Advances in Computing and Data Sciences

Face detection comes under the domain of object detection and tracking. Face detection is an integral part of the motion based object detection which combines digital image processing and computer vision for the detection of instances and faces as well. This paper provides a brief overview of the recent trends; current open challenging issues and their solutions available for efficient detection of faces form video stream or still images. This paper also discusses various approaches which are widely used to detect the faces in the dynamic background, illumination and other current challenges. In the last section, a framework for face detection is also proposed using SVM classifier.

Now days, Agile development methodologies are becoming popular in various IT industries. This methodology is combination of different stages in repetitive and incremental manner. Its main focus is to increase the adaptability in process, which in turn increases customer satisfaction. There are various development frameworks in agile methodology like Scrum and Kanban. There is also another programming practice known as Test Driven Development. It starts with developing test for a feature, before its implementation. It is also known as test first programming. The main objective of this paper is to adopt an approach in which TDD can be merged with Scrum to add benefits of TDD in Scrum and also to provide a comprehensive review of this approach.

Edge computing reduces latency, energy overhead and communication bandwidth bottlenecks. In this paper, a designed Proportional-Integrator (PI) motion controller for a Permanent Magnetic DC (PMDC) motor is integrated with IoT technology. This controller receives the preferred speed from the cloud, performs all necessary computation at Edge Level, derives actions and sends both output (real) speed and Integral Absolute Error (IAE) performance index (as an indication of controller performance) to the cloud. Firstly, both system identification and PI controller tuning are performed with the help of MATLAB Simulink and MATLAB support package for ARDUINO. ARDUINO Mega development board is used to implement the controller. An inbuilt PYTHON program in Raspberry Pi 3 is used as a software Gateway to enable receiving/sending data between the controller and the cloud (ThingSpeak.com IoT platform in our case). However, all necessary computations are intended to take place at Edge level only and this is for the tasks of improving latency, power consumption and bandwidth. Gateway level is used to gather the data coming from Edge level and send it to Cloud level; it is also used to send the data coming from Cloud level to the Edge level. Cloud level is the user interface to the system and enables him to control the speed and receive the controller working performance. An integrated work is the main contribution of current paper in which an attempt to construct a link between research works in both control systems and industrial IoT fields.

Security is a crucial aspect of our lives. The concept of smart home infrastructure drives the idea of providing flexibility to the end-user, to control their home devices from the remote location. It contains the upgrade of home-devices from traditional mode to the internet. Manufactures of smart home devices focusing on the usability part and do not develop the smart home devices from scratch they use the solutions already provided by big IT companies. Due to the lack of the standard for IoT devices communication, these devices are incompatible with each other. Different types of attacks done on many smart home devices using malicious firmware, insecure communication channel, physical uploading the malicious software and many more. In this paper, we represent the analysis of the survey on different smart home solutions and devices done by many researchers in the past few years and solutions provided by them. Analyzing different authors work and recent incidents, we found that we are still far behind the smart home security. It is not only the responsibility of manufactures but also the duty of the end-user to take the security of these devices seriously by using proper preventive measures.

Around 5% of people across the globe have difficulty in speaking or are unable to speak. So, to overcome this difficulty, sign language came into the picture. It is a method of non-verbal communication which is usually used by deaf and mute people. Another problem that arises with sign language is that people without hearing or speaking problems do not learn this language. This problem is severe as it creates a barrier between them. To resolve this issue, this paper makes use of computer vision and machine learning along with Convolutional Neural Network. The objective of this paper is to facilitate communication among deaf and mute and other people. For achieving this objective, a system is built to convert hand gestures to voice with gesture understanding and motion capture. This system will be helpful for deaf and mute people as it will increase their communication with other people.

One of the most promising fields is Wireless Body Area Sensor network (WBASN) which consists of a large number of wearable or implantable sensor nodes for collecting physiological data from the patients. The healthcare data is most sensitive digital data which contains high, delicate private data, and it needs to be shared among the peoples such as health specialists, pharmacist, family members, insurance companies, hospitals, etc. Due to the openness of wireless network, WBASN is vulnerable to a different variety of attacks; it needs high-security mechanisms to secure the data. The breach of healthcare information is one of the most crucial concern nowadays. In this paper, we mainly emphasise the general outlook of wireless body area sensor networks, which includes the architecture of healthcare system, various research domain, need of security and privacy of WBASNs. Further, the pro and cons of different security mechanism for WBASNs are discussed.

Fuzzy c-means (FCM) is a frequently used clustering method because of its efficiency, simplicity and easy implementation. Major drawbacks of FCM are sensitivity to initialization and local convergence problem. To overcome the drawbacks, the proposed method describes a hybrid FCM using Bat optimization and Maxi-min classifier. Maxi-min classifier is used to decide the count of clusters and then pass that count to randomized fuzzy c-means algorithm, which improves the performance. Bat optimization is a global optimization method used for solving many optimization problems due to its high convergence rate. Two popular datasets from kaggle are used to show the comparison between proposed technique and the fuzzy c means algorithm in terms of performance. Experiment results showing that the proposed technique is efficient and the results are encouraging.

Network motifs are the building blocks of complex networks. Studying these frequently occurring patterns disclose a lot of information about these networks. The applications of Network motifs are very much evident now-a-days, in almost every field including biological networks, World Wide Web (WWW), etc. Some of the important motifs are feed forward loops, bi-fan, bi-parallel, fully connected triads. But, discovering these motifs is a computationally challenging task. In this paper, various techniques that are used to discover motifs are presented, along with detailed discussions on several issues and challenges in this area.

In today’s digital era, managing patient’s records can be one of the most challenging task for hospitals and health care centers. In hospitals, patient’s medical data like medical reports and images (like x-ray, CT and MRI) etc. are stored at different locations at server side that results in more memory utilization. Also, Medical applications like telemedicine require exchange of medical data between two health care centers that needs huge transmission bandwidth. As a result, the use of watermarking in medical applications would prove to be advantageous as multiple images can be hidden into a single image. Multiple image watermarking system is proposed in this paper which thereby can result in efficient memory and bandwidth utilization. The algorithm simultaneously embeds three different medical images as multiple watermarks into all the three color planes (Red, Green and Blue planes) of cover image there by reducing the memory for compact storage and bandwidth requirement during transmission thereby retaining the quality of images. For maintaining the confidentiality as well as security, Arnold’s scrambling method is used for encrypting the watermarks. The Performance indicators used for measuring the quality of watermarked image are peak signal to noise ratio (PSNR) and correlation coefficient.

