Search

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.

Immunosensors are promising diagnostics for the next-generation healthcare systems and are evolving constantly with technological advancements. In recent days, immunosensors have found wide attention in the field of healthcare/clinical diagnostics due to their excellent capabilities of detecting various causative agents for diseases/disorders. Unlike the conventional immuno-technologies, immunosensors support ASSURED criteria for the diagnostics provided by the World Health Organization, and hence it has found wider commercial attention globally. In this chapter, we have summarized such immunosensors fabricated using paper substrates in comprehensive details and pictorial illustrations. First section of this chapter describes the conventional immuno-technologies used in biomedical/clinical applications and their associated limitations. In the next section, the chapter describes the transformations of clinical diagnostic technologies to the adoption of inexpensive paper-based solutions. Followed by this, the chapter also describes different types of paper-based immunosensors used for biomedical/clinical applications. Further, the recent trends and advancements have been summarized and concluded with detailed discussions in the next section.

This paper gives a review on the various modulation techniques that have been used to eliminate lower-order harmonics in multilevel inverters. The output voltage with high quality can be achieved using multilevel inverters. The selection is based on switching losses, power losses, noise, etc. Many authors proposed different modulation techniques such as sine triangular, selective harmonic selection (SHE), model predictive control techniques, space-vector-based modulation techniques. This paper presents a detailed review of existing harmonic elimination methods as well as concludes with the best method.

The explosive growth of the number of papers leads to the over-expansion of the paper information, resulting in the problem of information overload, causing a lot of trouble for researchers to find the papers they need. This paper implements a paper recommendation system based on LDA and PageRank. In this system, we recommend papers in the same field to researchers by modeling and analyzing paper data. Therefore, we can provide researchers with a quick and effective reference for related papers in the research field. In this paper, the probability distribution calculation and keywords extraction of paper topics and words are carried out according to the LDA of papers in the paper pool. At the same time, word2dec is used to represent the topic vector, doc2vec is used to represent the paper vector, and by calculating the system similarity between documents and topics, we get the top N papers that are most similar to the topic input by users. We use the PageRank algorithm to reorder the options in the reference network to get the final recommendation results. Under the experimental data set, the accuracy of system recommendation is more than 60%, which has high reliability and achieves the corresponding recommendation effect.

Citation evaluation has become one of the important indexes to evaluate the paper influence. In the academic area, paper influence is quite important to evaluate the author’s academic level, in addition to help researchers to identify important academic literatures. The traditional citation evaluation methods are mainly developed based on the citation frequency. Nevertheless, other citation characteristics, such as the nature, depth, and distribution are also valuable. Citation nature includes positive, neutral, and negative citations. The depth can be divided into deep, medium, and shallow citations, which could reveal the correlation and the degree of paper reference between citing and cited papers. The citation distribution contains three types: forepart, middle, and tail citations. The data sample of the empirical study is collected from highly cited papers in Chinese and English core journals. Firstly, by using content analysis methods, we analyze the citation evaluation from three dimensions of Chinese papers. Secondly, we compare the similarities and differences between Chinese and English papers. Thirdly, we show a series of results to verify the accuracy and effectiveness of our method.

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.

Automated identification of sarcasm refers to the job of anticipating wit within a message and is a critical advance in the field of sentiment analysis, taking into consideration, commonness as well as difficulties in the sphere of sarcasm detection in a text which bears sentiment. Starting with a proposition that utilized features based on speech, this domain has seen immense enthusiasm from the community of sentiment analysis. This paper is an assemblage of efforts that have been carried out in the past in the field of automatic sarcasm detection. It depicts the methodologies, datasets, issues, and patterns in sarcasm detection. The paper describes mutual tasks, provides recommendations to future work, and talks about representative performance values. As far as assets to comprehend the futuristic, the review shows numerous effective analogies—most conspicuously, an abridgment of papers from the past along various proportions like the datasets used, types of features, and annotation techniques with the help of a table.

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.

