreseen that advancements in the fabrication and analytical techniques will help in improving the accuracy and sensitivity of paper-based sensors. Different types of detection approaches used in microfluidic paper-based devices are colorimetry, luminescence, electrochemical detection, fluorescence, and surface-enhanced Raman scattering.

Paper-based devices has gained widespread applications with its unique properties like affordability, rapid detection, user-friendliness, sensitivity, specificity etc. These low costs devices are considered as powerful tool for point of care testing because of the inherent microfluidic nature, disposability, flexibility, and ability to store and analyze the sample. With the advancement in fabrication techniques and the requirement of real-time diagnosis and health monitoring, paper-based devices also emerged in the field of wearable diagnostic applications. Depending upon the biocompatibility, paper-based devices are integrated with different materials to be used as in vitro and in vivo wearable devices. In this chapter, we will cover the principles of the paper-based devices along with the evolution of the wearable devices and also briefly review the recent development in the wearable diagnostic devices.

In the last decade, the chemistry research community has witnessed an explosive growth of microfluidic devices made of paper (paper-based microfluidics). Use of paper as a substrate material brings several attractive features including extremely low cost and auxiliary pump-free liquid transportation, among others, and application of paper-based microfluidics to on-site medical diagnosis has been actively pursued. To meet the demand for medical diagnostic devices operable by end-users without expert knowledge in resource-limited settings, recent studies on paper-based microfluidics pay particular attention to simplification of user operations prior to an assay (e.g. achieving multistep enzymatic assays by single pipetting) and resulting signal readout (e.g. achieving naked eye-based analog thermometer-style result interpretation). One of the objectives of this chapter is to overview state-of-the-art research progresses in simplification of user operational procedures and development of equipment-free signal readout approaches. In addition, the basics of paper-based microfluidics including a short history of paper-based microfluidics, a comparison of paper-based and conventional plastic- or glass-based microfluidic devices and general requirements for ideal point-of-care testing devices are described. The authors believe this chapter helps researchers new to the field and researchers with different background to learn about analytical applications exclusively achieved by paper-based microfluidics and future challenges in developing “truly” practical medical diagnostic devices.

Paper-based microfluidic devices (µPADs) offer numerous advantages like low cost, wide range of applications, ease of fabrication/operation, high wicking properties, biocompatibility, non-toxicity, and biodegradability over other analytical devices. We present various fabrication techniques for µPADs reported to date. The fabrication methods reviewed in this chapter can be briefly divided into two types: two-dimensional (2D) and three-dimensional (3D) methods. This chapter discusses the various fabrication methods that are well established over the years towards as well as novel and emerging methods for rapid and cost effective fabrication of paper based microfluidic. The various techniques that has been used for fabricating paper based microfluidic devices(µPADs), including photolithography, embossing, flexographic printing, plasma treatment, laser treatment, PDMS screen printing, wax screen printing, ink-jet printing, and two-dimensional shaping (using X–Y knife plotters) are discussed. A comparison between the fabrication techniques with relative advantages and disadvantages is also provided to make the selection of a method of fabrication easier for a particular application.

Paper-based microfluidics has been extensively used in recent times to perform automated multi-step reactions. One of the requirements to carry out sequential reactions is the ability to control the fluid flow accurately on paper substrates, comparable to what is achieved in conventional microfluidic devices. Flow control still remains a major roadblock in the translation of more complex paperfluidic devices from research labs into the commercial domain. In this chapter, we describe the advantages and limitations of the reported flow control strategies in paperfluidics. Modelling the flow on paper and its implication on the device design are also discussed. Finally, we summarize the challenges for commercialization of these flow-switching devices.

The increasing focus on the environment-friendly electronics and requirement of easily disposable nonhazardous waste has introduced the cellulose paper as an attractive choice for energy storage devices. The disposal of commercially used energy storage devices is a major problem, and it has a great threat to a sustainable environment. Paper-based flexible and low-cost energy storage devices are recently gaining much research attention and are highly needed in various industrial applications. This field is undergoing exciting innovations and holds enormous opportunities for further developments. In this chapter, we review the current developments of the paper-based energy storage devices, their fabrication methods, design strategies, application areas, and challenges for their further growth. An industrial/technological roadmap is also provided to help the readers to understand the future directions in this field.