A lot of effort and literature has been developed for conventional software. Defect prediction models can be helpful for project managers to improve the quality of software. However, there is insufficient literature concerning the defect proneness of handheld device (mobile) applications, (henceforth HHDA) instead of conventional applications. Still, no efforts were accomplished to figure out the distinct characteristics of handheld device app bugs and their dispersion among the layered architecture of applications. This paper aims to investigate bug proneness of handheld device applications in contrast with the conventional application. In this work, the authors analyzed the bug distribution of HHDA and conventional apps in the different layer of the architecture. There are 15591 bugs of 28 distinct applications have considered. Two-way ANOVA and Bootstrapping approach have used. This empirical analysis firmly administers that mobile application is more defect prone as compared to conventional applications in the presentation layer.

Internet is distributed environment and hence, huge amount of information is available on it. People use internet to access the information on the web. While referring to any information people face difficulty to understand the complex sentences and words used related to technology and science. Technical and scientific words are mostly found in research papers, medical reports, newspapers and other reading material. Text simplification is a technique used to automatically transform complicated text into simpler form. In the proposed system an efficient text simplification technique has been developed using word net model available in the Natural Language toolkit (NLTK). The dataset used for experimentation is collected through a random survey from web sources. Here, the proposed system is divided into 3 phases. In the first phase data collection and pre-processing has been performed. In second phase complex words are identified and in the 3rd phase replacement of complex words with their simple synonyms is being done. The performance of the system has been analyzed by user review to accuracy of 87%.

In delay tolerant network data, routing is a field of concern in late years and a lot of researchers suggested many techniques for routing. Number of nodes is very less in delay tolerant sparse network, even criteria of losing data is huge. Many approaches been proposed in recent past but most of them failed to perform significantly. An opinion dynamic based approach proposed in this work, which calculates the trust value of a node due to which source node decide whether to send the data to the next node or not because of security issues as in network malicious nodes can be present, and it also calculate an opinion with other nodes too in a network. An opinion calculated based on parameters like packet delivery delay, number of packets lost and residual energy. The result also shows the improvement in various performance parameters in the network.

The article describes the principles of creating a unified system of modules interacting with each other in the implementation of projects for the conversion of industrial facilities. The main criteria of forming a complex organizational system, which includes a large number of functional subsystems and modules related to investment, design, production and information components of the structure of the project, are described. For decades, the principles of urban planning of various cities and megacities in the Soviet Union and later in Russia were formed on the same basic principles. Clearly distinguished contours of residential buildings, industrial areas, as well as forest areas and urban infrastructure. Over time, the urban environment absorbed new territories, developing not only geographically, but also forming new modern social requirements. So were formed new principles of design of space-planning solutions of residential premises of apartment buildings, requirements for the formation of “green” urban areas, otherwise began to form the structure of educational institutions. In addition, the approach to the preservation and development of urban Geoecology has changed significantly. Basically, this factor served as the basis for the formation of municipal programs of renovation of industrial areas that have an impact on the environment of the city.By creating qualitative and quantitative characteristics of the individual elements of the system under consideration, it is possible to formulate the basic requirements for the source data necessary to create a structured model of organizational design and project management. At the same time, the system should function reliably in the interaction of all integrated structures of the project under the influence of the external environment. For decades, the principles of urban planning of various cities and megacities in the Soviet Union and later in Russia were formed on the same basic principles. Clearly distinguished contours of residential buildings, industrial areas, as well as forest areas and urban infrastructure. Over time, the urban environment absorbed new territories, developing not only geographically, but also forming new modern social requirements. So were formed new principles of design of space-planning solutions of residential premises of apartment buildings, requirements for the formation of “green” urban areas, otherwise began to form the structure of educational institutions. In addition, the approach to the preservation and development of urban Geoecology has changed significantly. Basically, this factor served as the basis for the formation of municipal programs of renovation of industrial areas that have an impact on the environment of the city. The principles and nature of urban clusters are described in detail, their separate types and types, as well as the relationship and the main criteria for their functioning are highlighted.

In our modern world of social interactions where the analysis of each content on social media is based on the impact of the sentiment it imposes on the forum. The proposed system is used to implement a more personalized and customized report of these impacts. Email is of main focus here where in out of all other social applications, responses to and from the email is the most traditional and ethical way to communicate online. Users share all information, through internet especially emails because of its fast transmission and is considered as the most professional medium. Hence the proposed model focus more on the subjective content of email processed on R libraries created for Natural language processing in a more customized way. Nowadays, crime rate in emails are increasing drastically. Spamming, phishing and email fraudulent are the ways of targeting common people. The sentimental analysis on the impact of the email received is analysed and visualized. The system also proposes a design for establishing a framework that detects the suspicious one by comparing the mail with keywords and also reveals the level of suspiciousness in the particular mail.

Multi Word Expressions (MWEs) are one of the most widely used term in linguistics which mainly deals with combination of words rather than single word. In Hindi language, MWEs have become significant and popular for text processing and research related activities. The nicety of any term in linguistics is justified by using statistical measures. Many of these statistical measures are based on frequency of occurrence of a particular word pattern in a corpus. Syntactic fixedness is one of the important statistical measure, which can be used for measuring the degree of lexical and syntactic restrictiveness in the MWEs extraction and analysis process. This paper mainly focuses on evaluating the degree of lexical and syntactic fixedness and justifying their role for Hindi MWEs. The corpus used for experimental purpose is collected from the famous Hindi novel “Godaan”. Total 36 text files of the novel are used for the evaluation purpose. The degree of lexical and syntactic fixedness are measured for many classes of 2-grams Hindi MWEs and results are analyzed for accuracy.

QCA is an upcoming technology with high performance and ultra-low power, less time to designs any circuit while comparing with that of CMOS technology. In existing design the full adder is design with coplanar crossover method. This method requires more number of cells and leading to large area. In the present investigation coplanar crossover and multilayer methods have been implemented with 16 nm dot cells. Five input majority gate is proposed using multilayer full adder architecture and three input majority gate is designed using coplanar crossover method. The offer circuit to perform ultra-low power there by decreasing the area by 20% and more over complexity of circuit is reduced.

