Mining the Human Networks and Identification of Group Activities Using the Crime Scraping Engine

This article proposes a model (Crime Scraping Engine) to identify human networks and group activities using the crime victim’s data. These activities can be related to crime networks or terror networks. The primary focus of this work is to identify criminal networks along with gang activities. Organized crimes are generally repetitive in nature, which means if someone can analyze the criminal activities prevailing in the last few years in a specific area, then upcoming criminal activities along with the suspects can be identified. This thought process is the backbone of the model. The proposed model shows that how communities can be created using the crime victim’s data and then those communities are further linked to each other using linked persons. These linked persons are capable to establish the relationship between different groups. Also, a rating process can be evolved among the groups and individuals to …

X-ResFormer: A Model to Detect Infestation of Pest and Diseases on Crops

Of late, convolutional neural networks have shown significant performance improvement over the traditional machine learning and dominate the classification tasks in the field of computer vision. Considering the success of CNNs in deep learning, and the utilization of self-attention mechanism, the vision transformers can better model the global contextual information as compared to CNNs, have seen rapid interest in vision community. In this paper, an attention-aided CNN and transformer model is proposed to identify pest and disease infestations of 14 crop species with 26 diseases (or absence thereof) on a public dataset of 54,309 images are collected under the controlled conditions. The proposed model achieves an accuracy of 94.24% in training and a test accuracy of 94.74% with a standard deviation of 3.66 on a fivefold cross-validation, which provides the better result as compared to the convolutional block attention module …

Deep Learning-Based Approach to Detect and Classify Signs of Crop Leaf Diseases and Pest Damage

Leaf constitutes an important part of a plant as it gives prior information about its health. Timely and proper diagnosis of different pests, fungal and viral diseases of plants is indispensable for successful crop production. Sustainability in crop production is of utmost importance to meet the growing demand for food and support the economic growth of a country. In recent years, deep learning, a far superior method to traditional methods has paved the way in the field of digital image processing. Deep learning has the ability to process large numbers of features making it very powerful when dealing with unstructured data. It has achieved impressive results in the field of image classification. In this study, deep learning-based approach is adopted to identify and classify nine classes of tomato leaf diseases and mite infection along with healthy leaves and two types each of rice leaf diseases and pest infestation along with …

DFT with Universal Test Set for All Missing Gate Faults in Reversible Circuits

The design of reversible logic circuits has received considerable attention in recent times for their potential use in implementing quantum computers. A fault model, namely, the Missing-Gate Fault (MGF) model, has been found to be more suitable for modeling defects in quantum circuits as compared to the classical fault models used for testing conventional CMOS circuits. In this paper, we propose two design-for-testability techniques with universal test set to detect MGFs in a reversible circuit. In these techniques, an reversible circuit implemented with -CNOT gates is transformed to a testable design with addition of two extra inputs lines and some additional gates, which uses a universal test set of size to detect the MGFs. The proposed DFT techniques are compared with earlier works and it has been shown that these techniques outperform the earlier techniques with respect to quantum cost overhead …

A Novel Digital Watermarking Technique for Video Copyright Protection: An Advanced Study

Protecting digital data is becoming increasingly important due to the rapid growth of the internet and technology.A novel watermarking technique is proposed in this paper, which involves embedding both visible and invisible watermarks in a video.Since digital data can be easily replicated without loss of quality, it is important to protect the data. Digital watermarking is a technique for embedding additional information into the host signal to ensure multimedia data security and privacy.Since both watermarks are present in the video frames, it is more resistant to attacks.The watermarking scheme listed here deals with watermark embedding and extraction. The invisible watermark is embedded using the Discrete Wavelet Transform (DWT), and the Peak Signal to Noise Ratio (PSNR) is measured to assess the method's efficacy.The visible watermark is embedded using compound mapping, which improves the video's …

SeqPIP-2020: Sequence based Protein Interaction Prediction Contest

Computational protein-protein interaction (PPI) prediction techniques can contribute greatly in reducing time, cost and false-positive interactions compared to experimental approaches. Sequence is one of the key and primary information of proteins that plays a crucial role in PPI prediction. Several machine learning approaches have been applied to exploit the characteristics of PPI datasets. However, these datasets greatly influence the performance of predicting models. So, care should be taken on both dataset curation as well as design of predictive models. Here, we summarize the results of the SeqPIP competition whose objective was to develop comprehensive PPI predictive models from sequence information with high-quality bias-free interaction datasets. A training set of 2000 positive and 2000 negative interactions with sequences was given to each contestant. The methods were evaluated with three independent high-quality interaction test datasets.

A Statistical Model to Determine the Behavior Adoption in Different Timestamps on Online Social Network

In this article, a statistical model has been proposed to determine the behavior adoption among the users in different timestamps on online social networks by using vector space models and term frequency–inverse document frequency techniques. The concepts of herd behavior and collective behavior have been used successfully in the proposed model. The result has been generated after analyzing the collected dataset. The result analysis shows the diffusion of information among the participants from an initial timestamp to later timestamps.

