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Assuring high performance is one of the most important factors when building computerized systems. In distributed component-based systems different configurations and workloads are common. Over the years, many approaches were proposed to analyse, predict, measure and evaluate the performance of component-based distributed systems in an attempt to improve its performance. Higher performance results in better utilization of system resources, high throughput and quick response time of user requests. In this paper, we extend the work of the E-Avala approach to improve the overall performance of the proposed approach by adding power processing capabilities. The proposed approach has been implemented and compared with previous approach using Arena simulation with varying configuration parameters. Comparative results show that the proposed approach has given better performance with different levels of processing powers.

Today the computational study of people’s opinion expressed in free form written text is called the field of sentiment analysis and opinion mining. Various research areas such as Natural Language Processing, Data Mining, Text Mining lie in field of Sentiment Analysis and is also becoming major part of importance to organizations because of online commerce is included in their operational strategy. Due to excess of user’s comments, feedback on web there is a need to analyze the user generated text. This research focuses on aspect level sentiment analysis in which identification of aspects and their related sentiments is being done. Opinion analysis helps to identify the polarity of the text and feature extraction. This study is being done to provide an effective and efficient framework to calculate the sentiments of written text by using Naive Bayes approach. For sentiment analysis dataset of 1060 reviews of different restaurants from online website TripAdvisor.com is being used. The outcome achieved good accuracy 80.833 percent.

Source code similarity are increasingly used in application development to identify clones, isolate bugs, and find copy-rights violations. Similar code fragments can be very problematic due to the fact that errors in the original code must be fixed in every copy. Other maintenance changes, such as extensions or patches, must be applied multiple times. Furthermore, the diversity of coding styles and flexibility of modern languages makes it difficult and cost ineffective to manually inspect large code repositories. Therefore, detection is only feasible by automatic techniques. We present an efficient and scalable approach for similar code fragment identification based on source code control flow graphs fingerprinting. The source code is processed to generate control flow graphs that are then hashed to create a unique fingerprint of the code capturing semantics as well as syntax similarity. The fingerprints can then be efficiently stored and retrieved to perform similarity search between code fragments. Experimental results from our prototype implementation supports the validity of our approach and show its effectiveness and efficiency in comparison with other solutions., Comment: 11 pages, 5 figures, Journal

Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM). To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used. This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image. Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.

In Desktop Grid, managing faults is very crucial and challenging job. So a fault tolerant system is mandatory requirement in desktop grid for fault identification and their resolution. A fault tolerant system allows applications to continue execution despite having faults without termination. In this paper,an adaptive fault tolerant scheduling system is presented that combines dynamic replication with rescheduling. The system initially schedules jobs depending upon the completion time and fault rate of resources and then fault-tolerant strategies are applied depending upon availability of resources. To measure the performance,experiments has been carried out and it has been observed that proposed scheduling system outperforms by a factor of 4.8% w.r.t. Average task response time and 0.02% w.r.t. Average flowtime as compared to existing system. On the other hand, there is no significant improvement is observed when BoT completion time and average execution time are compared to existing system.

Housing market price prediction is a big challenge. The 2008 global recession strongly showed that even the most sophisticated traditional economic models failed to foresee the crisis. New developments of behavioural economic theory indicate that the information from micro-level’s decision making will bring new solution to the age-old problem of economic forecasting. Additionally, the information revolution and big data methods have provided a new lens to study economic problems apart from traditional methodologies. This research provides the theoretical link between irrationality and big data methods. Empirically, big data methods will be used in forecasting the housing market cycle in Australia. Specifically, Google trends is included as a new variable in a time series auto-regression model to forecast housing market cycles.

In the last few years, different machine learning techniques such as supervised, unsupervised, and reinforcement learning have been effectively employed to solve distinct real-life multidisciplinary problems. These techniques have been effectively applied to accurately predict the problems related to stock values, disease diagnosis, sentiment analysis, text processing, gene classification, crop prediction, and weather forecasting. The objective of this manuscript is to present the systematic review on the use of these techniques in five major domains i.e. agriculture, finance, healthcare, education and engineering. A standard review methodology has been adapted to include and exclude the related literature. The performance of different supervised and nature-inspired computing techniques have been accessed on the basis of different performance metrics. The publication trend on the use of machine learning techniques in these five research areas has been also explored. Finally, the gaps in the study have been identified that will assist prospective researchers who want to pursue their research in these areas.