In this reviewed endeavour, a mathematical model is formulated to assess the spread of a distributed attack over a computer network for critical targeted resources. In this paper a mathematical model is formulated, the two sources susceptible, vaccinated, infected, recovered nanonodes in the target population (e- $$ S_{t} V_{t} I_{t} R_{t} $$ ) and susceptible, infected, susceptible nanonodes in the attacking population (e-SIS) epidemic model generated in order to propagate malicious object in the network. Further the analysis of the model has been concentrated upon the basic reproduction number. Where threshold value has effectively examined the stability of the network system. This work is verified for both asymptotical stable, that is the basic reproduction number less than on when the infection free equilibrium express the stability and basic reproduction number is more than one when endemic equilibrium is stable. A very general recognized control mechanism is regarded as vaccination strategy, which is deployed in order to defend the malicious object in the computer network. Finally we examine the effect of vaccination on performance of the controlling strategy of malicious objects in the network. The simulated result produced has become compatible with the overall theoretical analysis.

Ransomware threat continues to grow over years. The existing defense techniques for detecting malicious malware will never be sufficient because of Malware Persistence Techniques. Packed malware makes analysis harder & also it may sound like a trusted executable for evading modern antivirus. This paper focuses on the analysis part of few ransomware samples using different reverse engineering tools & techniques. There are many automated tools available for performing malware analysis, but reversing it manually helped to write two different patches for Wannacry ransomware. Execution of patched ransomware will not encrypt the user machine. Due to new advanced evading techniques like Anti-Virtual Machine (VM) & Anti-debugging, automated malware analysis tools will be less useful. The Application Programming Interface (API) calls which we used to create patch, were used to create Yara rule for detecting different variants of the same malware as well.

Question and Answering (QA) in many collaborative social networks such as Yahoo!-answers, Stack Overflow have attracted copious users to post and transfer knowledge between users. This paper proposes an Adaptive global T-max Long Short-Term Memory-Convolutional Neural Network (ALSTM-CNN) method to retrieve semantically matching questions from historical questions and forecast the best answers by saving their effort and time. Moreover, a novel Field-aware Factorization Machine (FFM) classifier is adapted to rank the high-quality answers from large sparse data. This method has certain advantages include: (a) effectively learns the similarity based on simple pertained models with various multiple dimensions, (b) does not uses handcrafted features. This algorithm shows robust performance for various tasks (i.e., measuring textual similarity and paraphrase identification), when it employs on datasets such as Semantic Textual Similarity (STS) benchmark, Sentence Involving Compositional Knowledge (SICK), Microsoft Research Paraphrase Corpus (MRPC) and Wikipedia Question Answer dataset. The performance of our proposed method is compared with different classifiers and the result shows a better accuracy measure than other state-of-the-art methods.

The intelligent system has presumed a new hope in almost every field, a hope of improving things and making those things effortless for each person. In the same way, hope rose in the direction of distressed and impaired people like disabled people, handicapped people, autistic children, and many others. According to the study, Autism Spectrum Disorder (ASD) is easily found in 1 among 68 children. The main aim of our work is to help autism children to be adaptive, adjustable and interactive with outer world. For this we conducted survey and on the basis of the survey result analysis it was understood that the major problem the autism child suffers from is the Life skills and Emotional skills. So, we designed a Krisha mobile application which is friendly and easy to use, have games after every module so that autism child can enjoy learning and also have interactive interface that coincide with the current environment to keep them motivated and busy. The evaluation and the testing of the application were done. The result of which states that the autism children are benefitted in learning Life skills more than the Emotional skills.

The extensions in wireless sensor networks has stated that the research communities to turn up into the study of spatial reusability concepts. Because of restricted capacity in wireless links, the keen task is to select the optimal route for packet transmission. The optimal route selection which is achieved is verifiable by using the tremendous end-to-end efficiency rate. Wireless Sensor Networks routing is ideal confront because of essential features that differentiate these kind of networks are differ from other wireless networks like cellular networks or mobile ad hoc networks. This paper proposed an enhanced spatial reusability process using single path and any path routing protocols. Task of the single path routing is to reduce the cost of the nodes in wireless links, although the work of any path routing is to aid the intermediary node, one who observes the packet to take part in packet forwarding. Empirical outputs have shown the potency of our proposed work in terms of cost reduction functionalities.

Retinal vessel segmentation plays a significant role for accurate diagnostics of ophthalmic diseases. In this paper, a novel preprocessing step for retinal vessel segmentation via optic nerve head exclusion is proposed. The idea relies in the fact that the exclusion of brighter optic nerve head prior to contrast enhancement process can better enhance the blood vessels for accurate segmentation. A histogram based intensity thresholding scheme is introduced in order to extract the optic nerve head which is then replaced by its surrounding background pixels. The efficacy of the proposed preprocessing step is established by segmenting the retinal vessels from the optic nerve head excluded image enhanced using CLAHE algorithm. Experimental works are carried out with fundus images from DRIVE database. It shows that 1%–3% of improvement in terms of TPR measure is achieved.

A permutation on a given alphabet $$\varSigma = (1, 2, 3,\ldots , n)$$ is a sequence of elements in the alphabet where every element occurs precisely once. $$S_n$$ denotes the set of all such permutations on a given alphabet. $$I_n \in S_n$$ be the Identity permutation where elements are in ascending order i.e. $$(1, 2, 3,\ldots , n)$$ . $$R_n \in S_n$$ is the reverse permutation where elements are in descending order, i.e. $$R_n =(n, n-1, n-2,\ldots , 2, 1)$$ . An operation has been defined in OEIS which consists of exactly two moves: set-rotate that we call Rotate and pair-exchange that we call Exchange. Rotate is a left rotate of all elements (moves leftmost element to the right end) and Exchange is the pair-wise exchange of the two leftmost elements. We call this operation as LE. The optimum number of moves for transforming $$R_n$$ into $$I_n$$ with LE operation are known for $$n \le 10$$ ; as listed in OEIS with identity A048200. The contributions of this article are: (a) a novel upper bound for the number of moves required to sort $$R_n$$ with LE has been derived; (b) the optimum number of moves to sort the next larger $$R_n$$ i.e. $$R_{11}$$ has been computed. Sorting permutations with various operations has applications in genomics and computer interconnection networks.