Influential Nodes Identification Based on Activity Behaviors and Network Structure With Personality Analysis in Egocentric Online Social Networks

Online social network (OSN) platforms have become a place where the users share their details through various activities. Nowadays, OSNs have major impact not only on the users' lives but also on business and society. Organizations use OSN platforms to reach the target audiences and influential nodes may perform a major role to reach the target audience effectively and efficiently. In this article, a model has been proposed to identify influential nodes for a user in egocentric online social networks by considering the activity behaviors of the user and its network members along with network structure of the user. Finally, the personalities of both the user and the influencers has been analyzed using sentiment analysis and hashtag terms analysis. This article has derived a novel and efficient influence measurement model to evaluate an influence factor of each influential interacted node in the user's network for any …

Test Generation from Boolean Generator for Detection of Missing Gate Faults (MGF) in Reversible Circuit Using Boolean Difference Method

With the change in commuting paradigms, the demands in industries have changed accordingly. Consequently, efficient design models and related technologies have evolved as prime driving factors to this cause. In such a scenario, reversible logic design has come out as a prominent future technology which not only provides faster computing platform but also promises energy efficient designs. But it is also very evident that not only the efficient designs play a pivotal role in reliable design models but incorporation of proper testing mechanisms is very important. Further to contribute to this cause, here, in this work we introduce an efficient test vector generation method to detect all type of missing gate faults in reversible circuits. In our proposed scheme, we have explored the Boolean difference property of logic functions for designing a Boolean generator from which fault specific test vectors are computed for different …

Boolean Difference Technique for Detecting All Missing Gate and Stuck-at Faults in Reversible Circuits

Quantum reversible circuit is a new emerging technology attracting the researchers. A reversible circuit is composed of reversible gates. One example of reversible gate is Toffoli gate. A Toffoli gate (also known as -CNOT) has two components — the control and the target. Initially, stuck-at fault and other fault models were used for modeling defects in quantum reversible circuits. Later, a new fault model known as missing gate fault model was introduced, which is more effective in capturing the errors in quantum reversible circuit. Boolean Difference is already a known technique to detect stuck-at faults in conventional CMOS circuit. In this paper, Boolean Difference method is applied to derive the test set for detecting each stuck-at fault and missing gate fault in a reversible circuit. Then an optimization algorithm is used to derive an optimal test set, which will detect all possible faults in a circuit. The method is valid also …

Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves

Crop diseases in India are a major concern in the field of agriculture today. Efforts are being made to tackle this problem as agriculture is an important aspect of our economy. Due to diseases, a major part of crops produced is lost every year. This is due to lack of expertise among the farmers, failing to detect and diagnose the disease correctly in time or due to commencement of control measure in late. To prevent crop loss, image processing for diagnosis of disease symptom using soft computing techniques is a need of modern day agriculture. In this study, initial focus was paid towards automatic identification of leaves between okra and bitter gourd from the images. To diagnose disease symptom from leaf images, forty-three morphological features of leaves of both the crops were chosen. The Pearson Correlation Coefficient technique was exercised to set key identifying features depending on a particular type …

Survey of influential nodes identification in online social networks

The increasing popularity of Online Social Networks (OSNs), its complex and rapidly growing structure, its potential impact on business, politics, healthcare and society has led to a great deal of interest in the development of efficient influential nodes identification algorithms. Different kind of business organizations are trying to use the social networking sites to reach the target audience quickly and efficiently in terms of cost and time. The influential nodes can play an important role in it. In this paper the features of social network, models of social networks and different models or approaches used for identifying influential nodes in online social networks have been studied.

REVIEW: DETECTION & DIAGNOSIS OF PLANT LEAF DISEASE USING INTEGRATED IMAGE PROCESSING APPROACH

Farmers find it difficult to keep an eye on each and every plant in the cultivation area to detect manifestation of any infection. Many an image processing approaches has been developed in recent years to serve the purpose of a watch-dog, ie detecting the growth of a disease at an early stage, determining the type of the disease the leaf is infected with and suggesting necessary actions. This paper will go through most of the research work that has been done in the field of Image Processing and Artificial Neural Network for the purpose of detection and diagnosis of disease in a plant leaf from its image.

Derivation of test set for detecting multiple missing-gate faults in reversible circuits

Logic synthesis of reversible circuits has become an important problem because of its relevance to the emerging area of quantum computation. Special types of quantum gates such as k-CNOT may be deployed to implement a reversible circuit. Although the classical stuck-at fault model is widely used for modeling defects in conventional CMOS circuits, new approaches, namely single missing-gate fault (SMGF), repeated-gate fault (RGF), partial missing-gate fault (PMGF), and multiple missing-gate fault (MMGF), have been found to be more befitting for modeling defects in quantum k-CNOT gates. This article presents an algorithm to derive a test set (TS) for detection of multiple missing-gate faults in a reversible circuit implemented with k-CNOT gates. It is shown that TS is sufficient to detect all single missing-gate faults (SMGFs) and all detectable repeated gate faults (RGFs). Experimental results on test set for some …

Measurement of Displacement and Velocity of a Moving Object from Real Time Video

This article focuses on an efficient algorithm for measuring object displacement and velocity from real time video. The proposed technique for object identification and tracking is based on background subtraction with optimized threshold binarization. Mapping techniques have been developed to relate image with real world. The algorithm is also capable of working with a bad lighting conditions using histogram equalization approach. Further, the real scenarios like presence of noise, shadow, and multiple moving object environments have been taken under consideration for developing the algorithm.

Automatic Brain Tumor Detection and Isolation of Tumor Cells from MRI Images

Automatic Brain Tumor Detection refers to the problem of delineating tumorous tissues from MRI images for the purpose of medical diagnosis and surgical planning. The process uses tumor characteristics in images, such as sizes, shapes, locations and intensities for the isolation of the tumor which depends on manual tracing by experts. This paper proposes automatic brain tumor detection and isolation of tumor cells from MRI images using a genetic algorithm (GA) based clustering method, intensity based asymmetric map and region growing technique.

