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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper reviews the performance analysis based on image security for digital watermarked images using different techniques in transform domains. Digital watermarking is a technique of embedding data like text or image which is called watermark into digital data. It can be done by using different methods depending upon the digital data. It is examined that the most robust method in providing the security for watermarked images can be achieved by combining DWT with SVD when compared to other methods. Though DWT itself maintains good authenticity, when SVD is added to DWT, it increases the robustness in extracting watermark image. Firstly, DWT is applied to the original image and then SVD is applied to the resultant of DWT image. The steps involved in the algorithm to embed and extract the watermark are explained. Finally, the performance analysis of DWT and DWT with SVD is compared and quality of the image is examined.

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.

During the COVID-19 pandemic, the effort by most educational institutions (EI) over the world was necessary to ensure the pedagogical continuity during school closures so that schools could fulfill their role. In this chapter, we will describe how EI have switched in record time from traditional teaching using textbooks and face-to-face learning to one-way teaching that in most cases is based on the online screen. We will discuss the situation of distance learning and how stakeholders have prepared to face this pandemic. We will also highlight some challenges of the digital transformation in education during the past decade. An overview of the situation of the disruptive school will be presented, focusing on the main impacts on our society. Then, a brief description of the requirements for remote education in the crisis period will be presented, and adopted solutions will be discussed knowing that most of them were based on the use of online screen solutions. Finally, lessons learned from the COVID-19 pandemic will help all stakeholders to start thinking and designing the future of education.

The cloud computing is the advancement to shared volume of information through the network. There are lots of techniques that are used to providing security for data in cloud. But current techniques are not as better related to the ciphertext. So here, we propose information gathering, sharing and restrictive distribution plan with multi-owner privacy preserving in cloud. Here, data owner can impart private information to group of clients through cloud in secure manner with identity-based broadcast encryption (IBBE) technique.

The purpose of this paper is to automate the process of the billing system of vegetables. Raspberry PI is the heart of this project which monitors all the components in the system. Initially, we capture the different vegetable images and train the AI model with images. These images are used to recognize the vegetables taken by the customer. The camera captures the selected item image and AI model based on convolution neural network recognizes this image using an image recognition technique. Image recognition is not only the technique to find the vegetable, but also using the resistance of the vegetable we can recognize the vegetable. Resistance method of recognizing the vegetable is not suitable because it may cause damage to the vegetables. So we choose an image recognition method. The weight of the item is also measured using the sensor which removes the human intervention in weighing the item. Recognized item and its weight are going to display on the screen. Customer can add or delete the items using user interface. Finally, a bill is created based on the selected items of the customer.

The medical robotics market in the global healthcare industry have spread and extended in these several years. Internet of Medical Robotics Things (IoMRT) is also growing and Internet of Things is extending a new field of healthcare application. Whereas, the previous paper suggested that, in the field of robotics, the cloud platforms for Internet of Robotics Things (IoRT) were gradually developed; therefore, the digital platform and architecture for IoMRT of the healthcare industries should be formulated when necessary. An open healthcare platform for IoMRT is expected to promote and implement the robotics and the IoMRT applications for medical and healthcare communities efficiently. In this paper, we suggest that various medical robotics and the IoMRT-related applications will be designed and verified, while the open platform for medical, healthcare-related Robotics should be proposed and verified by the research initiative named “Open Healthcare Platform 2030–OHP2030.” In addition, the vision for enabling medical robotics and the IoMRT-related applications in the OHP2030 research initiative is explained.

Design and make a paper counting display device. Two parallel plates (plate A and plate B) are connected to the measurement and display circuit through wire A and wire B, respectively. The device can measure and display the number of papers placed between plate A and plate B. Based on the principle of capacitance sensing, when the medium changes, it will cause the change of capacitance capacity, which will be converted into 28-bit digital output for the convenience of microprocessor acquisition and processing.