Through this article a deep learning technique is proposed for the extraction and classification of mathematical keywords from textual documents. Extraction of math keywords from textual data is predominant problem as textual documents contain a culmination of mathematical symbols and literals from natural language such as alphabets and words. Separation of these textual words embedded in the mathematical formulae is a complex task. Our proposed technique solves this critical problem of extracting mathematical keywords from textual documents using techniques such as stemming,tokenization and clustering mathematical keywords based on a training set of mathematical keyword and formulae pairs. The performance of the proposed technique is measured using the metrics such as retrieval time, Sensitivity, Accuracy, FPR, FNR, and FDR are used for appraisal of the proposed technique.

The scalable diverse data and increasing levels of complexity in engineering and management science have given a boost to Data mining technology. The purpose of the proposed research is to evaluate the rule-based technique to develop solutions for analyzing customer Post Purchase behavior through knowledge discovery paradigm of Association rule mining. Over the years, it has proved a good tool to predict because of the incorporation of actual mined patterns. The current work is focused on extracting knowledge about the customer purchasing psychology and behaviour for the most frequent item combinations. For the purchase implementation, association rule framework is assessed for its performance analysis. The inferences of this automated intelligent system are based on of real life data set of 120 item-set combinations of five computer peripherals. This knowledge will help in framing and executing the most appropriate market laws and rules for the overall business growth.

The usage of the internet has increased rapidly from the past few years. Thus it automatically increases the internet traffic. In the internet, a web server always responds to the client or web browser's requests. The major feature of a web server is to be available, scalable and predictable. So the core part to be concentrated is web server load balancing. Load balancing plays a vital role in allocating the jobs to the web server based on its status. There are various policies available for web server load balancing. Each of these policies came into existence based on the certain needs. In this paper, we have examined the latest policies of web server load balancing.

Urdu is the national language of Pakistan, also the most widely spoken and understandable language of the globe. In order to accomplish successful Urdu NLP a robust and high-performance NLP tools and resources are utmost necessary. Word segmentation takes on an authoritative role for morphologically rich languages such as Urdu for diverse NLP domains such as named entity recognition, sentiment analysis, part of speech tagging, information retrieval etc. The morphological richness property of Urdu adds to the challenges of the word segmentation task, because a single word can be composed of null or a few prefixes, a stem and null or a few suffixes. In this paper we present supervised Urdu word segmentation scheme based on part of speech (POS) information of the corresponding words. For experiments conditional random fields (CRF) with contextual feature is used. The performance of the proposed system is evaluated on 300K words, results shows evidential improvements on baseline approach.

Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending a DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter., Comment: 9 pages, 1 figure. This article is accepted for publication in EAI Endorsed Transactions on Scalable Information Systems

The volume of data on the web is growing rapidly, due to the proliferation of news sources, contents, blogs and journals etc. Like other languages, the Urdu language has also observed tremendous growth on the internet. As the volume of data is expanding, information retrieval (IR) is becoming complicated. Document clustering is an unsupervised ML approach, employed to group a huge number of dispersed documents into a small number of significant and consistent clusters, thus providing a base for indexing, IR and browsing mechanisms. Documents clustering has a long tradition in English as well as English like western languages, but Urdu lags behind in terms sophisticated natural language processing (NLP) tools and resources for documents clustering. Documents clustering becomes a challenging task in Urdu language having a rich morphology, particular structure, syntax peculiarities and cursive nature. In this study, we have developed a framework of document clustering and analysed various similarity measures for Urdu documents. We have also checked the effect of stop words removal in the process of Urdu document clustering.