Fault diagnosis in reversible circuits under missing-gate fault model

This article presents a novel technique for fault detection as well as fault location in a reversible combinational circuit under the missing gate fault model. It is shown that in an (n × n) reversible circuit implemented with k-CNOT gates, addition of only one extra control line along with duplication each k-CNOT gate, yields an easily testable design, which admits a universal test set (UTS) of size (n + 1) that detects all single missing-gate faults (SMGFs), repeated-gate faults (RGFs), and partial missing-gate faults (PMGFs) in the circuit. Furthermore, storage of only one vector (seed) of the UTS is required; the rest can be generated by n successive cyclic bit-shifts from the seed. For fault location under the SMGF model, a technique for identifying the faulty gate is also presented that needs application of a single test vector, provided the circuit is augmented with some additional observable outputs.

On-Line Image Compression Based on Pipelined Architecture

Successes of multimedia applications demand on-line transmission of image data. This in turn demands on-line compression/decompression of image data files. This paper reports a pipelined architecture that can support on-line applications of image data. Spatial redundancy of an image data file are detected and removed with a simple and elegant scheme that can be easily implemented on a pipelined hardware. The scheme provides the user with the facility of trading off the image quality with the compression ratio. The basic theory of byte error correcting code (ECC) is employed in this work to compress a pixel row with reference to its adjacent row. A simple scheme is developed to encode pixel rows of a monochrome image.

Detection of Bridging Faults in Reversible Circuits

An Efficient Image Segmentation Algorithm using Dynamic GA based Clustering

A Study on Herd Behavior Using Sentiment Analysis in Online Social Network

Social media platforms are thriving nowadays, so a huge volume of data is produced. As it includes brief and clear statements, millions of people post their thoughts on microblogging sites every day. This paper represents and analyze the capacity of diverse strategies to volumetric, delicate, and social networks to predict critical opinions from online social networking sites. In the exploration of certain searching for relevant, the thoughts of people play a crucial role. Social media becomes a good outlet since the last decades to share the opinions globally. Sentiment analysis as well as opinion mining is a tool that is used to extract the opinions or thoughts of the common public. An occurrence in one place, be it economic, political, or social, may trigger large-scale chain public reaction across many other sites in an increasingly interconnected world. This study demonstrates the evaluation of sentiment analysis …

Application of Social Networks and Data Mining on Crime Victims

Technological advancement in computational areas has already entered to every fields of science, sociology, health, business, and in many others. Now people are connected across the world using Internet and more precisely it can be said that the communication is done freely through online social networks. From that point of view, researchers are trying to analyze the social networks for the betterment of different domains or trying to create some useful applications through social networks. Now aim is to use this ground to help the people by using the information of crime sufferers to reduce the criminal activities of future. It will benefit the people to get some ideas about the different criminal activities and who are the victims and how they have been victimized. Here, the open-source, GUI-based machine learning tool, WEKA, has been used to analyze the collected data. And last but not the least this process …

Swift Sort: A New Divide and Conquer Approach Based Sorting Algorithm

Sorting implies the task of presenting a specific type of data in a specific order. These tasks are getting accomplished by different algorithms proposed by researchers. Researchers are trying to achieve this task in minimal space and time complexity with improved stability, correctness, finiteness and effectiveness. Sorting is used in wide range of fields namely, in Operating systems, Data Base Management systems, in searching and in various other data science related areas. In this paper, a divide and conquer approach-based algorithm is proposed to sort the data in a specific order using min–max searching. The time complexity of the proposed Swift Sort algorithm is O(nlogn) and O(n2) in the average and worst cases, respectively. Moreover, time complexity of the proposed algorithm is comparable to Quick Sort, Merge Sort, Heap Sort and TimSort but at the same time Randomised Quick Sort, Merge Sort and Heap

Recommender system in healthcare: an overview

Today information technologies have led to a vast number of innovations and developments in almost every field of advancement. In this context, recommender systems (RS) have been setting milestones in the service industry. If we look at the web-based services, it can be said that RS has contributed majorly to increase the reachability of products and to provide a sea of options for potential customers. This RS can also be used as a tool to support decision-making by the decision-makers. With this advancement of RS in the service industry, healthcare systems also do not lag. Health RS (HRS) are becoming an important platform for providing healthcare services, which would cut down the hectic schedule of visiting the doctors and waiting for hours to get checked. In the healthcare industry, RS already play a very significant role in terms of supporting decision-making process about any individual’s health. Keeping …

Detection of Diseases in Potato Leaves Using Transfer Learning

The problem of food shortage has grown rampant in the recent times in developing countries. In a tropical country like India, potato is one of the major staple food that is eaten throughout the year. Recently the production of potato is falling short due to various diseases like Early Blight and Late Blight which cause a huge loss of the cropped plants. This also leads to a major loss in the national economy as well. The emergence of deep learning has affected many fields of machine learning research. Since it is not required in deep learning to develop hand-crafted features, it has found widespread adoption in the scientific community. To tackle the need for a huge amount of data for deep learning, another heavily implemented technique is used, namely, transfer learning, to make the training process faster and more accurate with a relatively small dataset at hand. The performance of the model is demonstrated …

Recommender system in healthcare: an overview

Today information technologies have led to a vast number of innovations and developments in almost every field of advancement. In this context, recommender systems (RS) have been setting milestones in the service industry. If we look at the web-based services, it can be said that RS has contributed majorly to increase the reachability of products and to provide a sea of options for potential customers. This RS can also be used as a tool to support decision-making by the decision-makers. With this advancement of RS in the service industry, healthcare systems also do not lag. Health RS (HRS) are becoming an important platform for providing healthcare services, which would cut down the hectic schedule of visiting the doctors and waiting for hours to get checked. In the healthcare industry, RS already play a very significant role in terms of supporting decision-making process about any individual’s health.

