Sentimental Analysis is a process of computing and categorizing the expressed opinions of people about certain event, subject or product as positive, negative or neutral. The major objective of Sentimental Analysis is to help data-driven decisions using insights from replies in social media, surveys and product reviews. Sentiment Analysis can be done with words, sentences, documents, features or aspects, concepts, phrases, links, clauses and implications. Recently, there has been a lot of attention on sentiment analysis especially from researchers in the fields of text mining and natural language processing. But due to extreme absence of annotated datasets which are used to train models in various domains, the accuracy of sentiment analysis has been hindered. Many types of research have been done to confront the challenge and enhance sentiment analysis classification. Sentiment analysis is important as it helps in identifying the emotional and attitude states of people. Positive or negative feelings of people can be expressed in different ways. This research article talks about, in subtle terms, the different ways to deal with sentiment analysis mostly in Machine Learning, Lexicon-based, Hybrid and Ontology-based approaches. This research article gives point by point perspective of the distinctive applications and challenges of Sentiment Analysis.
Data generated by the devices and the users in modern times is high in volume and variable in structure. Collectively termed as Big Data, it is difficult to store and process using traditional processing tools. Traditional systems store data on physical servers or cloud resulting in higher cost and space complexity. In this paper, we provide a survey of various state-of-the-art research works done to handle the inefficient storage problem of Big Data. We have provided comparative literature to compare existing works to handle Big Data. As a solution to the problem encountered, we propose to split the Big Data into small chunks and provide each chunk to a different cluster for removing the redundant data and compressing it. Once every cluster has completed its task, the data chunks are combined back and stored on the cloud as compared to physical servers. This effectively reduces storage space and achieves parallel processing, thereby decreasing the processing time for very large data sets.
Within the modern patterns, cloud computing valuable asset clients conceded to pay and utilize mannequins with the aid of getting to all the correct agencies furnished by means of the providers. It without a doubt offers with the possibility of throughout, the whole lot, and whenever management on hobby for stop clients. Builders with modern-day ideas for beginner net suppliers are not looking for the wonderful capital costs in equipment to carry their administration or the human fee to oversee it. The modern-day internet transforms into the irresistible expert to the internet of troubles (IoT) as certificate rising global. For the duration of this IoT as a help broadened in essence substances. Every and every concern of examination figuring is appreciating an important role and its research is ascending with a massive assortment. Each and everyone is speaking about the cutting-edge propensities of dispensed computing. The primary downside on this spot is that there isn’t always a normalization altogether that now not everyone getting into the indistinguishable level. There sq. Diploma many guides by that it’s planned, the scope of them sq. Degree in keeping with the accompanying: characteristic non-compulsory, protection offerings, algorithmic, Mathematical, and so on The Simulation of the effort is completed at spans the product used for the association of reasoning system. The activity gives with the IoT suppliers supplied by using the IoT specialist co-op to the person is that extraordinary amongst other users with the boundaries and metaphysics method.
Industrial developments in the Internet of Things (IoT) have lined the way for new fields for the use of wireless sensor network (WSN) technologies. Agricultural monitoring is a case in point where IoT can help improve production, quality and output yield. The use of WSN and data mining techniques will significantly improve many of the agricultural activities. One such activity is the management of the amount of water in planted fields. In addition, during recent years, WSN has become a more evolving field in precision farming. The key problem in the development of WSN is the use of energy and the improvement of the life of the nodes. This paper provides a systematic analysis of the clustering protocols based on soft computing approaches that are used in the agricultural domain to increase WSN‟s lifetime. Classification is carried out according to different soft computing techniques: swarm intelligence, genetic algorithm, fuzzy logic, neural networks. The survey will then present a comparative analysis of soft computing techniques with a focus on their goals along with their merits and drawbacks. This survey enables the researchers to choose the suitable soft computing technique used by clustering protocols for WSN-based precision agriculture.