A solution of whole paper packaging design for large lithium batteries and its material matching were proposed for meeting a requirement of modern logistics distribution. In this study, through simulating the common transportation environment, the protection performance of the transportation packaging for large lithium battery was analyzed. Based on ISTA 3E transportation safety inspection standards, the safety performance of the corrugated board box packaging was tested via related experiments. The results show that the whole paper packaging for large lithium batteries passed the impact test, pressure test, vibration test and rotary drop test experiment under detection conditions of the ISTA 3E standards. It can ensure the safety of the large lithium battery in process of actual transportation.

Till the 1950s & till Grashof law was applied, Roll to sheeting Equipment manufacturers were not able to develop Dual rotating Knives sheeters. Till then, it was either Guillotine shears with production speed limitations or a Single rotating knife with output defects. Dual rotary knife machines became successful only with the Adaptation of Four-bar linkage mechanisms (Double Drag link) for avoiding parallelogram defects, for achieving diagonal length and cut length accuracies of < ±0.5 mm of the set lengths, and for achieving higher production speeds at 100 mtrs/min. New servo drive technologies in both AC and DC saw the emergence of machines with more accurate performance, at higher productivity, and the highest machine uptime availability.

In the present scenario, electric vehicles and hybrid electric vehicles are fast replacing the combustion engine-type vehicles. Due to this sudden surge in growth of electric vehicles, there arises a major concern for the continuous overseeing and the need for safety enhancement features for battery, drivetrain and what should be done in case of emergencies. There are numerous ongoing works and efforts in the area of electric vehicles in order to make them cost effective and increase their efficiency. One of the main factors to be considered for the failure-free operation of vehicles is the batteries used. In this comprehensive literary review, the working and safety aspects of lithium-based battery used in electric vehicles are analysed. Safety goals for the safe operation of electric vehicles with respect to the batteries and battery management systems (BMS) involved are discussed. Various methods and technologies adopted and proposed to achieve these goals are analysed here.

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.

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.

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.

The vulnerability of many of the existing methods of protecting important documents of the enterprise and, first of all, paper documents, from fakes and falsification is shown. The analysis of the applied methods of protection allowed us to single out the technology of the voice signature (VS) of the document as one of the most promising. The essence of a VS is the transfer of special so-called audio markers in the document containing biometric information about the author of the document, as well as the most significant information of the document. The use of audio markers is an additional degree of document security. Their use makes it possible to identify cases of drafting documents under duress or in inadequate conditions. For this purpose, as part of the VS, it is proposed to transmit also information about the tremor of the fundamental frequency (FF) of the author’s voice. This indicator correlates with the current functional and psycho-emotional state of a person. A technology is proposed to minimize the reverberation effect, which is usually present in the office of the management team of an enterprise. Experimental studies have shown the possibility of reducing the degree of influence of this effect by 5–9 times.

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.

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 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.

In order to study color control and quality test of hologram paper printing, make a test form. Under the same printing conditions, print a test form on different substrates including different hologram papers and coated paper. Measure chromatic value and a series of dot area density using SP64, which is an integrating sphere color measurement instrument test, and then integrate and analyze the data. Under the same dot area, make a color reproduction comparison between hologram paper and coated paper. Scan the print proofs which are within different horizontal and vertical pound lines and fonts, and then make a reproduction comparison between hologram paper and coated paper in different fonts and different color aspect. Ascertain the differences between coated and hologram paper.

India has the second-largest road network system in the world. Roads are the most reliable and accessible system for transportation which determines the growth of the country. After construction, its condition goes on deteriorating with time due to several factors (load factors, environmental factors, etc.). Pavement performance prediction models developed by the researchers include models related to rutting, cracking, potholes, etc., and are applicable only for a specific set of traffic or environmental conditions. Thus, these models lack universal acceptance and applications. Development of realistic and efficient pavement condition rating model is very essential to determine the future condition of the pavement and its service life. An effort has been taken to identify the various parameters that affect the condition of the pavement and different tools used for modeling based on the literature review. The different tools for modeling include regression models, Markovian probabilistic process, multi-attribute utility theory, soft computing techniques, etc.

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.