Reversible Logic Circuits Synthesis using Genetic Algorithm and Particle Swarm Optimization

DIGITAL WATERMARKING- A Review

Detection and Measurement of Leaf Rust Disease in Wheat

Flight Arrival Delay Prediction using Gradient Boosting Classifier

The basic objective of the proposed work is to analyse arrival delay of the flights using data mining and four supervised machine learning algorithms: random forest, Support Vector Machine (SVM), Gradient Boosting Classifier (GBC) and k-nearest neighbour algorithm, and compare their performances to obtain the best performing classifier. To train each predictive model, data has been collected from BTS, United States Department of Transportation. The data included all the flights operated by American Airlines, connecting the top five busiest airports of United States, located in Atlanta, Los Angeles, Chicago, Dallas/Fort Worth, and New York, in the years 2015 and 2016. Aforesaid supervised machine learning algorithms were evaluated to predict the arrival delay of individual scheduled flights. All the algorithms were used to build the predictive models and compared to each other to accurately find out …

Gradation of Diabetic Retinopathy using KNN Classifier by Morphological Segmentation of Retinal Vessels

The extraction of blood vessels by morphological segmentation and the detection of the severity of diabetic retinopathy are proposed in this paper. The proposed algorithm extracts the finest vessels in the retina within a remarkably reduced computational time. The extracted blood vessel features being fed to KNN classifier determine the stage of Diabetic Retinopathy. The performance analysis is carried out which comes out to be of 94% along with the sensitivity (81.45%), specificity (96.25%), and accuracy (95.3%) defines the efficiency of the proposed system.

A Novel Reversible Synthesis of Array Multiplier

Quantum computation has seen immense progress which has popularised logic synthesis with the help of reversible circuits. A reversible circuit is implemented with multiple special types of quantum gates, known as k-CNOT gates. Ion trapping or nuclear magnetic resonance are newer technologies required to emulate quantum gates. This paper presents a reversible synthesis of array multipliers based on Booths Algorithm implemented with reversible k-CNOT gates. Our work has been simulated using the online quantum simulator davyw and its features have been compared with existing reversible array multipliers.

Synthesis of Reversible Array Divider Circuit

Progress made in quantum computation has resulted in logic synthesis with reversible circuits receiving considerable interest. This paper presents a synthesis of reversible array divider circuit based on non-restoring division algorithm implemented with k-CNOT gates. The execution time of such an array divider is low. Our aim is to propose a reversible array divider circuit which is efficient as regards to the number of garbage outputs and gates.

A Time Efficient Leaf Rust Disease Detection Technique of Wheat Images based on Ratio of Diseased Leaf Area (RDLA) and Rough Fuzzy C-Means

In agricultural sector diagnosis of crop disease is an important issue, since it has a marked influence on the production of agriculture of a nation. It is very essential to diagnose disease in an early stage to control them and to reduce crop losses. This paper presents a time efficient proposed technique to detect the presence of leaf rust disease in wheat leaf using image processing, rough set and fuzzy c-means. The proposed technique is experimented on one hundred standard diseased and non-diseased wheat leaf images and achieved 95 and 94 % success rate respectively depending on most three dominated features and single most dominated feature, Ratio of Infected Leaf Area (RILA). The three most dominated features and single most dominated feature are selected out of ten features by the Pearson correlation coefficient. A significant point of the proposed method is that all the features are converted …

Fuzzy C means based feature extraction and classification of diabetic retinopathy using support vector machines

Diabetic Retinopathy is a vascular disorder which can result in complete blindness. So, detection and treatment of it can prevent vision loss. This paper deals with feature extraction of retina using Fuzzy C Means and morphological methods and classification of whether the patient is suffering from Diabetic Retinopathy or not using Support Vector Machines (SVM). An automated detection of Diabetic Retinopathy is thus possible through this classification with efficiency of 97.6% which defines the efficacy of the proposed system.

Generalized construction of quantum multiplexers and de-multiplexers using a proposed novel algorithm based on universal Fredkin gate

In recent years, Reversible Logic is becoming a prominent technology due to its power reduction capability in circuit designing, thus having applications in quantum computing. The reversible circuit is implemented using reversible quantum gates such as CNOT gate, Fredkin gate, Toffoli gate etc. Among the various combinational circuits, quantum multiplexer and de-multiplexer circuits are two of the most important circuits. In this paper, we have proposed generalized algorithms for the construction of Quantum multiplexers (QMUX) and demultiplexers (QDe-MUX) using universal Fredkin gate for any given number of select lines. Using these novel algorithms, any valid size of Quantum multiplexers (QMUX) and de-multiplexers (QDe-MUX) circuit can be generated.