A focused crawler goes through the world wide web and selects out those pages that are apropos to a predefined topic and neglects those pages that are not matter of interest. It collects the domain specific documents and is considered as one of the most important ways to gather information. However, centralized crawlers are not adequate to spider meaningful and relevant portions of the Web. A crawler which is scalable and which is good at load balancing can improve the overall performance. Therefore, with the size of web pages increasing over internet day by day, in order to download the pages efficiently in terms of time and increase the coverage of crawlers distributed web crawling is of prime importance. This paper describes about different semantic and non-semantic web crawler architectures: broadly classifying them into Nonsemantic (Serial, Parallel and Distributed) and Semantic (Distributed and focused). An implementation of all the aforementioned types is done using the various libraries provided by Python 3, and a comparative analysis is done among them. The purpose of this paper is to outline how different processes can be run parallelly and on a distributed system and how all these interact with each other using shared variables and message passing algorithms.
Better information management is the key to a more intelligent DH. every country has a lot of information related to their heritage and culture in the form of manuscripts. To retrieve the science and heritage information from these manuscripts is time consuming and people dont have interest in them. DH is putting its efforts to retrieve the scientific information [1]. In this direction, many challenges must be first overcome. Enabling seamless, effective and efficient access to the various data sets and novel methods for exploiting from ancient manuscripts. This workshop aims to bring together an interdisciplinary audience interested in the fields of semantic web, data management and ancient manuscripts to discuss the unique challenges in Text — Object relationship establishment [2] We are doing hands on training for data management and to propose novel and practical solutions for the preservation of ancient heritage and culture to the next generation. As semantic technologies are currently widely exploited more and more for the management of DH data, new challenges occur while processing different kinds of ancient manuscripts data that dictate new solutions. Establishing Text and object relations is one of the challenges.
Social media such as Twitter, Facebook, Google plus, Reddit, Tumblr have been a widely used platform for people to communicate, share views and feelings with others freely. The information obtained from this short text messages helps in predicting their emotions, views, sentiment, opinion and it is applied in different fields like marketing, election, product review, sentiment analysis, emotion detection etc. Behavioral analysis from text data is another widely popular field. This paper gives an analysis of global word representations and overview of the work done on depression detection related tasks. Major steps such as preprocessing of data, feature extraction, representation and classification methods are summarized.
Routing protocols function as the obligatory force in MANETs to transfer data outside the physical wireless ranges of the nodes. In hierarchical cluster based routing; cluster head nodes and gateway nodes alone participate in routing decisions. Those nodes may fail to cooperate during route discovery due to selfish or malicious grounds. Hence, imposing cooperation among nodes in MANET to employ a secure route becomes an extremely significant issue. Cryptographic mechanisms can be used, but it acquires a high computational cost and may not categorize the nodes with malicious intention. Therefore, we proposed a dual cluster head based trust aware mechanism as an alternative to cryptographic technique to protect forwarded packets from malicious nodes. Our proposed protocol TWCBRP classifies the network into one hop overlapping clusters with primary and secondary cluster heads, which are accountable for conducting all the routing activities. It constantly assurances the trustworthiness of cluster heads by replacing primary with secondary cluster head, as soon as the former becomes malicious. Cluster members send routing packets only through trusted cluster heads and gateway nodes thus guaranteeing a secure path. The performance of TWCBRP is evaluated with Network Simulator2 and illustrates better performance in terms of packet delivery ratio, throughput, delay, and control overhead when compared to a distributed weighted cluster based protocol (CBPMD).
In this work, identification of diseases present in the plant of rice is carried out using methods of Deep Neural Network. So as to achieve image accession, a dataset having 2212 leaf images with different diseases is used. In this work, the entire dataset is divided into two classes in which class 1 contains the healthy leaves and the other class contains infected leaves. The identification is done using VGG-19, LeNet5, and MobileNet-V2predefined Convolutional Neural Network (CNN) models that own a fixed number of Convolutional layers and also the layers that are connected completely also known as fully connected layers. The architecture is designed as per the details for the LeNet5 model while for the other two methods that is MobileNet-V2 and VGG-19, the architecture is directly imported from some predefined libraries which are ready to use, and further, they are used according to the author’s requirement. Once the experiment was completed successfully, it was observed that the accuracy achieved of VGG-19, LeNet5, and MobileNet-V2 was 77.09 %, 76.63 %, and 76.92 %respectively.