Majority of Indian population depends on agribusiness for its survival, and it plays a vital role in every nation’s economy. The disease is spread to other plants. Early detection of disease is a significant thing. Detection models to detect the disease are built by direct observation of every plant. This is essential as we can take parameters to restrict. Hence, healthy cropping is necessary for the growing agricultural economy. A better yield of crop is dependent on many factors including disease detection. The on time disease detection helps the farmers to save their crop yield as the remedies can be given on time. In order to solve the problem, various convolution neural network architectures have been designed and tested on labelled data to obtain high accuracy in classification and detection of disease. This work deals with the brief and detailed study of various techniques used for classification and detection of disease in plants based on feature extraction and different training methods.

For primary stage breast cancer detection, mammography techniques are being used for a long time. The disadvantage associated with the technology is taking a long time required by the radiologist to diagnose each image pattern. Moreover, this technique is not suitable for the detection of cancer in younger women. At present, the technique has been upgraded by using the technology-based classifiers, which adds the accuracy, specificity, sensitivity, and time requirement by differentiating malignant and benign. Only limited reviews are available, that focuses on features reduction algorithm and artificial neural networks architecture to enhance the detection techniques. The purpose of this paper is to collect the recently used techniques, algorithms, and future scope related to thermography. The author further suggests the technique to divide the features according to different age groups and to find a unique feature for all.

Quality of service is an important parameter in wireless communication systems. To provide the good data delivery and end-to-end delivery to its users, a system or a network must work on its services. Providing quality of services like excellence throughout and less delay are the main tasks and responsibility of IEEE 802.11e. In this paper, various techniques were discussed to improve the network performance of IEEE 802.11e, and results were compared with existing techniques for better experiments and results.

The centralized and opaque nature of the existing peer review systems and academic management practices of journals have led to various transparency and accountability issues. In this work, we propose a novel smart- contract-based review system that stores data using blockchain and web applications. The proposed method can realize transparent operation of the academic paper review process and improve the quality of research through comparisons of opinions, research communication, and reviewer compensation. In addition, the data generated during dissertation review processes can be accessed transparently, while ensuring security of the generated data.

MANETs is one type of network which contains many free nodes. The nodes can be any mobile device or it can be a particle in terms of swarm optimization (PSO). PSO technique looks like a difficult mechanism, but practically it is having very easy, simple, and direct calculations for finding the optimal route to reach to the destination. The calculation in PSO depends on the swarm which is very close for the nodes. It finds out the optimal solution from the search space. PSO runs through the various iteration level gets applied on different nodes with different parameters for finding best, very nearby solution for destination nodes to send data. In MANETs, nodes can act as a sender, receiver, or router. Also, they can dynamically change their location from one place to other place. This dynamic change in the movement of the nodes structure makes the routing more difficult. It results into route disenchantment in entire route detection delivery method owing to information loss as well as communication overheads, makes difficulty to send the data to the destination node, creates various problems for finding the optimum way between the nodes for transferring data to the destination node. So, by using PSO, we need to make the improvement in the routing because of its mobility nature which depends on the network. The objective of the paper is compared various routing protocols which are used for data sending in MANETs and comparing them with the PSO algorithm used in MANETs. Swarm knowledge-based steering calculation will additionally encourage for particular nature of specially appointed system and will beat in a genuine situation, necessities condition and will change and reproduce to get a productive and fruitful steering convention for MANETs.

Convolutional Neural Network (CNN) architecture is a class of deep learning or deep neural networks, used for analyzing visual information. Traditional CNN architecture has large datasets useful for image classification and recognition. But the datasets in CNN are limited and take time for scene recognition tasks. The time taken for recognizing images plays a crucial role in deep learning. As the datasets are large in number, the object should be compared with millions of images from the dataset, and the time for object recognition goes on increasing. Scene-centric data collection, semantic segmentation, feature selection, and use of residual learning framework to improve the object detection and recognition with respect to time and improve the dataset collection.

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.

The Location recommendation playing a important role for finding interesting places. Although recently researching he has advise places and provide information using socially and geographically, some of which have dealt with the problem of starting the new cold user. The Records of mobility shared on a social networks. Collaborative content-based filters based on explicit comments, but require a negative design sample for a improving performance. negative user preferences not observable in mobility records. However, In previous studies that sampling-based methods and this method does not work well. A Propose system based on implicit scalable comments Content-based collaborative filtering framework is used to avoid negative sampling and incorporate semantic based contents. Algorithm of Optimization is used to major in a linear fashion with the dimensions of the data and the dimensions of the features, dimensions of latent space is represent in a quadratic way. Also established relationship with factorization of the plate matrix plating. Personalized recommendation recommends the Point Of Interest routes by mining users travel records. Finally, evaluated ICCF framework with large-scale Location Based Social Network data set in which users have text and profiles.