There are two sides of every coin, it happens with technology and there are pros and cons of it. In the current scenario, vehicles have provided a convenient commute, but the increasing number of vehicles on roads creates heavy traffic and road accidents. To overcome the problem of road accident, our project on automatic vehicle accident detection and rescue system will help detecting the accident and call in the emergency medic with sending current location. Our survey on this topic is made to construct such a system which is efficient and reliable. Moreover, we analyze different approaches on which work has been done to create a successor system which overcomes false detection of an accident.

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.

Paper microfluidics is a subarea of microfluidics in which porous materials are used to create devices. Advantages of paper microfluidics include fluid transport via capillary forces, so that external pumping equipment is not necessary, and the use of less expensive materials than those commonly used in conventional microfluidic devices. Paper microfluidics enables the development of fully disposable devices that are appropriate for use in even the lowest-resource settings, and the potential for high impact improvement to human health. In this chapter, we first discuss the paper microfluidic device fabrication processes of materials selection, fluidic boundary definition, and reagent patterning. Next, we discuss tools development for manipulating fluids in paper microfluidic devices. Then, we describe specific medical applications with discussion of three promising paper microfluidic devices. Finally, we close with a general discussion of challenges in the translation of paper microfluidic devices from the lab to the field.

Paper-based microfluidic systems have emerged as one of the most favorable technologies used in many potential applications such as point-of-care diagnostics, flexible electronics, energy storage, etc. From the past several decades, paper-based technology has readily accepted in the academic research lab and industries as well. The paper-based devices have changed the life of humankind. The distinguishing characteristics of paper substrate like low cost, biodegradability, biocompatibility, and ease of fabrication helped their adaptability in biosensing applications. This chapter gives a concise overview of the historical perspective of paper-based devices, classification of paper types, and their recent applications.

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.

With the rapid growth of the scholarly literature, finding relevant and influential articles is becoming increasingly important. Research shows that a scholar’s past works represent his latent interests. However, its effectiveness in recommending scholarly papers has not been well explored in the existing studies. In this paper, we propose an academic paper recommendation model, called CPM, which mainly mines researcher’s published works for improving scholarly navigation. Firstly, a scholar’s papers are divided into different interest points by clustering technologies. Then, scholar’s information needs are represented in terms of pattern equivalence classes. Finally, matching degree and preference degree are integrated to rank the candidate papers. Experimental results on real datasets demonstrate that CPM outperforms (5.6% in terms of NDCG@5 and 8.1% in terms of MRR) the baseline method.

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.

Infectious diseases are still a burden in the global healthcare sector. As per the WHO report, millions of people get affected annually at a worldwide level. An early diagnostic is a critical step in the prevention of their spread. The demand for point-of-care diagnostics of deadly diseases has increased significantly. From the past decades, several technologies have been developed for the diagnosis of these diseases. Among these, paper-based microfluidic devices present a unique opportunity for point-of-care diagnostic of various infectious diseases due to their simplicity, affordability, ease of fabrication, rapid detection, robustness, sensitivity and low limits of detection. As compared to laboratory-based diagnostics, the paper-based platform provides a cost-effective solution which plays a significant role in developing countries with limited resource settings. This chapter presents a brief overview of recent developments in paper microfluidic-based technology for the detection of various infectious diseases.

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.

Aiming at the defects of traditional paper currency sorting equipment, such as seizing-up paper currency, high manufacturing cost, large size, inconvenient maintenance and so on, a rotary structure type is proposed to design the paper currency sorting equipment. The proposed design method not only effectively simplifies the structural design but also reduce the transmission distance of paper currency, which can avoid the defects of traditional equipment. Meanwhile, multi-information fusion sensing technology is employed during the control system design, which makes the each module work smoothly and collaboratively. The experiment results indicate that the equipment based on the proposed design method presents good performances.

The basic concept of supply chain management (SCM) is to handle the flow of information, goods, and services in an efficient way to achieve better performance and minimize risk. In SCM, information or data is the key resource for collaboratory. However, it has been found that in many cases, there is lack of trust among collaborators. Blockchain technology is considered to facilitate security and privacy for several applications. This paper focuses on some case studies of SCM integration with blockchain and that how the technology allows transfer of transactions securely in a digital decentralized ledger in absence of any mediator.