Detection and Classification Technique of Yellow Vein Mosaic Virus Disease in Okra Leaf Images using Leaf Vein Extraction and Naive Bayesian Classifier

Okra (Abelmoschus esculentus (L) Moench), is widely grown all over tropical, subtropical and warm temperature regions of the world. It is a popular crop in India due to its ease of cultivation and adaptability to varying moisture conditions. But the crop is prone to damage by various diseases caused by various insects, fungi, nematodes and viruses. The most common disease of okra is Yellow Vein Mosaic Virus (YVMV), spread by white fly (Bemisiatabaci). This paper presents an efficient technique to detect and classify the presence of YVMV disease in okra leaf with the joint use of image processing, K-means and Naive Bayesian classifier. The proposed technique is experimented on 79 standard diseased and non-diseased okra leaf images. The input leaf images are of four classes, namely Highly Susceptible (HS), Moderately Susceptible (MS), Tolerable (T) and Resistive (R), depending upon the severity of the …

Boolean Difference Technique for Detecting All Missing Gate Faults in Reversible Circuits

Quantum reversible circuit is a new emerging technology attracting the researchers. A reversible circuit is composed of reversible gates. One example of reversible gate is Toffoli gate. A Toffoli gate (also known as -CNOT) has two components — the control and the target. Initially, stuck-at fault and other fault models were used for modeling defects in quantum reversible circuits. Later, a new fault model known as missing gate fault model was introduced, which is more effective in capturing the errors in quantum reversible circuit. Boolean Difference is already a known technique to detect stuck-at faults in conventional CMOS circuit. In this paper, Boolean Difference method is applied to derive the test set for detecting each stuck-at fault and missing gate fault in a reversible circuit. Then an optimization algorithm is used to derive an optimal test set, which will detect all possible faults in a circuit. The method is valid also …

A Time Efficient Secret Image Sharing Scheme for Group Authentication System without Pixel Expansions

Information security is of vital importance in today’s internet world. Classified data such as military and government information are constantly transmitted over the internet. Among this, top secret data are images such as maps and surveillance footage. It is thus necessary to secure such visual data against unauthorized access. Visual secret sharing is the process of distributing “shares” of a secret image among a group of individuals. Only when all the shares have been combined, can the original secret image be obtained. In this paper, we have proposed a visual secret sharing scheme, using a simple division algorithm, for group authentication systems. This method has the advantage of being both time efficient and devoid of any pixel expansion. It can be applied to both grayscale as well as colour images and, as illustrated further ahead, ensures complete secrecy and regeneration of the original image from …

An Effective Stock Price Prediction Technique Using Hybrid Adaptive Neuro Fuzzy Inference System Based on Grid Partitioning

Prediction of forthcoming stock price is an important area of research. A large number of data is used by the system to predict the possible upcoming events in future. The stock prediction work is done primarily for overnight as it gets more volatile in a longer span. However in this work an effective effort is made to extend the duration of prediction to 14 days. A fuzzy logic approach based on grid partition is adopted in the paper to deal with the uncertainty factors while predicting the stock price of any company. The premise and consequent parameters of the learning rules are optimized in an adaptive fashion using a hybrid neural learning mechanism. This Adaptive Neuro-Fuzzy Inference System (ANFIS) using grid partition is undertaken to deal with the problem of stock price prediction, which leads to an accuracy of 94-95%.

An Integrated Digital Image Analysis System for Detection, Recognition and Diagnosis of Disease in Wheat Leaves

Wheat leaves need to be scouted routinely for early detection and recognition of rust diseases. This facilitates timely management decisions. In this paper, an integrated image processing and analysis system has been developed to automate the inspection of these leaves and detection of any disease present in them. Disease features of wheat leaves have been extracted using Fuzzy c-means Clustering algorithm and disease detection, recognition of its type and identification algorithm has been developed based on artificial neural network (ANN). Through the use of ANN and more specifically multilayer perceptrons, detection of the presence of disease in wheat leaves have been successful in 97% of the cases, after analysis of about 300 test images of wheat leaves. Also, identification of type of disease, if present, in wheat leaf has been successful in 85% of the cases.

Automatic Tortuosity Detection and Measurement of Retinal Blood Vessel Network

Increased dilation and tortuosity of the retinal blood vessels causes the infant disease, retinopathy of prematurity (ROP). Automatic tortuosity evaluation is a very useful technique to prevent childhood blindness. It helps an ophthalmologist in the ROP screening. This work describes a method for automatically detection of a retinal image into low, medium or highly tortuous. The proposed method first extracts a skeleton retinal image from the original retinal image to get the overall structure of all the terminal and branching nodes of the blood vessel based on morphological operations. Then, it separates each branch and rotates it so that partitioning process is easier which follows a recursive way. Finally, from the partitioned vessel segments, the tortuosity is calculated and the tortuous symptom of the image is detected. The results have been compared with the eye specialist’s analysis on twenty five images which …

Detection of Downy Mildew Disease present in the Grape Leaves based on Fuzzy Set theory

Agriculture has a significant role in economy of the most of the developing countries. A significant amount of crops are damaged in every year due to fungi, fungus, bacteria, Phytoplasmas, bad weather etc. Grapes are one of the most widely grown fruit crops in the world with significant plantings in India. Grapes are used in the production of wine, brandy, or non-fermented drinks and are eaten fresh or dried as raisins. Sometimes grape plants are affected by downy mildew, a serious fungal disease. Therefore, farmers try to detect the stage of the disease in plant at an early stage so that they can take necessary steps in order to prevent the disease from spreading to others parts of the fields. This article presents a novel technique for detection of downy mildew disease present in the grape leaves based on fuzzy importance factor. The proposed technique uses some digital image processing operations and fuzzy …

Generator for Test Set Construction of SMGF in Reversible Circuit by Boolean difference method

Reversible logic synthesis has received considerable attention in the light of advances recently made in quantum computation. Implementation of a reversible circuit is envisaged by deploying several special types of quantum gates, such as k-CNOT. Although the classical stuck-at fault model is widely used for testing conventional CMOS circuits, new fault models, namely single missing-gate fault (SMGF), repeated-gate fault (RGF), partial missing-gate fault (PMGF), and multiple missing-gate faults (MMGF), are likely to be more suitable for modeling defects in quantum k-CNOT gates. This work proposes an algorithm for deriving the test set for the detection of all single missing gate faults in a reversible circuit implemented with k-CNOT gates. Instead of deriving test set directly for the detection of missing gate faults, a Boolean generator is developed by Boolean difference method to derive the test set and to detect all …