Paper microfluidics is one of the rapidly growing technology and shown a tremendous caliber in field of healthcare, biochemistry, environmental monitoring, analytical chemistry, etc. Owing to their distinct advantages such as rapid, easy-to-use, compatible with biological, organic and inorganic entities, chemical inertness, robustness, economical, environment-friendly and easy disposal, paper microfluidic-based devices have become a prominent alternative for existing technologies POC technologies. The challenge remains, however, in the designing of an efficient paper-based device for specific applications. A great deal of work has been done in this field to address the challenges in its two key enabling parameters namely ‘materials properties’ and ‘fluid-transport mechanism’ to achieve the functional paper device in an efficient and predictive way. Keeping in mind the previously published articles, the focus here is primarily on showcasing the fluid transport mechanisms through paper-based microfluidic devices. This chapter intends to provide an understanding towards theoretical modelling from a device perspective. The commonly used paper substrates and critical factors involved with any fluid transport phenomena is also covered.

Since the inception of paper microfluidics, there has been a significant leap in developing microfluidic assays for wide range of applications, particularly in healthcare. Considering the average economic status of the population and epidemic outbreaks of diseases within territory of developing nations, it is indeed an essential demand to have an alternate diagnostic plat-form which is ideally expected to serve the purpose at faster and affordable means. In this context, introduction of the paper devices started with qualitative assays and thereafter it has subsequently evolved for semi-quantitative and quantitative platforms. Conventional diagnostic procedures involve time consuming multi step processes that are rather elaborate and expensive, necessitating the involvement of highly trained personnel for analysis. Paper based diagnostic procedures; on the other hand, normally deploy tiny amounts of physiological fluids and consumables, to achieve the same end objective at much reduced expenses and time. Towards this, colorimetric assays have shown significant promises, but are limited by the sensitivity of the diagnostic method. In an effort to improve the sensitivity of the detection, other approaches like fluorescent, electrochemical, luminescence have been progressively explored. In this Chapter, the discussion is systematically organized from fabrication of paper-based devices to their different applications in the diagnostic arena. Starting from the fabrication fundamentals and working principles, we delve deep on different colorimetric detection of bio-molecules, bacteria etc. We also focus on the critical aspect of plasma separation from the whole blood, which is a key for most colorimetric detections. Finally, we summarize the key findings about the advancements of these devices for practical use in resource-limited settings and bring out some new and unaddressed questions as well as challenges in the field.

Microfluidic paper-based analytical devices are an attractive tool for point of care diagnostics as they facilitate fast detection without the need for any sophisticated instrumentation and skilled professional. These devices are disposable, portable, and affordable; hence, they are utilized in almost all the diagnostic domains for carrying out the detection. There are various aspects associated with the paper-based devices, namely working principle, reaction mechanism, fabrication schemes (2D/3D), detection sensitivity, and readout mechanism. Over the period, continuous progress is envisioned in all these domains to enhance the sensitivity of the detection and several variants, namely miniaturization, the inclusion of nanoparticles, multi-functionalization, etc. are also explored to make the detection more efficient. This chapter provides a state of the art review of the various aspects of paper-based microfluidic devices, including their fabrication scheme, sensing methodology, and their several applications in DNA detection domain. Also, advantages, disadvantages, and future aspects of these devices are also discussed.

Due to the non-linear and highly volatile nature of the Stock market, it has become a very challenging task for researchers to make accurate predictions. Improving the efficiency of predictions has become the main goal of many researchers. From the traditional approach of working with historical dossiers to using the latest machine learning and deep learning techniques, researchers are busy finding out the best possible ways of accurate predictions. Many new models are suggested that can make good estimations of stock prices. Investors are interested in knowing both the immediate next-day prices and as well as future share prices in the long run. This paper inspects the algorithms and techniques that are useful for making accurate predictions.

The 3D printed parts made by using the additive manufacturing (AM) process are presently high in demand due to many advantages. This includes well-known features such as the cost-effective method of producing customized parts made of thermoplastic material and having a shorter lead time for a wider range of applications. However, it has also a few limitations like poor dimensional accuracy and resolution. Thus, this technology is not suitable for parts having interior details. So, it is very much essential that the parts being produced using this technology must be characterized and analyzed accordingly so as to increase their potential use in the present market. For this, a review paper has been made in order to study all the main characteristics affecting the rise in demand of parts. Several statistical and optimization techniques have been examined and reviewed. The optimal parameters corresponding to the specific characteristics causing an effect to a large extent have been observed.