This paper attempts to propose a methodology to reduce the size of high definition colored images taken by the professional photographers. In this digital era as technology is advancing so fast, high definition photos are captured. But these pictures take a lot of memory space. Therefore data and image compression techniques are in great requirement. The major goal is to find computationally efficient algorithm to significantly reduce the storage size with capability to retrieve the quality of image. The proposed work effectively uses Discrete Wavelet Transform (DWT) and only low frequency part of the image is transmitted, after that further iterations are performed to increase the compression ratio. Numbers of iterations are decided by making a trade-off between compression ratio (CR) and Peak Signal to Noise Ratio (PSNR) value. Arithmetic coding is applied for further compressing the image. To improve the image quality (PSNR) further at higher iteration, filters has been applied. Switching weighted median filter and simple median filter has been studied. Analysis on different window size of median filter has also been done to achieve improved PSNR value.

Image fusion is the procedure in which two input images are fused so as to develop the image quality. The input images have to be the images of the comparable prospect with assorted superiority measures. The superiority of the output image will be superior to any of the input images. In this paper, satellite image fusion performance based on Wavelet Packet Transform (WPT) is proposed. Two level decomposition WPT is done on two images to obtain sub-images. The ensuing coefficients are fused by new fusion rule to acquire the fused image. The worth of this method has explained by different images such as the INSAT 3D, INSAT 3A, LANDSAT and PAN images. In this paper the proposed WPT based fusion technique is compared with Discrete Wavelet Transform (DWT) based image fusion. Simulation results accomplished that the proposed method performs finer for image fusion when compared with DWT. Image fusion methods made a comparison against DWT and WPT quality and quantity. Investigational output ended that the proposed WPT design carry out finer for image fusion in association with DWT.

Role of cloud computing is emerging extensively as it not only reduces hardware maintenance cost, but high end servers provide all the facilities with very high speed and versatile resources required to run an application. Pricing and fairness play very important role in the market as marketing is done online and everything is handled virtually without physical presence. Role of cloud providers become very significant as they are controlling the competitive market by providing the required resources within the budget of a customer. At the same time importance of customers can’t be neglected as market can only flourish with the customer’s satisfaction. In online market fair distribution of resources is also a necessity for sustainability. The proposed model maintains fairness by giving priority to the genuine providers over the providers who want to monopolies or spoil the market. It also encourages customers who want to use resources for more time with fair bid values. Instead of rejecting non-genuine providers having bid very close to genuine providers, they are allowed to participate after genuine providers according to their degree of closeness with genuine providers. It will lead to increased resource availability and hence increased customers and provider’s participation ratio. The proposed model is implemented in CloudSim environment and compared with two popular existing models. The evaluation of proposed model has clearly shown improved utilization of resources and fair allocation with increased participation of customers and providers both.

Convolutional neural networks (CNN) are a boon to image classification algorithms as it can learn highly abstract features and work with less parameter. Overfitting, exploding gradient, and class imbalance are the major challenges while training the model using CNN. These issues can diminish the performance of the model. Proper understanding and use of corrective measures can substantially prevent the model from these issues and can increase the efficiency of the model. In this paper the conceptual understanding of the basic CNN model along with its key layers is provided. The paper summarizes the results of training the deep learning model using CNN on publicly available datasets of cats and dogs. Finally the paper discusses various methods such as data augmentation, regularization, dropout, etc. to prevent the CNN model from overfitting problem. The paper will also help beginners to have a broad comprehension of CNN and motivate them to venture in this field.

Various protein sequence classification approaches are developed to classify unknown sequences in to its classes or familes with an certain accuracy. Features extraction from protein sequence is a key technique to implement all approaches. N-gram encoding method is a popular feature extraction procedure. But to maintain the low computational time and high accuracy level of classification, it requires to fix up the upper limit of ‘N’ of N-gram encoding method. On the other hand, the standard deviation value of protein sequence is one of the important feature value which is extracted by N-gram encoding method. This feature can be extracted by two different ways like standard deviation calculation using standard mean value and using floating mean value. It is also important to find proper method to calculate the value of standard deviation. In this paper, an investigational proof has done to find upper limit of N-gram encoding method as well as find the proper technique to calculate the standard deviation value as a feature which are extracted from unknown protein sequence.

The incidence of diabetes is on the rise all over the globe. Therefore, a proper approach is necessary to identify the diabetic patients at the earliest and provide appropriate lifestyle intervention in preventing or postponing the onset of diabetes. Hyperglycemia and hypoglycemia are two important consequences of diabetes computed on the basis of blood glucose level. In this paper, we propose a machine learning approach to identify the probability of occurrence of hyperglycemia with the impact of physical activity (exercise). This prediction will be helpful in order to reduce the risk factor of hyperglycemia by timely taken preventive step and changing their lifestyle.

In this era of e-learning, it will be a great help to deaf people if there can be a system which will generate text caption for various videos. Most of the automatic caption generation system is based on audio to text conversion and thus its accuracy is inversely proportional to the noise in the video. So we have proposed a system which will generate the caption for video based upon the lip movement of the person speaking in the video. Using Facial landmark detector we have extracted facial features of the lip region from frames of the video. These features are fed to the three-dimensional convolutional neural network (3D CNN) to get the text output for the particular frame. The system is trained and tested on GRID dataset.

A new numerical method using Legendre wavelet together with the quasilinearization for solving nonlinear Klein-Gordon equation with initial conditions is proposed. In the proposed scheme both time as well as spatial derivatives of the Klein Gordon equation are approximated using wavelet without the help of Laplace transform, a contrast to the schemes available in the recent literature. Numerical studies assure that the less number of grid points are required to produce better accuracy and more stable with faster convergence than the Laplace transform based Legendre wavelet method. Further, While solving them numerically in the last section, a comparison is provided between Python and Matlab. The order of accuracy in Python and Matlab are same but Python takes much lesser time to produce the output compared to Matlab [Table 5].

Smart contracts are programs that help in automating agreement between multiple parties involving no external trusted authority. Since smart contracts deal with millions of dollars worth of virtual coins, it is important to ensure that they execute correctly and are free from vulnerabilities. This work focuses on smart contracts in Ethereum blockchain, the most utilized platform for smart contracts so far. Our emphasis is mainly on two core areas. One involves the runtime verification of ERC20 tokens using K framework and the other involves the comparison of tools available for detecting the vulnerabilities in smart contract. The six core functions of ERC20, namely allowance(), approve(), total-supply(), balanceof(), transferfrom() and transfer() were considered for runtime verification. ERC20 contracts were tested with ERC20 standard and the results showed that only 30% in allowance() function, 50% in transferfrom() function, and 90% in transfer() function, were compliant to the standard. The other focus area involves the comparison of existing tool that could identify vulnerabilities in smart contract. Five tools were taken for the comparison, namely Oyente, Securify, Remix, Smartcheck and Mythril and were tested against 15 different vulnerabilities. Out of the 5 tools taken, Smartcheck was found to detect the highest number of vulnerabilities.