: Internet of Things (IoT) are the first-class citizen of the 4th industrial revolution often called Industry 4.0. Security is of utmost concern when deploying IoT infrastructures. A majority of such deployments are centralized in nature and are much prone to several attacks such as DoS or DDoS, insider threats, etc. and are also vulnerable to a single point of failure. Such deployments rely on centralized cloud servers for identification, authentication, and storage of data generated by the IoT devices. Centralized architectures are in use for a long time with small to medium size IoT networks however, it may not be able to handle the requirements of the large-scale networks with billions of devices of tomorrow. Also, such systems lack transparency as users or organizations need to trust the way the cloud services store and use their data. Distributed technologies such as Blockchain along with Smart Contracts with the power of secure cryptography can help to solve a number of such challenges for better auditing, increased fault tolerance, and improve the overall security of such deployments. Integration of Blockchains with IoT based systems is still in its early phases and knowledge about them will give a researcher, or an industry practitioner an edge over others.
Stemming is a procedure used to reduce the inflected words by removing affixes from them. It is widely used in Natural Language Processing systems developed for various languages like English, Hindi etc. Punjabi is a low resource availability language spoken in northern regions of India and Pakistan. In Pakistan, Shahmukhi script is used to write Punjabi. While in India, Gurmukhi script is used. Various approaches are used for stemming natural language words such as Brute Force approach, Rule based approach, Statistical approach etc. In Gurmukhi scripted Punjabi language stemming, either the Brute Force approach or Rule based approach or a combination of both of these approaches have been used till now. This paper presents a comparative analysis of various stemmers developed so far for stemming Gurmukhi scripted Punjabi language words based on their methodology and accuracy. It will motivate further research in developing more efficient stemmers for Punjabi language. It will benefit the researchers to compare and understand the used approaches and the problems faced in adopting each approach.
The Internet of Things (IoT) is a the most recent trend of Internet technology which is turning vehicles into the group of an entire ecosystem of connected IoT services. IoT in the vehicular sector offers a new experience for the drivers as provides enhanced safety and security, and a new range of product offerings. Identifying the potentially risky behaviors of driving is the most important roles in avoiding dangerous traffic situations. IoT based systems are now used for vehicle and fleet tracking, fault and error detection, analyzing driver behavior and many other operational needs. The IoT protocols allow us monitoring and controlling remotely, over an existing network which results in improving accuracy, efficiency and security of the system. This paper describes the developments and evolution of Internet of Things in the automotive sector to provide diagnostic system which is intelligent diagnostic system based on IoT.
Identification of plants is a very important field in the earth’s ecology to maintain a healthy atmosphere. Certain of these plants have significant medicinal properties. Nowadays of finding a plant is not easy by looking at its physical properties. This paper provides an academic database of literature between the duration of 2015–2020. It has been observed that the new generation of convolutionary neural networks (CNNs) in the space area of image recognition has produced remarkable performance. In this paper, techniques are discussed the concepts of Deep learning and different leaf recognition methods.
Bipolar disorder is a mental ailment caused by maximal mood swings with emotional highs and lows. Nowadays, this has become the most common abnormality related to mental health and furthermore it is ignored by people of all age groups. Bipolar disease is generally heritable but not all siblings of the family will be having it though, and will be having same genetics and the factors which can be risky. Here we use random forest algorithm, along with the Magnetic Resonance Imaging (MRI) information. The utility of these irregularities in recognizing individual bipolar disorder patients from state of mind issue or health controls define patients dependent on their illness. Here we use machine learning algorithms like Random forest algorithm and CNN-mdrp(multimodal disease risk prediction) for the accuracy .We give the risk factor and stage of the healthy patient with the attributes we collected from the MRI. We use a trained dataset and machine learning algorithms mentioned above to get the output. Voxel-Based Morphometry (VBM) will be used to dividing and pre-processing the MRI information obtained. To see the changes in Gray Matter (GM) and White Matter (WM) of the different data groups individually, a simple equation is use and also the Principle Component Analysis will be used and The project gives you the output showing that CNN MDRP with random forest has high accuracy than other algorithms in bipolar disease prediction.