To provide business cases and creative ideas for paper-based color 3D printing applied into the map industry, two map categories including military derivative map market and civilian derivative map market were introduced and analyzed with lots of famous applications. According to the statistic results, the 3D map printed by various 3D printing processes will be attributed to a promising 3D printing application market in China. At last, three flexible business modes were also offered with detail cases and usage development guides based on paper-based color 3D printing process.

In the case of information overload of academic papers, the demand for academic paper recommendation is increasing. Most of the existing paper recommendation methods only utilize scholar friendship or paper content information, and ignore the influence of temporal weight on research interest, and hence they are hard to obtain good recommendation quality. Aiming at this problem, the method HNTA for academic paper recommendation based on the combination of heterogeneous network and temporal attributes is proposed. HNTA firstly constructs a heterogeneous network composed of different types of entities to calculate the similarity between two papers, and then the temporal attribute is introduced into scholars’ research interests which are divided into instant interests and continuous interests to calculate the similarity between scholars and papers. Finally, by weighting the above two similarities, the purpose of recommending papers to scholars is achieved. Overall, HNTA can not only comprehensively utilize both relationships of scholars and the content information of papers, but also it considers the impact of the temporal weight of scholars' research interests. By conducting comparative experiments on the data set of the real academic social network: SCHOLAT, the results show that HNTA performs better than traditional paper recommendation methods.

Analysis of images with deep learning and machine learning has recently show more accuracy in medical image processing. The paper review the major machine learning methods is used for the extraction of retinal fundus image. It describes how hardware has been modified according to time for retinal eye segmentation that device is helpful for the environment and also useful for humans The algorithms used for image extraction, clear and noise-free Images of different color scales are used to improve image quality. The paper gives a review of how researchers and scientists have been trying different methods to improve vessel segmentation in the future.

In this paper, we discuss, any work must be reproducible in order to influence research and contribute to our profession's knowledge. Nevertheless, studies show that 70% of university laboratory work cannot reproduce. Reproducible work with not always available data sets or procedures not clearly specified is uncommon in software engineering and complex specifications engineering. The lack of reproducible research prevents development, which means that researchers must replicate a scratch study. The RE researcher will read conference papers, find empirical articles and then review data that can (if available) be replicated in the empirical report. This paper deals with two aspects of the problem and discusses in RE articles, RE documents and theoretical documents. In learning and development of an automatic classification in RE and empirical document identification, recent conference papers and RE documents have been utilized. They use the ERRC approach for performing supervised lecture classifications using natural language therapy and machine learning. Our software is equated with a fundamental keyword approach. We study the paper collections from the engineering conference sponsored by IEEE on Requirements and the International Software Testing and Evaluation Symposium sponsored by IEEE in order to test our process. In all but a few cases, we examine that the procedure of ERRC worked superior than the reference procedure.

Uniqueness is inherited in every individual. Each individual links with another individual forming some social organization. Social organization in common places some trends or norms that influence each of its members. Thus, each individual ascribes common liking or behavior. Analysis of this behavioral pattern leads to the development of a recommender system, which helps to predict and determine the preference an individual ought to opt. Recommender system proves significant in e-commerce marketing strategies. Besides, recommender system finds its application in academics, health, entertainment, etc. This paper proposes a probabilistic model research paper recommender system using naive Bayes algorithm. The originality this paper incorporates is to classify the instances of the target paper into attributes such as title, reference, citation, calculated conditional probability and label them into recommendation or not. Deploying naive Bayes classifier, the evaluation of the current research is done generating the confusion matrix and hence calculated accuracy, precision, recall and F-measure. We have achieved an accuracy of 90.47%.