A model of an automated temperature prediction on smart AC system for a room has been designed, developed and implemented with an embedded system. In a room, temperature of object (like human being) with the environment is detected, identified and analyzed, with an ideal temperature. Based on data, a mathematical formula can be derived and an algorithm has been formed by using the mathematical formula of the predicted temperature data and the values of the two sensors, where sensors are used for object temperature detection and the AC perform automatically turned on or turned off. Python programming language with its default library has been used to code for the successful implementation of the algorithm. This proposed embedded system can be implemented in any smart AC room where anyone can utilize the AC system automatically switched on/off with the predicted temperature. Exploit this embedded system in all over the places including for disabled peoples, personal room, conference room, hall room, classroom and transports, where manually control of Air conditioner is not feasible.

A special type of cluster is called hotspots in the sense that objects in the hotspot are more active as compared to all others (appearance, density, etc.). The object in a general cluster has a similarity which is less than the object in the hotspot. In spatial data mining hotspots detection is a process of identifying the region where events are more likely to happen than the others. Hotspot analysis is mainly used in the analysis of health and crime data. In this paper, the health care data set is used to find the Hotspot of the health condition in India. The clustering algorithm is used to find the hotspot. Two clustering algorithm K-medoid and Rough K-medoid are implemented to find the cluster. K-medoid is used to find the spatial cluster, Rough K-medoid finds the cluster by removing boundary points and in this way find cluster which is denser. Granules are created on the clusters created using K-medoid and Rough K-medoid and point lying in each granule is counted. Granule containing points above a particular threshold is considered as a potential hotspot. To find the footprint of the hotspot convex hull is created on each detected hotspot. Also in this paper hotspot and footprint is defined mathematically.

Cloud computing has gained more importance in the IT service model that offers cost-effective and scalable processing. It provides virtualized and on-demand services to the user over the internet using several networking protocols with exceptional flexibility. However, with the existing technologies and the vulnerabilities, it leads to the occurrence of several attacks in the cloud environment. Distributed Denial of Service (DDoS) is most dangerous among all the attacks which limit the cloud users to access service and resources. Therefore, the detection of DDoS in the network and the identification of attacked VMs is the most dominating task in the cloud environment. In this work, a novel DDoS attack detection mechanism is presented. The research is carried out as follows: (i) Initially DDoS attack is detected by identifying the maximum number of connections to the network, (ii) then the attacked virtual machine and non-attacked virtual machines will be clustered using Self-Organized Mapping (SOM) based Neural Network (NN). The experimental results exhibit that the presented system can efficiently detect DDoS attacks and cluster attack and non-attack VMs in an attacked cloud network. Moreover, these results demonstrate that the proposed DDoS attack prediction accuracy of 97.63% and precision of 95.4% and it is better than the existing technique.

People nowadays prefer sharing their opinions towards various products and services frequently on social networking sites (SNSs). These online reviews are huge in size and act as a goldmine for organizations to understand and monitor public reviews of their products and services. But these online reviews are highly unstructured in nature due to the presence of various linguistic features like hashtags, URLs, misspelled words, emoticons and many more. This highly unstructured data makes sentiment classification a challenging task. Hence, data pre-processing is an underlying and fundamental step in sentiment analysis. In the present work, authors have rigorously explored a series of pre-processing steps and observed that the sequence order of pre-processing steps affects the overall results. Hence, a sequence order of pre-processing steps has been proposed and implemented on two different social networks - Twitter and Google+. Twitter has been selected because of its tremendous popularity among netizens and Google+ has been selected because the domain of data for the proposed approach closely matches with the users’ interests on Google+. As the existing approach for handling data on Twitter cannot be implemented directly to handle Google+ data, a modified approach for Google+ has been suggested and implemented by the authors. In addition, some new dictionaries for handling linguistic features have been compiled and existing dictionaries have also been modified to improve pre-processing results. The proposed approach is implemented to evaluate the overall results.

Drought affects the natural environment of an area when it persists for a longer period, prompting dry season. Thus, such dry season can have many annihilating effects on river networks. The paper address this predominant issue in the form of an alternate solution which re-routes the course of the natural water sources, like rivers, through those areas, where the water supply is minimal in comparison with the demand, in a cost-effective and highly beneficial manner. In the proposed model, Deep Belief Network (DBN) is utilized to foresee the early event of drought in Marathwada region of Maharashtra. Standard Precipitation Index is used to categorize the severity of drought. Using DBN model, the accuracy obtained with root mean square error of 0.04469, mean absolute error of 0.00207 is far better over the traditional methods. The application of Swarm optimization technique is used to address the problem of drought mitigation through providing a re-routed path.

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%.

Topics generated by topic models are typically reproduced as a list of words. To decrease the cognitional overhead of understanding these topics for end-users, we have proposed labeling topics with a noun phrase that summarizes its theme or idea. Using the WordNet lexical database as candidate labels, we estimate natural labeling for documents with words to select the most relevant labels for topics. Compared to WUP similarity topic labeling system, our methodology is simpler, more effective, and obtains better topic labels.

Automatic detection of fake news, which could negatively affect individuals and the society, is an emerging research area attracting global attention. The problem has been approached in this paper from Natural Language Processing and Machine Learning perspectives. The evaluation is carried out for three standard datasets with a novel set of features extracted from the headlines and the contents. Performances of seven machine learning algorithms in terms of accuracies and F1 scores are compared. Gradient Boosting outperformed other classifiers with mean accuracy of 88% and F1-Score of 0.91.