Electrocardiogram (ECG) paper records are used commonly for diagnosing heart abnormalities. The stored paper ECG records may be blurred or affected by noise. Enhancement of such blurred paper ECG records is done using a low-pass Wiener filter and background grid removal using adaptive thresholding, signal extraction, and parameter extraction. In this work, clinically important parameters such as heart rate, R-peak, RR-interval, and S-peak are extracted from the enhanced digitized ECG signal, and these extracted parameters are then compared with printed parameters on original paper ECG records. The average absolute error between extracted parameters and original paper ECG parameters is 0.02 with an average accuracy of 97.66%. The extracted digitized signal from stored ECG records may be useful for retrospective analysis of cardiac abnormalities, using automated diagnosis software.

Through this research, we propose a model to generate different sets of question papers automatically using deep learning methodologies. We first develop LSTM classification models to classify questions into Bloom’s level of taxonomy, chapter name and we have an LSTM prediction model to predict the marks to be allocated. These Deep Learning techniques can help in reducing human effort in deciding the marks, the section, and Bloom’s level to be allocated to a question. Given a pool of hundreds of questions, our deep learning models develop a knowledge base consisting of questions and predicted attributes marks, Bloom’s level, and chapter name. We then we have a randomization algorithm to pick the questions for different units of the question paper, keeping the standards to be maintained and even distribution of questions across all topics.

Word Sense Disambiguation (WSD) is a prominent area of research in the field of linguistics. A number of researches have been made to resolve the ambiguity issue in natural sentences. If a sentence has ambiguity or ambiguous word in it, then the meaning of this sentence may differ from context. If the meaning of the sentence is not appropriately inferred from the context, then the WSD algorithms are used to remove the ambiguity. This paper discusses the issue of ambiguity in translation of question paper through various MT tools. In our experiment, we have collected different types of questions for analyzing the impact of ambiguity for wh-questions with respect to other questions (objective, match, fill in the blank and keyword specific). Some machine translators often fail to understand different types of questions and treat them as normal question/sentence. We used five different types of questions in English to translate them using five standard online/offline translators into respective Hindi translation. Our aim is to analyze the impact of translations that arise due to ambiguities. The experiment carried using 150 questions of different types, and the result suggests that most of the translations have performed better in objective questions while the keyword specific questions (such as discuss, explain, etc.) performed poorly.

Constructing well-balanced question papers of the suitable level is a difficult and time-consuming activity. One of the remedies for this difficulty is the use of Bloom’s taxonomy. As we know that, Bloom’s taxonomy helps in classifying educational objectives into levels of specificity and complexity. Therefore, the primary goal of this research paper is to demonstrate the use of Bloom’s taxonomy in order to judge the complexity and specificity of a question paper. The proposed work employs various Machine Learning techniques to classify the question papers into different levels of Bloom’s taxonomy. To implement the same, we collected question papers data set, consisting of 1024 questions, from three universities and developed a web app to evaluate our approach. Our result shows that we achieved the best result with Logistic Regression and Linear Discriminant Analysis (LDA) Machine Learning techniques both having an accuracy of 83.3%.

Recent advancement on Paper-based Digital Microfluidic Biochip (PB-DMFB) creates a new opportunity for low cost diagnostic tests and treatment compared to conventional DMFB. Such DMF chips require new test models and methodologies for testing and fault diagnosis due to its inherent physical structure which is different from traditional DMFBs or ‘Lab-on-a-Chip’(LoC)s. The elementary fluidic operations like droplet transportation, mixing, splitting, incubation and detection are executed on the paper-based microfluidic arrays. However the reliability of those basic fluidic operations can be ensured by functional testing of such chips. In this work a new method is proposed to diagnose various types of faults on a PB-DMFB. Routing based new diagnosis model is proposed where different types of single as well as multiple-site faults on the routing path are considered. We have also considered single site and dual site faults in different mixing modules on such chips and proposed solutions for tackling such mixing-faults to accomplish the entire bio-assay on a Paper-based DMF chip. We are able to find alternate routing path for all such faults occurred and viability of the proposed method is ensured through experimental result which are quite encouraging and significant.