The paper presents a model that will predict the acceptance of the paper for a particular conference. The model is designed for the conferences, which accept researches done researches in the Machine Learning domain. The dataset that is used to develop this model is the ICLR 2017 (International Conference Of Learning Representation). The model gives its prediction based on extracted features. The features that most of the conferences consider are Number of References, Number of Figures, Number of Tables, Bag of words for ML related terms, etc. Some more features are taken into consideration to give better results such as Length of Title, Frequency of ML related words, Number of ML algorithms, Average length of sentences. The model is trained on the above-mentioned dataset, which contains 70 accepted and 100 rejected papers. For the prediction, different Machine Learning algorithms are used. The model is trained by applying algorithms such as Logistic Regression, Decision Tree, Random Forest, KNN, and SVM. The comparative study of different algorithms on the dataset gives the result that Decision Tress works effectively by providing 85% accuracy.

Seismic vulnerability and its evaluation are a critical issue involving various parameters of structural safety. Various types of vulnerability index method and assessment procedures have been identified by various researchers across the world. This paper summarizes the seismic vulnerability assessment methodology for various typology of buildings. Various rapid visual screening methodologies used in the country have been also reviewed. Various parameters such as number of stories, materials of construction its impact on vulnerability have been discussed. The development of new vulnerability assessment methodology has been described. In few literature paper, retrofit solutions have been discussed for most vulnerable buildings.

Fifth-generation wireless transmission cellular networks require waveforms which have higher data rates, low latency and maximum spectral efficiency. OFDM and advanced LTE is utilized in 4G but due to higher-frequency range of 24.25 GHz and above with latency less than 30 ms for 5G, OFDM cannot be implemented further. Generalized frequency division multiplexing is best suitable multicarrier modulation technique as well as waveform suitable for high-frequency network. Nowadays, in the present scenario, there is a high demand of fast data rates and wider frequency range with efficient latency. This paper deals with various methods and analyses that led to the evolution of GFDM. Comparison of GFDM is done with different types of modulation techniques, and conclusion is drawn that proves its dynamic and advantageous characteristics. GFDM transreceiver can be designed by using MATLAB and Python software; in the paper, second one is taken into consideration. Bit error rate is analyzed that depicts the importance of GFDM in terms spectral efficiency and minimal out of band (OOB) emissions.

It is meaningful for researchers to find the interested and high quality new papers. We propose the Joint Text and Influence Embedding recommendation model (JTIE) to consider both the paper quality and the content correlation. We train a paper embedding based on its core elements: contents, authors and publication venues. The quality of a new paper is evaluated based on the author authority and the venue reputation. The citation relationships between papers are considered asymmetric such that they can reflect the user’s consideration on the intrinsic influence of a paper. We learn user interests by one’s historical references or a set of query keywords. Finally, papers are recommended according to the relatedness between user interests and paper embeddings. We perform experiments against three real-world datasets. The results show that our model outperforms baseline methods on both the personalized recommendation and the query keywords based retrieval.

To study the impact of extraction process parameters on Baume and solid content of extraction solution in paper-process reconstituted tobacco production, artificial neural network and ensemble learning methods are utilized to build the prediction models for the extraction process in the paper. The prediction models describe the influencing factors such as amount of adding water, extraction water temperature, feed mixing time, squeezing time, centrifuge frequency, squeezing dryness and screw pump frequency on Baume and solid content of extraction solution. It is found that the ensemble learning model is better than the artificial neural network model by the comparison of the prediction results of these two models. The influencing parameters of the solid content and Baume are optimized by genetic algorithm based on the ensemble learning model to improve the product quality of the extraction solution. The experimental results show that the proposed models are helpful to improve the product quality of the paper-making reconstituted tobacco.

Anomaly is in general defined as deviation or diversion from the normal. The word anomaly came from the Greek word anomalia which means “uneven” or “irregular”. In our day-to-day lives, we have seen many such irregularities or deviations from normalcy. For example, a condition monitoring system beeps an alarm when it detects any value or parameter of the machine away from the minimum value limit to the maximum value limit, or a credit card fraud alerts the bank and the customer immediately. Now, the crucial task here is how we detect anomalies in data streams. When there is streaming data that is continuously generated from any source, it is called a data stream. The task of finding anomalies from such a stream of data will be a challenging job. In this paper, we will discuss elaborately about data streams and anomaly detection in data streams by reviewing several papers and articles written on this topic.