In the domain of Intelligent Monitoring, Smart Driving and Robotics, Pedestrian intention detection is a prime discipline of object recognition. Currently, several pedestrian detection techniques are proposed however, just a handful are re-ported in the domain of pedestrian ‘intention’ detection. Due to the complications of the image background and pedestrian posture diversity, pedestrian intention detection is still a challenge which requires concise algorithms. In this paper, Single Shot Detector (SSD) is compared with Faster Region Convolutional Neural Network (Faster RCNN) architecture of deep neural network by applying different Convolutional Neural Network (CNN) models. Experiments have been conducted in a wide spectrum to obtain various models of Faster RCNN and SSD through compatible alterations in algorithm and parameters tuning. In this paper, Faster R-CNN and SSD architecture have been trained and their results are compared. New and simple evaluation performance parameters are suggested namely: Percentage Detection Index, Percentage Recognition Index and Precision score as compared to the traditional mean average precision (mAp) found in literature. While training these architectures with 1350 images, Faster RCNN learned three times faster than SSD with 2% increased accuracy.

The rate of crime is increasing rapidly and many citizens of country are the victims of these crimes. It is observed that though the number of crimes taking place is very huge, the actual number of crimes being reported to the legal authorities is very small. This huge difference is because of several reasons where one of the reason is lack of awareness about the civil rights and the laws. Many citizens in India are unaware about the evil practices performed against them and unethical exploitation of them done by others. The citizens are also not aware about various laws in the Indian Penal Code. So inorder to bridge this gap between the laws and the common citizens of India, we have proposed a Natural Language Processing (NLP) system using Word Mover’s Distance (WMD) which takes as input the textual description of crime and generates as output, the applicable Indian Penal Code (IPC) sections and the description of the punishments mentioned under these sections. The performance comparison of WMD with cosine similarity is presented. Finally, the accuracy of the system is measured by getting the result generated by the system to be validated by a lawyer personal.

Within recent decades the chances of a child being diagnosed with autism spectrum disorder have increased dramatically. Individuals with autism disorder have markedly different social and emotional actions and reactions than non-autistic individuals. It is a chronic disorder whose symptoms include failure to develop normal social relations with other people, impaired development of communicative ability, lack of imaginative ability, and repetitive, stereotyped movements. There exist numerous techniques associated to detect autism disorders in children. Facial expression-based method is an effective technique frequently used by medical experts to detect the emotional patterns of autistic children. Our paper reviews this technique to determine the behavioral analysis of autistic children. Comparative analysis of existing techniques is undertaken to select the most optimal technique of autism detection.

According to recent trends in information technology, classroom learning is transformed to Web based learning. This transformation helps learner to trigger digital technologies anywhere and anytime. This paper plan to build a system that can harness the power of the brain and build smart and meaningful applications to make life easier. The major problem is emerged during online education is loose the learner’s active attention after some duration of time. This leads to the user getting distracted without having any mechanism to provide him with a feedback, as a result, online learning is not getting as much effective as classroom learning. Therefore, EEG device is used for data acquisition, to measure EEG signals and also to monitor the attention levels of user. Proposed project will collect the EEG data to calculate various parameters such as concentration level, attention level, etc. These parameters will be used in the smart applications to provide real-time analysis and feedback to the user. This technology will provide real-time feedback user who has enrolled in MOOCs. This should foresee whether the student struggles or not while learning to give convenient alarms.

Optical Character and Font Recognizer focuses primarily on building a complete model for document processing. The proposed system recognizes the font style along with the text from an image of certain resolution. The system uses principles of both machine learning and image processing to obtain the desired results. The model uses Contour selection for character extraction and K-Nearest Neighbor approach for character and font recognition. With the assistance of the proposed system using the mentioned techniques, scanned documents can be altered or the font style of a particular document can be known as desired. Many models that perform character recognition are present but a model that performs both character and font recognition with good accuracy is difficult to find. The experiment resulted in 87% overall accuracy for detection of characters.

Customer’s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k-means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty.

In this work, the analysis of the surveys was carried out through a factorial analysis, which facilitates the evaluation of the validity of the selected construct for the case under study, as well as evaluating the quality of the service for each factor, with a view to determining the level of quality of the educational service, for which it integrates elements of descriptive and multivariate statistics with the management of the quality of the educational service. They are used as fundamental statistical techniques, descriptive analysis, factor analysis and analysis of variance. As a final result, it was concluded that the students of five UNITEC careers evaluated the educational service they receive as very satisfactory (4 points), highlighting the tangible elements as the most weighted factor. A significant aspect is that there are no significant differences in the perceptions of students from different careers and different sections.

This paper demonstrates a Windows based interactive application, which can be used for showcasing artworks in a virtual environment and can be a potential support for artists, curators, art gallery owners, art critics and academic researchers. In this demonstration, we described in details - how this application was developed with the help of a series of highly sophisticated software like- Autodesk 3ds Max, Unity and how it actually comes into action. Bunch of challenges that we faced during the development and implementation of this application - are also mentioned. The proper utilization of this interactive application will bring a significant change in the sector of arts. In addition, this work will add up a new dimension on the way of art appreciation.

As images are increasing exponentially over the Internet, the retrieval of such images using content-based approach becomes an important research area. Out of the various models of the image retrievals, recognition of facial images is highly used by many application areas. However, due to the different variations involved in the facial images, it is a challenging problem. Therefore, this work introduces an efficient face recognition method which uses the bag-of-features approach for the same. The proposed bag-of-features based face recognition approach uses Grey wolf optimization algorithm for obtaining the prominent visual words. The enhanced bag-of-features based face recognition approach has been analyzed on a face database of Oracle Research Laboratory against the classification accuracy. The experimental results show that the presented method identifies the faces more accurately than the other meta-heuristic based approaches.

The Internet of Things (IoT) is a new revolution of technology that interconnects billions of smart objects to each other offering autonomous services and comfort to everyday human lives. However, the information exchanged to provide services can introduce potential risks in terms of security and privacy. The geographic location of the user is one such information that can breach location privacy of the user. Researchers have provided algorithms to sustain location privacy of the users in IoT. The existing approach provides fixed base points as an obfuscated location to preserve location privacy of the user. However, these fixed base points are sometimes too far or too close to the user’s true location that either he cannot utilize services provided by Location Based Services (LBS) or sometimes there is not much distance between actual and obfuscated location. In this paper, the proposed method procures random obfuscated location according to time and location of the context-aware IoT device while retaining service utility. Experiment results compares the existing and proposed algorithm and shows that the proposed algorithm maintains a certain distance with user’s true location and the services provided by LBS can still be used.