The existing Kinect somatosensory games and other human–computer interaction are lack of natural interaction experience. We made research on a natural immersive closed-loop interaction method for human–robot “rock–paper–scissors” game. Kinect was used to make somatosensory dynamic recognition. Mechanical arm was used to make gestures to interact with human and to show the result of the game. System through the development of Modbus master and Modbus slave based on Arduino to control the mechanical arm, and used the multithread concurrent to achieve a better real-time experience. The PC was programmed to determine the outcome and record the result. The natural immersive closed-loop interaction method has the advantages of natural interaction, fast response, and strong expansibility.

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.

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.

In cyberspace one of the major security issue is phishing attacks. Phishing attacks are the most treacherous form of fraudulent activities of mobile and desktop. With the growing usage of smartphones, user find it convenient to keep their private data on mobile phones which allows phishers to exploit mobile devices for ingathering valuable data. The limitation of small screen size and low computational power makes mobile phones security vulnerable to more phishing attacks compared to desktop computers. Mostly, mobile users stay online so increase the chance of being phished. Moreover, to avoid phishing attacks malware detection and filtering system should be deployed and companies should educate their users about diversity of phishing attacks. Until now, no such effective anti-mobile phishing technique has been invented or adopted that can precisely distinguish authentic and phishing websites. However, many research has been done in this regard. In this paper, a detailed discussion is presented on several anti-mobile phishing models based on various methods for preventing users to evade phishing attacks. Furthermore, many issues and challenges faced while preventing users from phishing attacks are also elaborated. Lastly, the experimental result based on the evaluation of models are presented and using these facts suggested that which model can effectively and accurately detect malicious sites.

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.

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.

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.

Extracting thematic information from scientific papers has wide applications in information retrieval systems. Keywords give compact representation of a document. This paper proposes a document-centered approach for automatic keyword extraction and domain classification of research articles. Here, we induce a hybrid approach by adopting different methods in various phases of the system. Domain classification is important for researchers to identify the articles within their interest. The proposed system uses Rapid Automatic Keyword Extraction (RAKE) algorithm for automatic keyphrase extraction which gives best score of keywords. The classification process concerns semantic analysis which includes keyword score-matrix and cosine similarity. A comparative study of performance of RAKE algorithm which uses score-matrix against KEA based on term frequencies to extract relevant keyword was also performed.

In the wake of developments in science and technology, a variety of map products comes along. However, there are some inherent defects in the conventional paper map. For instance, it can’t be changed once published; contains limited information; and its real-time performance is also poor. In addition, static rendering mode, lack of interaction and dynamic effect also slow down the development of conventional paper map. The augmented reality makes it possible to present the conventional paper map vividly, and it has wide application prospects. In this paper, the status and progress of augmented expression for paper map are reviewed which may provide reference for future related research.

Over the last decade, the fabrication of analytical devices utilizing microfluidic structures and lab-on-a-chip platforms has shown breakthrough advancements, both for environmental and biological applications. The ASSURED criteria (affordable, sensitive, specific, user-friendly, robust, equipment-free, delivered), developed by the WHO for diagnostics devices, point towards the need of paper-based analytical devices (PAD) for diagnostics. On the other hand, cost-effective PADs owing the great advantage of affordable applicability in both resource-rich and -limited settings are recently employed for on-site environmental monitoring. In this book chapter, we will discuss about the brief history of paper analytical devices, fabrications, need, and its environmental and biomedical applications.

In this paper, we proposed digital doorsign system based on E-paper display for reducing cost and time in replacing doorsigns and effectively communicating with visitors. We also showed implementation results of the proposed system. Because the system could create final notice by only using standard forms which is designed by system manager, it is possible to keep uniformity of institution. By posting announcement for temporary event venues, users could communicate with visitors. We also designed the system which uses E-paper display and BLE for low power consumption in mobile device, Digital Doorsign.

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.