Mathematical average of word embeddings is a common baseline for sentence embedding techniques which typically fall short of the performance of more complex models such as BERT and InferSent. There has been significant improvement in the field of sentence embeddings and especially towards the development of universal sentence encoder that can be used for transfer learning in a wide variety of downstream tasks. Academic paper retrieval systems are widely used in academic institutions to store and categorise scientific papers and find connections between them using citation links, but these methods do not account for the content of the papers. For unsupervised clustering of these papers, a new approach of sentence embeddings is proposed using concatenated power means sentence embeddings and centroid sentence embeddings. The sentence embeddings so created are clustered using K-means clustering algorithm. The results show a clear increase of 47.94% in cosine distance of nearest papers using concatenated power means sentence embeddings with respect to baseline centroid embeddings for the highest performing GloVe models proving that the computationally inexpensive P-Means clustering sentence embeddings can be used for unsupervised clustering of scientific research papers using their abstracts.

The level of the education is an important aspect in the career of a student. Quality of education is the root parameter to enhance the standard of teaching. In order to achieve superior quality of teaching examination paper’s quality plays a vital role. Student’s knowledge, potential, and skills are all judged with the help of exam papers. Thus, examination papers play a very crucial role in the phase of student’s life and also help in judging the quality of teaching. So, the foremost aim of this paper is to assess the quality of examination paper using fuzzy logic.

Web services that provide academic papers present information based on their own search algorithms. However, since it only judges the relevance between the query and the document without understanding the user’s interest field and the retrieval intent, there is a limit to presenting the optimal information for each individual. In this paper, the user search intention at the time of retrieval is identified based on the subject classification and it is determined as the area of interest. In particular, we propose personalized search as a way to provide information suitable for individual interests when homonym or different meanings are used in different research fields. The proposed personalized search performance based on the subject classification tracking showed higher search performance than the comparison target.

This chapter covers the fundamental theory related to paper-based microfluidics. A brief introduction of the field is presented followed by description of the physical and electrical properties of paper which play a key role in accurate prediction of flow rate. The theory related to fluid flow in paper is then described in detail and categorized into wet-out (Lucas-Washburn equation) and fully wetted (Darcy’s Law) flow for various boundary conditions, geometries, and external factors. Finally, the theory of electro-kinetics in paper is presented with electro-osmosis, electrophoresis, and electrowetting on a dielectric as potential techniques for fluid or particle manipulation in paper-based microfluidic devices.

Automation in every domain is today’s need, so in question paper setting also. The proposed system is automation of paper setting and identification of difficulty level of questions and question papers, which uses blooms taxonomy categorization to classify the questions in easy, medium and high categories. By using blooms taxonomy, the difficulty level of question can be identified, which is helpful in generating qualitative question paper. While categorizing questions, system uses natural language pre-processing techniques, which are understood by computer and also uses Latent Dirichlet Allocation algorithm. Based on paper setter’s requirement, paper setting is done; also, if user wants to check difficulty level of any question paper then this is also done by proposed system using NLP. The paper setting using difficulty measure is right benchmark for evaluating student’s efficiency. Automation of paper setting improves the speed, and difficulty level module improves the quality.

In this paper, decision support system (DSS) for a paper making unit of a paper plant has been developed by using genetic algorithm. Process industries like paper plant, sugar industry, fertilizer, etc., are comprised of various complex engineering units. A paper plant comprises chipping, digesting, washing, bleaching, screening, stock preparation, paper making units, etc. These units are connected in combined configuration. The paper making unit is having four main subsystems. The mathematical model of paper making unit has been developed by using Markov birth–death process. On the basis of probabilistic approach, the differential equations are formed by using of transition diagram. These equations are solved recursively and then reduced to steady-state conditions, mostly required for the paper industry. Normalizing condition has been used to find out the probability of full working state with or without the use of any standby subsystems, and finally performance model has been developed for paper making unit. Finally, the genetic algorithm technique is used for optimum value of the unit performance by coordinating the parameters of all subsystems of paper making unit.

The work presented in this paper forms part of a literature review conducted during a study on error detection and correction systems. The research formed the foundation of understanding, touching on space radiation, glitches and upsets, geomagnetism, error detection and correction (EDAC) schemes, and implementing EDAC systems. EDAC systems have been around for quite some time, and certain EDAC schemes have been implemented and tested extensively. However, this work is a more focused study on understanding and finding the best-suited EDAC solution for nanosatellites in low earth orbits (LEO).