Noise Elimination from Web Page based on Regular Expressions for Web Content Mining

Web content mining is used for discovering useful knowledge or information from the web page. So, noisy data in web document significantly affect the performance of web content mining. In this paper, a noise elimination method has been proposedbased on regular expression followed by Site Style Tree (SST). The proposed technique consists of two phases. In the first phase, filtering method based on regular expression is used on web pages to remove noisy HTML tags The filtered document then undergoes to second phase where an entropy based measured is used for removing further noise. The page size is reduced considerably by eliminate a number of lines of code preceded by some predefined noisy HTML tags. The con-sized web document is then used to form Document Object Model (DOM) tree and consequently the Site Style Tree is formed by crawling the pages from the same URL path as of …

Users Activity Measure in Online Social Networks using Association Rule Mining

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Application of Fuzzy C-Means Clustering Method to Classify Wheat Leaf Images based on the presence of rust disease

This paper presents a novel and efficient way to detect the presence and identification of disease in wheat leaf from its image. The system applies FCM on data-points consisting of selected features of a set of Wheat Leaf images. In the first step, number of clusters is fixed to 2, in order to divide the input into sets of diseased and undiseased leaf images. The diseased leaf set is further classified into 4 sets corresponding to possibility of occurrence of known 4 types of disease, by applying FCM on this set with number of clusters fixed to 4. We have proposed an efficient method for selection of feature set based on inter and intra-class variance. Although testing has been done only on wheat leaf images, this method can also be applied on other leaf images through careful selection of the feature set.

Structural Analysis and Regular Expressions based Noise Elimination from Web Pages for Web Content Mining

Commercial websites usually contain noisy information blocks along with main content. Noisy information degrades the performance of web content mining. Web content mining is used for discovering useful knowledge or information from the web page. In this paper, we propose noise elimination method that uses tag based filtering followed by structural analysis of the web page. The proposed tag based filtering method is implemented by regular expression. Firstly, the filtering method is used to remove several predefined HTML tags present in the web page. Then the concise web page is taken for structural analysis to remove remaining noise. Most of the time Noisy blocks share same contents and layouts or presentation styles in every web page of a website. In structural analysis phase, we compare the HTML contents of the crawled web pages from a website to capture common blocks having same contents and …

ESOP-based Synthesis of Reversible Circuit Using Improved Cube list

This work presents an ESOP-based synthesis technique for reversible logic circuit using improved cube list. Input specification for this method is a .spec file and it generates improved ESOP cubes which build the Toffoli network. The road map for generating this improved ESOP cubes is discussed in the paper. Each cube is independent in nature and can be shuffled according to needs. Sharing of cubes between multiple outputs has not been considered here. Our experiments demonstrate that the proposed approach is more effective than the previous on shared-cube synthesis methods. The algorithm is capable of generating reversible circuits for functions up to sixteen input variables within reasonable time.

An Efficient Secret Image Sharing Scheme using an Effectual Position Exchange Technique

In image secret sharing scheme the specific image is distributed among a group of n participants. Each gets a shared image that has no visual relationship with the original one. The entire set or their subsets are used to reconstruct the secret image. The sharing schemes are useful for group authentication. In this paper, we proposed a novel approach to image secret sharing scheme where a secret image is transformed into an entirely new image by repositioning intensity values of each pixel using an effective position exchange technique. The transformed image undergoes a relatively less overhead negative transformation to make it more unpredictable. Finally n random numbers are generated for creating n shares in a way that their summation yields ‘1’ and they lay in some predefined interval. Each stage provides an additional level of security. The proposed method is simple and efficient and also …

On Designing Testable Reversible Circuits Using Gate Duplication

Design of reversible logic circuits has received considerable attention in recent times for their potential use in implementing quantum computers. In this paper, it is shown that in an (n×n) reversible circuit implemented with k-CNOT gates, addition of only two extra inputs along with at most 5 k-CNOT gates per gate can yield an easily testable design. The modified design admits a universal test set of size (n + 2) that detects all SMGFs, PMGFs, and detectable RGFs in the circuit.

Implementation of AES algorithm in UART module for secured data transfer

This work proposes the application of Advanced Encryption Standard (AES) algorithm in Universal Asynchronous Receiver Transmitter (UART) module for secure transfer of data. The proposed architecture implements AES-128 algorithm that encrypts the data before transmission through UART transmitter and decrypts after receiving the data at UART receiver module. In this work, we present the AES-128 encryption and decryption circuit using iterative architecture. The design has a clock generator circuit which provides the different clock frequencies to different sub modules. The complete design is described in Verilog Hardware Description Language (HDL) and is functionally verified using Xilinx ISE 9.1i software. It takes 47.2msec to transmit 128 bit encrypted data and 36.7msec to receive decrypted data on a Xilinx xc2vp70-7ff517 device. All the blocks of the proposed architecture are designed using FPGA …

An optimized S-box for advanced encryption standard (AES) design

This work presents an optimized Substitution Box (S-Box) for Advanced Encryption Standard (AES) design. The S-Box is one of the most important components of AES. During SubByte transformation, the eight bit input is substituted by eight bit output using the S-Box. S-Box is constructed by composing two transformation - multiplicative inverse in Galois Field GF(2 8 ) followed by an affine transformation. The S-Box is the most time and power consuming component of AES Algorithm. In this paper, we have modified the affine transformation and tried to reduce the time complexity of AES. The time consumption for AES S-Box is decreased by the proposed affine transformation.