The task of researching and developing information retrieval systems is becoming important in the big data age. Current search methods try to mention to fast searching based on keyword matching or similar semantic between query and documents but have not got a really effective engine for semantic search . In this paper, we propose a method for information retrieval based on probability inference with the DLMC model to search by semantic equivalents and a topic word with score for fast searching. Results of the experimental with 952 Vietnamese documents show that our method is really effective for Vietnamese document retrieval system.

INTRODUCTION: In this world of internet and artificial intelligence, Natural Language Processing has emerged as most demanding research area. Under Natural Language Processing, sentence simplification is one of the research area that deals with simplification or conversion of complex sentences in to simple sentences. OBJECTIVES: In this research article, author has proposed a novel approach for conversion (simplification) of complex sentences (participial type) of Punjabi language into easily understandable simple sentences. METHODS: Author performed lexical and morphological simplification by using morphological features of the language. Morphological features are used to identify the participial type complex sentences. RESULTS: on testing the proposed algorithm, a precision of 96.39%, recall of 91.37% and Fmeasure as 93.79% was reported. CONCLUSION: the developed system can be helpful for Aphasic and Dyslexia readers and can be used as subpart for machine translation system, summarization system and other Natural Language Processing applications.

Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It can be cured if the disease identified at early stage. Accurate classification of benign tumours can avoid patients undergoing unnecessary treatments. Data Analytics and machine learning methods provides framework for prognostic studies by errorless classification of data instances into relevant based on the cancer severity. In this study we have purposed a prediction model by combining artificial intelligent based learning technique with multivariate statistical method. For automation of the diagnosis process data mining plays an significant role. The data sets available in different repositories are noisy in nature. This study suggests a hybrid feature selection method to be used with PCA (Principal Component Analysis) and Artificial Neural Network (ANN). Preprocessing of data and extracting the most relevant features done by PCA. The proposed algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases. In classification phase 10 fold cross validation was used. The suggested algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved better accuracy with sensitivity and F measure comparison with others and by enhancing this concept we can provide a future scope to produce sophisticated learning models for diagnosis.

With huge advancements in Microelectronics, Wireless Sensor Network (WSN) is conquering its domain with low powered tiny sensors. These sensors are commonly used in monitoring and surveillance of remote and urban areas. Since the power supply to these tiny sensors is a battery, for the maximal efficiency of WSN, there arises a need for a maximal lifetime of these tiny sensors wherever they are deployed. Clustering is a promising solution but there is uncertainty in the selection of cluster heads. Fuzzy logic, however, can contribute to the selection of optimal candidates to play the role of such cluster heads. To muddle through the issue, we have proposed a fuzzy-based energy-efficient clustering approach (FEECA) in wireless sensor networks and also designed a fuzzy inference system that defines influential parameters for selecting optimal candidates for cluster head (CH) role. To reduce the expenditure borne by communication, master nodes are selected among the chosen cluster heads so that their communication distance to the sink can be reduced. In the proposed scenario the area is divided into the diagonal form to reduce the load of the network. Each part consists of a Sensor Node (SN), cluster head, and master node. In the diagonal form, the network is divided so that the distance travelled by the data packet through the diagonal path is always smaller than the distance in which the horizontal path exists, based on the triangular inequality theorem. Simulation experiments were conducted and experimental results unveiled better performance of proposed work in terms of stability period, more packet delivery to sink and extended lifetime.

The death ratio caused by heart diseases is threating around the world. Efficient and accurate diagnosis through information technology can turn over this picture. This article proposed Diagnosis Heart Disease using Mamdani Fuzzy Inference (DHD-MFI) based expert system which intelligently diagnoses heart disease. In an explorative pattern, the current research has taken six conducive variables for the purpose of fuzzy logic technical enhancement in the diagnosis of heart disease. The input fields comprise of age, chest pain, electrocardiography, blood pressure systolic, diabetic and cholesterol are transmitted with the help of Fuzzy rules which are framed in the light of low, normal, high and very high intensity among the input variations. The single output is obtained as a clinical decision support system for the heart diagnosis by using the Mamdani Inference method. The proposed DHD-MFI based expert system gives 94% overall accuracy.