With the development of artificial intelligence ushered in the third wave, how to develop artificial intelligence in the future has become the focus of attention. This article first introduces the basic situation of the development of artificial intelligence, and secondly analyzes the development context of artificial intelligence based on the data of the paper. It then analyzes the hotspots of artificial intelligence application according to the patent data, and finally, it summarizes and forecasts the development trend of artificial intelligence related fields.

This precis presents a research concept paper for Leading Organizations of the Future: it specifies the primary reasons for undertaking the proposed study, states the problem to be examined, particularizes the research question, articulates the conceptual framework for the expanded literature review while flagging one key text, and justifies what methods of data collection and analysis would underpin next steps.

Correspondence is a significant piece of our lives. Hearing disabled individuals who can't talk experience various issues while bantering with regular people. These individuals locally can't speak with others without any problem. There are numerous manners by which individuals with incapacities attempt to convey. Sign language is one of the most natural and sensible ways for the disabled. Since deaf people cannot speak like normal people, they often have to rely on some form of visual communication. It has been shown that they sometimes find it difficult to communicate with ordinary people with their hands, as very few of them are recognized by most people. Communication through signing is a significant method for correspondence with the hearing impaired community. Gestures come in numerous structures, for example, hand gestures, two hands, and face movements. Gesture-based communication can be partitioned into two classifications: static and dynamic. Static signs are a hand setup and a particular shape, addressed by a single image. The dynamic sign is a moving action, represented by a sequence of images. Accordingly, the requirement for an intelligent computer-based program is in desperate need for deaf and dumb people that will empower them to convey all the more successfully with any remaining individuals utilizing their normal hands. This paper presents a top-to-bottom audit of Sign Language recognition techniques, a report related to those methods, and identifies challenges. The various methods and algorithms that have been used in sign language recognition projects have been refined and compared in terms of their advantages and disadvantages.

The numerous features of the reconfigurable antenna are discussed in this paper from the basic idea which will give us the broader perception into the fundamental design approach to the advance methodology. These are the modern technological antennas which are capable of analyzing the surrounding and perform necessary adjustment in order to attain and maintain maximum performance and these adjustments are performed by applying different technologies such as varactors, microelectromechanical systems (based on radio frequency), PIN diodes, and photoconductive plates. This kind of antenna is also capable of avoiding noise sources and capable of improvising to gain of the system and security due to their special properties, they consume less power and saves the energy by directing signals only toward the intended direction. Due to this phenomenal property of self-adjustment of with respect to the environments condition, reconfigurable antenna gets a wide-open field of operation in cognitive radios, satellite communications, multi-input multi-output systems (MIMO), and other various fields.

The present study “Doodling: Introducing paper art into textiles” involves the collection of student scribbled designs from students’ notebooks. A market survey was conducted for the selection of product range. Sourcing of suitable fabric was done in which different fabrics were evaluated by panel of judges. For selection of background colour for blending, six different colour themes were created and evaluated by judges. A total 10 ranges were designed inspired from students scribbled designs. For selection of ranges, ranges were evaluated by panel of judges. Documentation of both scribbled and developed designs was done. Selected ranges were constructed into final products. Market and consumer acceptability were done and responses from both for living area range were found appreciable, very innovative, unique and exclusive. The objective of dissemination of the gathered data was achieved by conducting workshops for students on doodling art. The workshop conducted received positive responses and ample interest was shown by students for attending more such programs.

The recognition of paper currency with various designs is a difficult task for automated banking machines and people with low visions. Due to the wide use of automated system such as ATM, for getting cash, safe and precise methods for recognizing paper currency are highly demanded. Recently, the use of an efficient automatic classifying system becomes one of the most important needs of current banking services. The main aim of this paper is to introduce an intelligent model that can distinguish between different types of Yemeni paper currencies using convolution neural networks (CNN). The system is based on image processing methods to classify different types of currencies efficiently. Moreover, deep learning techniques are used to perform the classification process effectively. The obtained results in the actual implementation of this model are encouraging. The accuracy rate reaches 92.7% in the testing phases.