Aptitude test plays a vital role in assessing the ability of a person to perform various tasks and inculcates the ability of numerical reasoning, logical thinking, speed, accuracy and other such skills. Generating an effective aptitude question paper for aptitude test is a non-trivial task and manual generation of aptitude question paper is a conventional method. In this paper, a novel method is proposed, which automatically generates aptitude-based questions with certain keywords using randomization technique. The proposed system has the feature of generating multiple-related answers including the correct option for every generated question, and at the same time, it verifies the user’s response in real time and generates score. It overcomes the major limitations of the existing automated system where question papers are generated by random selection of questions from question banks prepared by the examiner. The implementation of the proposed system has been shown along with the performance evaluation on the basis of repetitiveness of same questions.

Requirements engineering (RE) is a practice that helps to determine the customers, users and stakeholders needs in the structure of system and software that can lead towards high probability to meet the requirements of end users. Requirements engineering process is a key part of software engineering and crucial for developing real-world software systems. Requirements elicitation is the initial, foremost and crucial phase of an RE process. It includes activities that are set to uncover, acquire and elaborate requirements for software systems. Generating software requirements is an important and essential requirement for the next stages of the system development life cycle of software development. It is important to understand and tackle wider elicitation problems and challenges on a large scale, particularly in the area of geographically dispersed software development. There are different studies aimed at generation, but these are relatively small but in this research article there is a whole systematic review of requirement gathering techniques. Different requirement gathering techniques are acknowledged and related factors are also elaborated. This paper discusses the overview of systematic literature review, various problems that we face in requirement capturing their suitable methods are also identified. This study also deliberates comparative review, pros, and cons of requirement elicitation techniques.

Cloud computing security remains the goal of both cloud service providers and customers. With many of the security threats to the security of cloud computing, Distributed Denial of Service (DDoS) attacks is one of the most worrisome. The danger posed by the DDoS attacks are already known and continue to be the predominant security challenge in reaching an impervious and guaranteed safe cloud computing resources and service delivery. Many researchers have proposed many detection and defense techniques to protect cloud computing against DDoS attacks. In this paper, we present a review of many detection techniques that are useful in spotting DDoS attacks that are cloud-based and make a comparative analysis between them to find a suitable technique for spotting these cloud computing based DDoS attacks.

The regularly expanding measures of patient’s information as medicinal image, forces new difficulties to clinical routine, for example, diagnosis, treatment and checking. Image mining is the way toward seeking and finding profitable data learning of information. It is connected on image preparing and machine learning. Picture handling is having essentially for ailment recognition on restorative pictures. These sickness acknowledgment and arrangement are particular to human organ and image nature. With help of image processing and machine learning strategies it is conceivable to computerize as well as help doctors in clinical analysis. In this paper depicts the utilization of different image processing and machine learning methods for identification of eye diseases.

Cloud computing relies on sharing of resources to achieve coherence and economy of scale, similar to a utility. In existing system ABE (attribute based encryption) is used to maintain security. Attribute-based encryption is a type of public-key encryption in which the secret key of a user and the cipher-text are dependent upon attributes. In computing, data de-duplication is a specialized data compression technique for eliminating duplicate copies of repeating data. Related and somewhat synonymous terms are intelligent compression and single-instance storage. In this paper we studied the different attribute storage mechanisms as well as de-duplication system.

The main intention of this study paper is to explore and analyze the numerous methods that are used for license plate extraction and identification. Analysis is done by detailed study of the prevailing methodologies and their drawbacks were duly notified. Various concepts such as OCR reading, bounding method and other computational techniques were thoroughly studied and are recorded below. Thus, with the knowledge gained from this review, a pristine and a noble method were developed for the same by utilizing the concept of API. This not only involves extraction of characters from the license plate but also aids in the identification of the vehicle model, as a whole, with the help of image processing. This method is not merely effective for detection but also holds good for certain amount of automation with the aid of the upheld database. These techniques of detection backboned by databases can prove to be very effective in a wide range of use-cases in the current world. Such identification as well as compilation of useful data can be implemented in applications such as automatic toll system, fee collection in parking areas, traffic disciplinary maintenance, vehicle monitoring inside a premise and much more.