Enhancing the Security of Digital Video Watermarking using Watermark Encryption

Video Watermarking is a very popular and effective way of copyright protection of videos. But in some cases simple watermarking is not enough because security has become a major concern with the rapid development of technology and internet access. Thus, to enhance the security of the embedded watermark, in this paper the watermark is encrypted using a key known only to the author. The encrypted watermark is then embedded into the frames of the video. According to necessity, the encrypted watermark can be extracted, and then decrypted using the author's key, to regain the original information. This will confirm the authenticity of the video as indicated by the watermark as well as increase the security of the information. The strength of this scheme lies in the fact that without the key, it is very difficult to decrypt the watermark. If the size of the watermark be n X m, then an attacker will have to consider m …

A Design for Testability Technique for Quantum Reversible Circuits

Design of reversible logic circuits has received considerable attention in recent times for their potential use in implementing quantum computers. In this paper, it is shown that in an (n×n) reversible circuit implemented with k-CNOT gates, addition of only two extra inputs along with at most 5 k-CNOT gates per gate can yield an easily testable design. The modified design admits a universal test set of size (n + 2) that detects all SMGFs, PMGFs, and detectable RGFs in the circuit.

Digital Video Watermarking Using Motion Detection and Singular Value Decomposition

Various digital watermarking techniques have been proposed in recent years for the purpose of protection of intellectual property rights of the creators of multimedia data, such as digital video. In order to achieve copyright protection of digital video we have adopted an imperceptible video watermarking approach based on Motion Detection and Singular Value Decomposition. Motion detection using temporal difference is used to identify the motion regions within a video frame where a watermark can be effectively embedded to ensure imperceptibility of the watermark. The unique properties of the singular values of an image, in this case the individual motion regions within the video frames, are utilized to enhance the robustness of watermarking and make it non-invertible. The resultant watermarked video is then tested for its robustness against attacks for watermark degradation and removal.

Derivation of Automatic Test Set for Detection of Missing Gate Faults in Reversible Circuits

This article presents a novel technique for the generation of test set in a reversible quantum circuit. The algorithms are developed to derive the automatic test set (ATS) for the detection of all partial missing-gate faults, all single missing gate faults and multiple missing gate faults in an (n x n) reversible circuit implemented with k-CNOT gates. Experimental results on some benchmark circuits are also reported.

Derivation of Optimal Test Set for Detection Multiple Missing-Gate Faults in Reversible Circuits

This article presents a novel technique for the generation of test set in a reversible quantum circuit. The algorithms are developed to derive the automatic test set (ATS) for the detection of all partial missing-gate faults, all single missing gate faults and multiple missing gate faults in an (n x n) reversible circuit implemented with k-CNOT gates. Experimental results on some benchmark circuits are also reported.

Derivation of optimal test set for detection of multiple missing-gate faults in reversible circuits

Logic synthesis of reversible circuits has received considerable attention in the light of advances recently made in quantum computation. Implementation of a reversible circuit is envisaged by deploying several special types of quantum gates, such as k-CNOT. Although the classical stuck-at fault model is widely used for testing conventional CMOS circuits, new fault models, namely single missing-gate fault (SMGF), repeated-gate fault (RGF), partial missing-gate fault (PMGF), and multiple missing-gate fault (MMGF), have been found to be more suitable for modeling defects in quantum k-CNOT gates. This article presents an efficient algorithm to derive an optimal test set (OTS) for detection of multiple missing-gate faults in a reversible circuit implemented with k-CNOT gates. It is shown that the OTS is sufficient to detect all single missing-gate faults (SMGFs) and all detectable repeated gate faults (RGFs). Experimental …

Optimal Reversible Logic Circuit Synthesis Based on a Hybrid DFS-BFS Technique

Logic synthesis with reversible circuits has received considerable interest in the light of advances recently made in quantum computation. In this paper, we propose an improved technique for synthesizing reversible circuits based on a combined depth-first search (DFS) and breadth-first search (BFS) algorithm. A method based on DFS alone may often take a long time to converge, whereas, a BFS based method requires a large amount of memory for designing a circuit of moderate complexity. To strike a balance between these two approaches, we propose a hybrid DFS-BFS based synthesis algorithm that reduces the computation time compared to the DFS method and requires less space compared to the BFS method, while optimizing the cost of the circuit. Synthesis results on several reversible benchmark circuits have been reported.

Synthesis of Online Testable Reversible Circuit

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A Constructive Algorithm for Synthesis of Reversible Logic Circuits

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On-Line Colour Image Compression Based on Pipelined Architecture

This paper reports a pipelined architecture that can support on-line compression/decompression of image data. Spatial and spectral redundancy of an image data file is detected and removed with a simple and elegant scheme that can be easily implemented on a pipelined hardware. The scheme provides the user with the ability of trading off the image quality with the compression ratio. The basic theory of byte error correcting code (ECC) is employed in this work to compress a pixel row with reference to its adjacent row. A simple scheme is developed to encode pixel rows of a color image.