The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their market strategies and hencehave raised several questions on the users' decision making (DM). OSNs, as being regularly used, have resulted in playing asignificant role in supporting consumer’s rational DM. We use a hybrid approach i.e. an online survey and fuzzy modeldevelopment to indicate how OSNs have resulted in giving an impact on the decision making. The research depicts that OSNssupport and empower users in the DM process specifically in Rationality, Design, & Choice and the model predicts the DMstrategy of each user according to their present decisions using fuzzy logic. Our results also reveal that different types of users(observers, seekers, and advisers) have significantly different participation styles, which in turn have an impact on the efficacyof the DM process. We discussed the implications for OSN designers and developers based on the findings from the research.

With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles manually is a time-consuming and laborious task, thus, giving rise to the requirement for automated computational tools that can provide insights about degree of fake ness for news articles. In this paper, a Natural Language Processing (NLP) based mechanism is proposed to combat this challenge of classifying news articles as either fake or real. Transfer learning on the Bidirectional Encoder Representations from Transformers (BERT) language model has been applied for this task. This paper demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform significantly well on the downstream task of classification of news articles. In addition, LSTM and Gradient Boosted Tree models have been built to perform the task and comparative results are provided for all three models. Fine-tuned BERT model could achieve an accuracy of 97.021% on NewsFN data and is able to outperform the other two models by approximately eight percent.

For years now, players known as data brokers have operated on an unregulated market, by building businesses from the aggregation and processing of data, away from public scrutiny. Due to the genesis of laws that ended up exposing their model, along with debates on data privacy, a new generation of data protection acts, much stricter, are being enacted worldwide. The shift away from the data commoditization paradigm might, at first, indicate the extinction of such businesses. In this paper, we present related concepts and a brief history that led to that current scenario, along with some collaborativeoriented proposals for the adequacy of the data brokerage industry to a transparent perspective.

In this paper, Audio QoE assessment experiments were conducted by using network parameters such as packet loss and reorder. The network model was created by using three computers, which includes sender, receiver, and middle between Linux based router machines was configured. NetEm tool was used to disturb the on-going network traffic to real-time packet loss and reorder in the broadcasting environment. VLC player was used to broadcasting and recording audio from sender to receiver machines and audios were presented to users to listen, and assign ratings according to their level of satisfaction. The results show the user’s satisfaction level is decreased when packet loss and reorder level is increased in audio streams. The user accepted a certain level of network traffic disturbance, however, the increment of disturbance in network traffic damage the audio quality below the then acceptable level. This research work will help to cloud service providers to provide QoE to users according to singed in service level agreements.

Hyper-convergence is a new innovation in data center technology, it changes the way clouds manage and maintain enterprise IT infrastructure. Hyper-convergence is more efficient and basically agile technology environment. Cloud computing is a latest technology due to provision of latest cloud services over the internet. The cloud service providers cannot promise accurate reliability of their services i.e. problem in provisioning of software or hardware failure etc. Reliability of cloud computing services depends on the ability of fault tolerance during the execution of services. There are so many factors can cause faults, such as network failure, browser crash, request time out or hacker attacks. When users are facing these types of faults, they usually resubmit their requests. However, if there is any key element involved in faults or errors, additional action may be needed to deal with system logs. If there is anomaly behavior occurred in faulted virtual machine, these VMs may need extra attention from cloud system protection and security point of view. In this paper, provision of reliability management in hyper-convergence cloud infrastructure is proposed and self-healing techniques in software as a service on the basis of failure in cloud services. Intelligent cloud service reliability framework will increase the reliability during execution of cloud service.

Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges of topic modelling, which will definitely give researchers an insight for good research.