Reviewer assignment problem is a time-consuming work, so an automatic reviewer recommendation method is important and necessary, where how to represent the reviewers’ research interest was the key. In order to mine the research interests, the published research papers are firstly collected from Internet; secondly a diversified knowledge modeling method based on the cluster algorithm is proposed; finally carry out research on the similarity computing between the pending paper content and the experts’ research interests, and then recommend experts based on the similarity. Experimental results show that the proposed diversified knowledge model can find the appropriate paper reviewers effectively, which can reduce the workload of the editors greatly.

Since food is one of the essential human needs, studies on this topic have always been a global concern. With the advent of COVID-19 and the emergence of many problems in all aspects of the food supply chain (such as production, transportation, distribution), this issue has become doubly important. This paper discusses an MINLP optimization model for handling the impact of the COVID-19 pandemic based on the food supply network through Food Hubs (FHs). In this research, the concept of FH has been used for a more effective and faster connection of consumers to production sites. Due to prevention of the spread of coronavirus and the quarantine conditions, the areas have been divided into two parts (high-risk and low-risk) and two scenarios have been defined for this supply chain. The purpose of this paper is to reduce costs and environmental impacts as much as possible. The proposed model is solved by GAMS software for small- and middle-size test problems, and it is solved with genetic optimization algorithm as a meta-heuristic approach for large-size problems. Also, to solve the developed linear multi-objective model, augmented ɛ-constraint approach is applied, and a real case study from Iran is examined to illustrate the validation of the proposed model. Numerical and computational results are provided to prove the efficiency and feasibility of the presented model. Finally, sensitivity analysis is presented to evaluate the effect of changing some parameters on variables and objective functions.

Portable electronic devices and wireless communication enable a broad range of applications including environmental surveillance, food safety monitoring and point-of-care testing. Particularly, by incorporating smartphone and microfluidic paper-based device, rapid analysis, mobile laboratories for chemical assay, remote sensing and data management become more available. In this work, we designed and fabricated a 12-unit paper chip for colorimetric detection of metals. 48 chips can be obtained on a single A4 size paper within 5 min. Additive manufacturing technology was employed to construct the experimental platform. A cellphone’s camera was used to capture colorimetric images, and then the RGB values of the color images were converted to grayscale via a self-built App. The intensity reflecting metal concentrations were processed and analyzed by the same app and a remote server. To demonstrate the utility of this approach, Fe solutions were tested. The detection limit of this method is 0.2 μg. The presented device and data processing tools hold a potential to assist mobile sensing, pollution detection and healthcare in routine work.

Based on two-domain DNA strand displacement, a computing model is proposed. The model is used as a “referee” for two players in a well-known Rock-Paper-Scissors game, which can be utilized as an example of the study of game theory and artificial intelligence (AI). A molecular model based on Two-domain strand displacement is applied to emulate the process of the game. The output of the circuit shows the final result of the underlying game, that is, each player’s win, lose and draw. The two players hold a win of one inning and two win of three innings which are simulated by employing Visual DSD software. The simulation results show that the molecular model is correct and feasible. The establishment of the computing model is hoped to provide some new insights for the AI in the field of nanotechnology.

This research aims to develop a cloud-based safety information and communication system in infrastructure construction projects. To achieve this aim, the current paper-based construction safety management was reviewed. Following this, a cloud-based safety information and communication system, which incorporates geographic information system (GIS) and Global Position System (GPS), has been designed and developed. Google Drive was chosen as the system database and hosting web server, where the Google Docs, Sheets and Forms are tailored to accurately capture on-site safety data with mobile devices. The captured data with its actual construction activity location is automatically synchronized in Google Fusion Tables and the processed safety information is visualized simultaneously on Google Maps together with the project’s general information. Both data capturing and visualization processes are managed in a single web interface, bringing easy-to-use benefits to end users. The system was trial tested with a real case project, which confirmed its usability and benefits. The system developed in this research has potential applications in managing safety in infrastructure construction projects.