Cyberbullying is one of the radical emerging problems with the advancements in the Internet, connecting people around the globe by social media networks. Existing studies mostly focus only on cyberbullying detection in the English language, thus the main objective of this paper is to develop an approach to detect cyberbullying in Hindi-English code-mixed language (Hinglish) which is exorbitantly used by Indian users. Due to the unavailability of Hinglish dataset, we created the Hinglish Cyberbullying Comments (HCC) labeled dataset consisting of comments from social media networks such as Instagram and YouTube. We also developed eight different machine learning models for sentiment classification in-order to automatically detect incidents of cyberbullying. Performance measures namely accuracy, precision, recall and f1 score are used to evaluate these models. Eventually, a hybrid model is developed based on top performers of these eight baseline classifiers which perform better with an accuracy of 80.26% and f1-score of 82.96%.

Advances in all the fields of everyday life, in the past decade have laid a foundational block for production of huge amount of data. In this age of algorithms where everything is within the finger tips, this huge amount of data is of no use unless and until it is not converted to a form which is beneficial and meaningful for the recipient to do something beneficial. This is taken care of by the Predictive analytics, which involves use of various statistical models, data mining techniques, artificial intelligence, machine learning and others to extract meaningful insights from data. Business ventures are constantly being flooded with huge amount of data that is generated from time to time. So there is a need to develop new approaches to foresee the time series data. This research paper presents the predictive analytics of time series data by taking real world data, to build an effective model for prediction. The results obtained were quite interesting.

Elections conducted on paper consumes many resources. Online voting system is very faster, cheaper and more suitable. Recent in online voting system improve the security guarantees for elections, because of confidentiality of voters and their integrity and validity. For security purpose, three election models are used for online voting: the mix-net model, the blind signature model, and the homomorphic encryption model. However only homomorphic encryption gives direct tallying without decrypting every votes. In this paper, we are focusing on ballot casting and tallying for ranked based voting system using Paillier homomorphic and Elgamal homomorphic encryption schemes and at the end we will compare results of both encryption schemes.

Latent Dirichlet Allocation (LDA) and its variant topic models have been widely used for performing text mining tasks. Topic models sometimes produce incoherent topics having noisy words with high probabilities. The reason is that topic model suffers from binary weighting of terms, sparsity and lack of semantic information. In this work, a fuzzy document representation is used within the framework of topic modeling that resolves these problems. Fuzzy document representation uses the concept of Fuzzy Bag of Word (FBoW) that maps each document to a fixed length fuzzy vector of basis terms. Each basis term in the fuzzy vector belongs to all documents in the dataset with some membership degree. Latent Dirichlet Allocation is tailored to use fuzzy document representation and results are compared with regular LDA and term weighted LDA over short and long text documents on document clustering and topic quality tasks. LDA with fuzzy representation generates more coherent topics than other two methods. It also outperforms the other methods on document clustering for short documents and produces comparable results with term weighted LDA for long documents.

In the era of big data and IoT, rate of data generation is very high which needs lot of space. So it is not feasible to store such huge data locally. Hence, to cater the large storage need cloud based storage models has evolved. With the increasing demand of cloud storage, occurrence frequency of security threats has increased exponentially. The steep rise in threat incidences cannot be ignored, as cloud storage model is being used for storing confidential data. Increased threat rate cause decline in the acceptance rate of cloud-based storage models besides it numerous benefits. In this paper, we are evaluating our existing proposal by applying it on an open source cloud based system. Evaluation is done by comparing the threats identified using our approach and threats reported on it by CVE.

Accurate iris movement detection and tracking is an important and widely used step in many Human-computer interactive applications. Among the eye features, eye corners are considered as stable and reliable reference points to measure the relative iris motion. In real time scenarios, the presence of spectacles prohibit the current state-of-the-art methods to yield accurate detection as the appearance of eye corners changes considerably due to the glare and occlusion caused by them. We term this problem as the Spectacle problem. In this paper we review the available single and multiple image based spectacle problem removal techniques and highlight the pros and cons of the approaches. For this state-of-the-art report, we investigated research papers, patents and thesis presenting the basic definitions, terminologies and new directions for future researches.

Prediction of field displacements of Earthquake induced slope failure is a common methodology used to estimate the possibility of failure in a ground shaking scenario of interest. Newmark’s algorithm has been used extensively over years to arrive at estimates of ground displacements during earthquakes. This method has been proven to be a reliable technique to predict the spatial distribution of earthquake induced landslides. The current study involves selecting 12 horizontal components of strong motion records from 6 recording stations during the main shock of the Sikkim earthquake of magnitude Mw 6.9 that occurred on September 18th 2011. This data is then used in predicting the spatial distribution of the landslides triggered in and around the Mangan and Singtam areas in the district of North and East Sikkim. Displacement values were calculated by rigorous numerical integration of the acceleration records. These values were then regressed in a multiple linear regression model and the resultant equation was found to be statistically significant with an R2value of 85.7%. These predicted displacement values were compared with the field data of triggered landslides and was found to predict the slope failures with fair amount of accuracy given the nature of the triggered landslides and the climatic conditions during the earthquake event.

Isolated word recognition has been a subject of research since the 1940s when speech recognition technology was in a nascent stage. Recurrent neural networks and deep feed-forward networks are currently being explored by researchers to increase the efficiency of the speech recognition systems. However, probabilistic techniques like Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) have been the state-of-the-art since long. This paper performs a quantitative analysis of feature extraction techniques for isolated word recognition to provide better insights in improving the efficiency of the system. In this regard, a basic architecture of the word recognizer system has been modelled and it has been observed that Mel Frequency Cepstrum Coefficients (MFCC) in combination with Delta and Delta-Delta parameters have 92.4% accuracy for a sufficiently large dataset. Also MFCC features, appended with Delta parameters have 87.0% accuracy which is 36.4% higher than that of Short Time Fourier Transform (STFT) features. The feature extraction techniques have been classified by a Gaussian Mixture Model- Hidden Markov Model (GMM-HMM) classifier. This paper also studies the effect of varying data size and number of features on the recognition model towards efficient word recognition. These recognizers may then be used as a building block for mispronunciation detection in a Computer Aided Pronunciation Training (CAPT) system.