Big data era has large volumes of data generated at high velocity from different data sources. Finding frequent subgraphs from the graph streams can be a challenging task as streams are non-uniformly distributed and continuously processed. Its applications include finding strongly interacting groups in social networks and sensor networks. To find frequent subgraphs, we proposed static single-window technique and dynamic sliding window techniques. We also proposed enhancements by extending proposed static approach with its variations and extending dynamic approach in variations of incremental strategy to find frequent subgraphs. We also solved the sub problem to extract frequent subpaths from sequence of paths. Its applications include finding congested sections in traffic analysis. We applied our proposed static and dynamic techniques to extract the frequent subpaths from sequence of paths. We experimented the proposed dynamic and static approaches with real and benchmark datasets.

Here in this study, we provide an empirical analysis of recent advances, characteristic and challenges of big data. Initially, we acquaint the readers with the general background, history, and characteristics of big data including volume, velocity, value and variety etc. The scope of applications for big data including political services and government monitoring, enterprise management, scientific research, public utilities, public administration and internet of things are illustrated. A detailed analysis is presented regarding opportunities and challenges faced by the public and private sectors during analysis phase of big data management such as storing, visualizing, capturing and so on. In addition, we investigated and reported a detailed empirical analysis of the most recent management tools like Hadoop and MapReduce, along with their different components, usage, and limitation. Finally, open issues and future directions for this new and dynamic area of research are provided. The primary objective of this empirical analysis is to present a broad-spectrum perspective of this emerging research area with the goal to present big data related concepts in a coherent manner to the beginners.

In this paper a novel approach for effective topic modeling is presented. The approach is different fromtraditional vector space model-based topic modeling, where the Bag of Words (BOW) approach is followed.The novelty of our approach is that in phrase-based vector space, where critical measure like point wisemutual information (PMI) and log frequency based mutual dependency (LGMD)is applied and phrase’ssuitability for particular topic are calculated and best considerable semantic N-Gram phrases and terms areconsidered for further topic modeling. In this experiment the proposed semantic N-Gram topic modeling iscompared with collocation Latent Dirichlet allocation(coll-LDA) and most appropriate state of the art topicmodeling technique latent Dirichlet allocation (LDA). Results are evaluated and it was found that perplexity isdrastically improved and found significant improvement in coherence score specifically for short text data setlike movie reviews and political blogs.

People have informal conversations on social media sites (Twitter, Facebook, etc) that shed light on the current issues around the world – opinions, and concerns about the learning process. Data from such environments can provide valuable information to predict the future to make effective decision making, however, the usage of such data is challenging. The complexity of such data from social media content requires the use of human understanding as well as social network analysis to make predictions. We focused on political groups around the world to discover linkages behind the Terrorism around the World to make predictions depend upon the data available. In this research paper, we focused on tweets related to the politicians and communities based on hashtags like #Syria #Weapons #Terrorist, etc. to identify the linkages of the user who are talking about these issues depending upon the HashTag we used for the content analysis of social network by using Gephi to make a prediction.

Blockchain is gaining attention since its development due to its features like immutability, transparency, decentralization. It can also be used as decentralized storage. Blockchain offers a wide range of digital services both in financial and nonfinancial sectors. In this paper, we discuss the basic concepts and terminologies associated with blockchain and discuss the application areas both in financial and non- financial sectors such as Digital voting, Digital identity, Education, etc. and discuss the existing framework in these areas, also the progress and challenges of global acquisition. The main highlight of this paper is COVID-19 and we discussed existing solutions to fight the COVID-19 pandemic by using such technology. This survey paper will be helpful for a data scientist as well as researchers to explore in the area COVID-19.

In today’s society, sentiment analysis has gained due importance as it provides useful information about products that are used by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at an early stage. It thus intimates users’ perception and charts out a path that is beneficial for the market to grow as a whole. Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of the detailed components based products and their associated features, a novel method is desired to meet these criteria. So far, no such method is explored that analyses the product’s components and their features simultaneously, on the basis of sentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis of product reviews while formulating a tree structure of product, components, and associated features. In addition, evaluation of double negative sentences, detecting questions and emotions from the review sentences are measured which increases efficiency of the FBA method. The comparison of product’s components reviews is done with other existing algorithmsTF, TF-IDF and Naive Bayes to demonstrate that the proposed FBA is coherent and auspicious.