On the detection of missing-gate faults in reversible circuits by a universal test set

Logic synthesis with reversible circuits has received considerable interest in the light of advances recently made in quantum computation. Implementation of a reversible circuit is envisaged by deploying several special types of quantum gates, such as k-CNOT. Newer technologies like ion trapping or nuclear magnetic resonance are required to emulate quantum gates. Although the classical stuck-at fault model is widely used for testing conventional CMOS circuits, new fault models, namely, single missing-gate fault (SMGF), repeated-gate fault (RGF), partial missing-gate fault (PMGF), and multiple missing-gate fault (MMGF), have been found to be more suitable for modeling defects in quantum k-CNOT gates. In this paper, it is shown that in an (n times n) reversible circuit implemented with k-CNOT gates, addition of only one extra control line along with duplication each k-CNOT gate yields an easily testable design …

Optimum Test Set for Bridging Fault Detection in Reversible Circuits

Testing of bridging faults in a reversible circuit is investigated in this paper. The intra-level single bridging fault model is considered here, i.e. any single pair of lines, both lying at the same level of the circuit, may be assumed to have been logically shorted in order to model a defect. For an (n · n) reversible circuit with d levels realized with simple Toffoli gates, the time complexity of the test generation procedure is O(nd2 log2n). A test set of cardinality O(d log2n) is found to be sufficient for testing all such detectable faults. A minimal test set can also be easily derived by using the concept of test equivalence.

An Automated Group Key Authentication System Using Secret Image Sharing Scheme

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Detection of Bridging Fault in Reversible Circuit

Logic synthesis with reversible circuits has received considerable interest in the light of advances recently made in quantum computation. Implementation of a reversible circuit is envisaged by deploying several special types of quantum gates, such as Toffoli gate. Newer technologies like ion trapping or nuclear magnetic resonance are required to emulate quantum gates. This paper presents the testability issue of reversible circuits under the bridging fault model. Intra-level bridging fault model is considered here, ie, a single pair of lines, both lying at the same level of the circuit, may be assumed to have been logically shorted in order to model a defect in reversible circuit. For an (N x N) reversible circuit with d levels realized with simple Toffoli gates, the time complexity of the test generation procedure is O (Nd2 log2 N). A test set of cardinality O (d log2 N) is found to be sufficient for testing all such detectable faults. A …

Healthcare Technology for Reducing the Risk and the Spread of COVID-19 Pandemic and Other Epidemics

As the very first big outbreak in our era, COVID-19 offers an outstanding incentive to legislators, and regulatory authorities should find procedural viability, moral validity, and usefulness of implementation in the time of new healthcare technologies. This chapter addresses the outbreak of coronavirus and how digital healthcare technologies can help navigate and resolve COVID-19 and future epidemics. As the coronavirus pandemic (COVID-2019) spreads, technological advancements and efforts to control the disease, manage clients safely, and promote teamwork among overburdened health care practitioners designing innovative, efficient vaccines would be critical. This review looks at how various healthcare scientific sectors are assisting in the fight against the pandemic, as well as how innovative implementations are being used to treat or cure illness. An analysis of the technical landscape in the …

Personalization in Education Using Recommendation System: An Overview

In the modern era, when everyone has access to all the information around the world, Internet plays the most crucial role in making the process much smoother. It has already been able to flow its legacy in nearly all paths and age groups, education as a whole is also not excluded from this association factor. Internet has thus made its footprint in the world of education known as e-learning. E-learning takes into account different techniques of Internet such as Integrated Classroom Teaching to provide education to common masses. This high-tech education is a significant component of modern world today, delivering, aiding, and maximizing teaching quality. E-learning encompasses the involvement of educators and learners and mentors to enhance their work using this technology. In today’s era, everyone searches any information using the help of sites like Google, Yahoo, YouTube, etc., and it is readily available to …

Social Network Analysis in Education: A Study

These days social media sites have been gaining huge attention. Millions of people are accessing social media like Facebook, Instagram, Twitter, etc. Because of very affordable Internet, people are spending hours on it. People are taking interest on social network for information, user’s opinion on diverse subject matters. A wide range of techniques of data mining allowed us to detect useful data from massive database by some of the algorithms where we can find the patterns of users’ thinking and trend on social network. It also reduces difficulties by the time of discovering contents. Not just that, a huge data also comes from the educational system. This data is used to realize the knowledge in decision-making. Educational data mining methods are designed to understand and measure the performance of students and also helpful to study students’ behaviour. Data mining technique is also important to survey the …

10. Recommender system in healthcare: an overview

Today information technologies have led to a vast number of innovations and developments in almost every field of advancement. In this context, recommender systems (RS) have been setting milestones in the service industry. If we look at the web-based services, it can be said that RS has contributed majorly to increase the reachability of products and to provide a sea of options for potential customers. This RS can also be used as a tool to support decision-making by the decision-makers. With this advancement of RS in the service industry, healthcare systems also do not lag. Health RS (HRS) are becoming an important platform for providing healthcare services, which would cut down the hectic schedule of visiting the doctors and waiting for hours to get checked. In the healthcare industry, RS already play a very significant role in terms of supporting decision-making process about any individual’s health.

Deep Learning for COVID-19

The devastating outbreak of the SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus) also known as COVID-2019 has succeeded in introducing the danger to the worldwide living. Now COVID-19 is among the main potentially deadly issues in the world. Rapid and precise identification of COVID-19 infection is important to diagnose, make informed assumptions, and assure that patients receive care that aims to save people’s health and life. The entire community is making enormous endeavors to tackle the spreading of such a horrible epidemic in forms of communications true, economy, information sources, safety equipment, existence-risk treatments, and many on this and many other tools. Coronavirus triggers a vast range of viral disease; however, it is a virus of the kind RNA which can affect all humans and animals. Coronavirus is now identified in this chapter uses a form of deep learning …