It is often observed that phases in complex transform of images are important ingredients compared to their magnitude for information extraction. Literature indicates that these phases are immune to noise. The aim of this paper is to find the structural (Edge) and statistical information retained by the complex domain phases for the images corrupted with additive white Gaussian noise (AWGN) and multiplicative (speckle) noise. Initially, we measure the edge information preserved by both phase and magnitude only synthesized image using edge mismatch error (EMM), to illustrate the significance of phase in image restoration and reconstruction. A mathematical model for the sensitivity of phase and magnitude is derived to examine the respective rate of deterioration under varying noise strength. Both the mathematical finding and experimental results indicate that the phase of any complex transform is degraded slowly compared to its magnitude. Comparative analysis of effect of noise on these phases is also investigated and reported.

Fundus image, picture of posterior portion of eye, facilitates non-intrusive diagnosis of various eye diseases. Glaucoma is progressive neurodegenerative ocular disorder, second leading cause of blindness in the world. For diagnosis of Glaucoma using fundus image, cup to disc ratio is the method preferred by researchers. Various methods like Active Contour, K-mean Clustering and Thresholding method are used to segment cup and disk from fundus images. In this paper a comparative study of the above mentioned methods has been presented. These methods were independently tested on two public databases namely DRISHTI-GS, and DRIONS-DB. Based upon the results obtained it was found that active contour technique is best among these methods.

Digital watermarking always attracts the attention of researchers from early nineties to present scenario, in the ever changing world of digital creation, transmission and modification of information. In this paper we draw our attention towards a next generation of digital watermarking based on specific features of digital information using Harris corner detection (strongest) method. In this scheme first twenty strongest corner points obtained using Harris method are used as a reference for embedding watermark information and to retain the synchronization information after distortions. The scheme is time efficient as only twenty strongest corner points per frame are computed. Hence the proposed scheme is useful for time constrained applications. The experimental results confirms that the suggested scheme show a adequate level of resilience against the majority of image processing attacks together with affine transformations.

This paper presents a new approach to study the use of the Presentation Architecture Control model in Business Intelligence. BI Tools are in heavy demand as they are used to extract data components and reports. The model view controller architecture while being most commonly used interactive system is still not a universal solution and fails in certain scenarios. The presentation abstraction controller architecture has several agents with different responsibilities in a software system has layers with completely different functionalities. It also solves the issue of separability in Business Intelligence We describe the object-oriented models and define their characteristics. While stating the limitations of the MVC model, The PAC model is explained with the help of scenarios and its limitations are also highlighted.

Among the biometric recognition systems, gait recognition plays an important role due to its attractive advantages over other biometric systems. One of the crucial tasks in gait recognition research is the extraction of discriminative features. In this paper, a novel and efficient discriminative feature vector using the signal characteristics of motion of centroids across video frames is proposed. These centroid based features are obtained from the upper and lower regions of the gait silhouette frames in a gait cycle. Since gait cycle contains the sequence of motion pattern and this pattern possesses uniqueness over individuals, extracting the centroid features can better represent the dynamic variations. These variations can be viewed as a signal and therefore the signal properties obtained from the centroid features contains more discriminant information of an individual. Experiments are carried out with CASIA gait dataset B and the proposed feature achieves 97.3% of accuracy using SVM classifier.

The automatic discovery of user navigation pattern can be done by web usage mining. The web logs which are created on daily basis at the time web pages access by various user. The paper presents restructuring of web contents according to the user preference and pattern. The proposed algorithm suggests optimal path for users by considering eye tracking and mouse movement. The path suggests by the proposed algorithm considers only the true users those who are physically present and suggests a optimal path as per the logs recorded.

Since the beginning of computational linguistics machine translation is being one of the holy grail of natural language processing. With the advancement of world wide web and emergence of data driven methods the machine translation has received a jolt of new activity and more visibility. Still the visible lack of accuracy in machine translation shows that the much work remains to be done in concern of human identical translation. The approaches of machine translation uses the dictionary meaning of the target language as a heart of translation process. Dictionary based machine translation system continuously requires the intervention from the human expert. Unlike other languages Sanskrit is having a standalone procedure for developing new words and defining its meaning as well. Maharshi Panini developed a complete set of grammatical rules that defines the whole procedure to process the input language. The paper presents the computer simulation of Paninian rules of Sanskrit grammar focuses on the generation of meaning of unknown word using most elementary component of a language - dhAtu. We have used the direct machine translation approach for achieving dictionary-independent machine translation.

Cloud computing is one of the computing methodology that can address the on demand requirement of most of the applications in efficient manner. This system also permits the pay-per usage rating model for the purpose of computing services which are delivered to the end users throughout the globe using Internet. Because of reduced resources it becomes very difficult for the cloud purveyors to offer all the end users their required services. One such challenge raised by the cloud application is management of Quality of Service (QoS) that is the matter of allocating resources to provide assured service on the basis of availability, performance and reliability. From the cloud vendors impression of cloud resources can be assigned in a reasonable manner. Thus it is at most importance to meet the QoS requirement and satisfaction level of cloud users. This paper aims at the study of various researchers in the field of Resource allocation and resource availability in cloud computing environment based on QoS requirements.

Finding the vacant space for parking a vehicle during peak hours is becoming a difficult task at ones end. Parking process whether in shopping malls, restaurants, or offices etc. is a long process and also leads to waste of gasoline. Smart car parking helps in finding the parking slot through Vehicular Ad Hoc Networks (VANET’s). For vehicle communication, some devices such as roadside units and on-board units are present that provides parking slot information. In the proposed work, we have introduced an online reservation facility for parking slot. People can reserve their parking space in advance before reaching to their venues in advance. This will help in reducing the waiting time for the parking allocation to the particular vehicle. This will also help to enhance the parking capabilities and will increase the efficiency when compared to other parking strategies. Our proposed approach can minimize the cost of parking on per person basis, exhaust of vehicle, and indirectly it will impact on save of wastage of gasoline and will keep the environment green.

A blockchain is a peer-to-peer electronic ledger of transactions that may be publicly or privately distributed to all users. Apart from unique consensus mechanisms, their success is also obliged to smart contracts. Also, These programs let on distrusting parties to enter reconciliation that are executed autonomously. Although a number of studies focus on security of introducing new programming languages., However, there is no comprehensive survey on the smart-contract language in suitability and expressiveness concepts and properties that recognize the interaction between people in organizations and technology in workplaces. To fill this gap, we conduct a systematic analysis about smart-contract language properties that focus on e-contractual and pattern-based exploration. In particular, this paper gives smart-contract language taxonomy, introducing technical challenges of languages as well as recent solutions in tackling the challenges. Moreover, this paper also represents the future research direction in the introducing new smart-contract language.