An escalating focus worldwide on the development of persons with disabilities results in the disability-inclusive 2030 UN agenda for sustainable development. This paper discusses the development of Punjabi text to Indian Sign Language (ISL) conversion system aiming for the better communication and education of hearing-impaired people. The developed system accepts Punjabi text as input and deliver output as 3D animation. The currently created corpus involves more than 100 commonly used Punjabi words under various categories. This system has been tested on all the prepared words. Results of the developed system are very encouraging and the work is still going on with full enthusiasm. Since there is no universal or written form of sign language available, so generating synthetic sign animation is the best solution. In near future it can be implemented as a tutoring system to promote sign language education among common people of Punjab.

Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that efficiently predicts diabetic retinopathy. Machine learning techniques like Decision tree, Random forest, Adaptive boosting and Bagging are used as primary algorithms to train predictive models. An algorithm namely ‘Support Vector Machine using Gaussian kernel for retinopathy prediction’ is proposed in this work. The proposed algorithm is compared with the primary algorithms based on five evaluation metrics namely accuracy, Youden’s J index, concordance, Somers’ D statistic and balanced accuracy. From the results obtained the proposed algorithm obtained better values for all considered evaluation metrics. Thus the use of SVM with Gaussian kernel is proposed to be used for prediction of diabetic retinopathy.

The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.

In present internet usage scenario users (clients or customers) are utilizing distinctive physical things for different purposes. This idea takes the users into the universe of Internet of Things (IOT) which is a course of action and development of fair innovation. It allows affiliation, game plan and usage of different physical things present in the propelled universe, utilizing heterogeneous frameworks and correspondence of advances. The universe of IOT in different splendid circumstances with the infra-structure-less system refereed as Mobile Ad-hoc Network (MANET) prompts an imaginative methodology altogether appealing for current customers. The monetary imaginative worldview prompts associations among adaptable and compact MANET framework with IOT shaping a structure for the data innovation. MANET based IOT paradigm gives imperative flexibility to cutting edge clients and customers by diminishing costs of associations for the significant frameworks. This leads to new testing parameters in perspective of various organizations. In this examination work, authors have proposed a controlling response for the Internet of Things structure surrounding neoteric firefly routing algorithm associated with intelligent water drop routing methodology. Both firefly routing algorithm (FRA) and intelligent water drop routing algorithm (IWDRA) performs enhancement for the customary conventions of MANET. These enhancements will give enormous advances for various real life scenarios and issues. In the proposed methodology when FRA and IWDRA were connected in customary directing convention and mimicked for advancements, firefly calculation demonstrated its value over the water drop approach. The presented results of plan's examination give a ground-breaking procedure for growth through different execution factors specified as End-to-End Delay (E2D), Normalized Routing Load (NRL), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR) and throughput for the MANET based IOT worldview. The approval of the proposed methodology through network simulator test system approach prompts new stage of reliable game plan for future system establishment.

Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of these classifiers with training patterns of different sizes to study alcohol tests. In this paper, the Improved Version of the kNN (IVkNN) algorithm is presented which overcomes the limitation of the conventional kNN algorithm to classify wine quality. The proposed method typically identifies the same number of nearest neighbours for each test example. Results indicate a higher Overall Accuracy (OA) that oscillates between 67% and 76%. Among the three classifiers, the least sensitive to the training sample size was the kNN and produced the unrivalled OA, followed by sequential decision trees and logistic regression. Based on the sample size, the proposed IVkNN model presented 80% accuracy and 0.375 root mean square error (RMSE).

All the associations are genuine thoughts to receive the cloud computing administrations, seeing its advantages as far as cost, openness, accessibility, adaptability and profoundly computerized procedure of upgrading. Cloud computing upgrade the present capacities progressively without further speculation. Cloud computing is a band of assets, applications, and administrations. With the execution of cloud computing, associations have solid worries about the security of their information. The research focuses on the security issues of cloud services. The AES and RSA algorithms will be verified on five parameters key generation time, encryption execution time, memory usage, file uploading time and file downloading time. The CloudSim simulator will be used to find results that the RSA is performing much better than AES in terms of encryption execution time.

Water plays a vital role for the survival of life. We get water in different forms of precipitation and rain is the most beneficial of all types. The demand for water is very high throughout the Pakistan, especially where we have limited natural water resources. Summer monsoon season has profound impact in reducing water scarcity in Pakistan. Heavy rainfall results into floods whereas less rainfall creates drought which not only affects the economy but also puts humanlife in risk. The aim of this research is to manage the movement of clouds from heavy rainfall areas to those areas where there is paucity of water, through satellite using electromagnetic waves. To accommodate such a sensitive satellite data which is paramount to a country, it is required to have some special systems based on quantum mechanics that are more efficient and pregnable than conventional computers. The consequences will be very high of not securing weathermanagement system, implies to country level disasters. In this paper we first discuss the technology behind quantum computer then proposed a secure architecture by employing quantum computers to ensure the security of data transmission for weather management system.

Internet integrated sensor networks have gained much importance and exponential growth in wireless body area networks (WBAN) over the last few years. These networks are used in health services to remotely monitor the health of patients and send/receive sensitive, time critical medical data. Because of the wireless and broadcast nature of WBAN, it is easy for an adversary to get, inject, or update the information transmitted in the medium or launch many security attacks. To protect the sensitive data of the patient, we propose a secure communication strategy for different sensors to form a WBAN. Here, a new node ID is assigned for sensors by using public key cryptography to communicate in the network. An energy utilization and communication cost analysis show that our approach incurs less communication overhead as compared with the recently proposed secure solutions in WBAN. The complexity analysis show that the proposed model works efficiently for secure communication in WBAN.

The exponential increase of population in the urban areas has led to deforestation and industrialization thatgreatly affects the air quality. The polluted air affects the human health. Due to this concern, the prediction ofair quality has become a potential research area. For the assessment of air quality an important indicator is Air Quality Index (AQI). The objective of this paper is to build prediction models using supervised learning.Supervised Learning is broadly classified into: classification, regression and ensemble techniques. This study has been carried out using various techniques of classification, regression and ensemble learning. It has been observed from experimental work that Decision Trees from classification, Support Vector Regression from regression and Stacking Ensemble from ensemble techniques work more effectively and efficiently than the rest of the other techniques that fall under these categories.

Nowadays, eHealth service has become a booming area, which refers to computer-based health care and information delivery to improve health service locally, regionally and worldwide. An effective disease risk prediction model by analyzing electronic health data benefits not only to care a patient but also to provide services through the corresponding data-driven eHealth systems. In this paper, we particularly focus on predicting and analysing diabetes mellitus, an increasingly prevalent chronic disease that refers to a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. K-Nearest Neighbor (KNN) is one of the most popular and simplest machine learning techniques to build such a disease risk prediction model utilizing relevant health data. In order to achieve our goal, we present an optimal KNearest Neighbor (Opt-KNN) learning based prediction model based on patient’s habitual attributes in various dimensions. This approach determines the optimal number of neighbors with low error rate for providing better prediction outcome in the resultant model. The effectiveness of this machine learning eHealth model is examined by conducting experiments on the real-world diabetes mellitus data collected from medical hospitals.

Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.

In modern secure Wireless Sensor Networks (WSN), the sensor-nodes need extra energy owing to secure transmission of perceived information. So the energy-utilization of sensor-node should calculate while transfer the sensed-attributes securely to network. In this experimentation, we are proposing a revised Low Energy Adaptive Clustering Hierarchy (LEACH) protocol as LEATCH along secure information transmission (privacy and node authentication) in various levels using Quality of Protection Modeling Language (QoPML), which balance the Security-Energy trade-offs. This research experimentally analyzes the impact of data privacy, authentication operations on energy-utilization at sensor-node level while applying a LEACH & LEATCH. The obtained outcomes indicate the optimized LEATCH is outperforming correlated to the basic Leach with respect to minimal energy-utilization, time efficiency and expands life-time of modern-secure-WSNs.

Emerging technologies such as Cloud Computing, Internet of Things (IoT) and Big Data are developing a digital ecosystem. This ecosystem is catering diverse types and volumes of data that represents information segments. The essence of these segments become vital when transformed into knowledge units to provide a more meaningful and productive perspective. The transformed knowledge at this stage is heterogenetic in nature, consisting of functional and structural properties which needs to be arranged to formulate robust and efficient knowledge repositories. The heterogenetic knowledge can be transformed into classification clusters using structural properties by controlling the degree of heterogeneity. In this paper, Fuzzy Inference System (FIS) based classification approach is proposed for heterogenetic knowledge clustering.

In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients., Comment: 11 PAGES

This communication presents a novel system for Content Based Image Retrieval (CBIR) using Granulometry and Color Co-occurrence Features (CCF). These features are extracted directly from images using visual codebook. Relative distance measures are used to identify the similarity between the stored images and the query image. Results show that proposed method of using Granulometry and CCF is superior to most state of the art CBIR systems. The proposed system is tested on Wang image database that contains 1000 images having different categories. The performance of the system, quantified using the Average Precision Rate (APR), is very encouraging.

Nowadays LI-FI attracts the intention of the whole world. With in no time, the idea of LI-FI spread in the field of wireless communication technology. The working of LI-FI is purely based upon visible light communication in the form of LED. Due to VLC and LED LI-FI not only become an important part of the internet of things (IOT) but also internet of everything (IOE). LI-FI deal with a large amount of data rate, which is in the range of several Mbs to Gbs. Different parameters are affected by the performance of data rate. The proper arrangement of LED play part and parcel role in this new technology so there needs to investigate the novel architecture and flow control protocol in LI-FI system. The dimension of LED and the position of LED lead this technology towards high data rate. So, this paper presents the actual technique and novel architecture that tells us how to increase the data rate in front of different parameters.

Vehicular Ad-hoc NETworks (VANETs) is a special form of Mobile Ad-hoc NETworks (MANETs). The continuous developments in wireless networks have paved the way for the adoption of wireless data exchange for onroad vehicles. The application of VANETs is considered as a key component within the envisioned Intelligent Transportation Systems (ITS). One of the primary applications of VANETs is to enhance the safety measures for the vehicles on-road. Information about vehicles, such as vehicle speed and location coordinates are passed among vehicles to improve their awareness of each other. In VANETs, vehicles are referred to as nodes within a network where each node can act as both; end devise and router. The current design of VANETs has been identified with several drawbacks, while obtaining high performance in multi-hop message dissemination being as one of the most prominent issues. This problem need a solution to improve VANET's performance, especially over networks with high node densities. In this paper, we propose an ontologybased approach for multi-hop message dissemination over VANETs as a solution that would address the above highlighted issue.

As mobile technology is becoming more advance, the uses of this technology is increasing day by day. The mobile phone is used by everyone and it became the necessity of life. Today, smart devices are flooding the internet with data that are everywhere and in any form. Crowd sensing is a new sensing model which depends on the strength of mobile devices. One of the key challenges in mobile crowd sensing system is multiple selections of participants with low priority to perform tasks. This research work presents the concept of Crowd sensing along with the raking process of participants from a large user pool. This article provides the efficient raking process of participants to assign the priorities for performing tasks in smooth manner.

Underwater wireless sensor networks (UWSNs) are intended to be positioned at the areas that need to be supervised constantly deprived of the human interaction. Consequently, these networks are likely to stay active for a longer duration. Though, sensor nodes in UWSNs are equipped with finite energy resources. Furthermore, congestion is one of the biggest challenges in UWSNs because it leads to data packet loss and ultimately shorten network lifetime. In this paper, a congestion control model in UWSNs with congestion control is considered. A routing protocol is purposed which will be a modification of current UWSNs protocols such as RACE and ARCUN by presenting their attributes with congestion control mechanism. We will deliver some comparisons between the proposed scheme and the existing schemes and prove that this new scheme with congestion control has better performance than the existing ones.