Data sets published for public consumption related to governance typically contain common metadata This metadata normally describes the publishing organisation, the domain, the administrative region and a topic that the data best describes A relevant semantic vocabulary will not only facilitate linking the metadata through meaningful contexts but also enable grounding related information from diverse public data sets using semantic entities defining the metadata In this paper, we present Bharathi -Linked Data Vocabulary for the Indian context Bharathi contains information regarding government organisations at the union & state government level, administrative regions, sectors, sub sectors and common topics used frequently in the vocabulary of the government functions Further, Bharathi contains links to other open vocabularies such as GeoNames for geographical locations The schema of Bharathi uses existing established ontologies making it inter-operable and extensible We describe Bharathi along with a live use case of its application in accomplishing a semantic Covid19 data analysis for India © 2021 CEUR-WS All rights reserved
When a plaintiff or a defendant finds themselves engaged in a legal case, they often turn to legal practitioners for getting perspective on their legal standing and how the argument and conflicts play a part in the court’s decision making. Since much of the past legal proceedings are accessible online, many individuals seeking legal counsel use a search engine to obtain some knowledge of related historical cases on their own. In this research, we present a conceptual framework based generation of legal case perspectives for further case planning. For previous legal proceedings, we construct conceptual frameworks led by a surrogate template generic to all court cases. We generate concept classifications and indications using these conceptual frameworks, used later in the text search to extract relevant text spans to generate numerical analyses for getting perspective on the historical data. We provide users with a case characterization system, in which users select case elements that apply to their situation. We construct the numerical perspective evaluation based on case characterization by parsing the text from historical cases. We explain our system using two case studies: Divorce and Parental alienation cases demonstrating the effectiveness of the system in case planning.
Coronavirus disease (COVID-19) is a severe pandemic infectious virus that enters into healthy cells of a living body COVID-19 virus makes copies in the organs of the host body by multiplying itself which ultimately leads to the death of some healthy cells and therefore weakens the immune system In a mild stage, it mainly affects the respiratory tract and leads to pneumonia, organ failure, and death reaching the last stage This paper focused on the early detection of the COVID-19 patient based on the positive symptoms of the disease In this paper, the COVID-19 Remedies (CURE) scheme is proposed based on machine learning prediction models for the treatment of COVID patients For experimental results, the performance analysis of the CURE scheme is evaluated on the Python platform which is tested using the Kaggle dataset from Johns Hopkins University © 2021 CEUR-WS All rights reserved
Nowadays ontologies are used in everywhere and provide a reusable piece of knowledge about a specific domain. However, those pieces of knowledge are not static and change over the time in order to fulfil the requirements of the different task. So, it is essential that changes in ontologies should be managed very well. Ontology versioning mechanism is used to keep the track of the ontology changes via making the relationship with previous version of the ontologies. Many ontologies encode reality by representing ontological concept as a knowledge unit. Till the date, no work has been started towards to solve the ontology versioning problem when ontological concept store based on the idea of knowledge unit. To overcome this problem, we present an ontology versioning framework which is capable to maintain the relationship among different version of ontology explicit. We show operational analysis of the proposed work for the better understanding about ontology versioning framework.
Clinical decisions are considered crucial and lifesaving. At times, healthcare workers are overworked and there could be lapses in judgements or decisions that could lead to tragic consequences. The clinical decision support systems are very important to assist heath workers. But in spite of a lot of effort in building a perfect system for clinical decision support, such a system is yet to see the light of day. Any clinical decision support system is as good as its knowledgebase. So, the knowledgebase should be consistently maintained with updated knowledge available in medical literature. The challenge in doing it lies in the fact that there is huge amount of data in the web in varied format. A method of knowledgebase curation is proposed in the article using RDF Knowledge Graph and SPARQL queries.
The reasoner is the mechanism for interpreting the semantics of web ontology language. This paper focuses on reasoner performance study and predicting it by use of machine learning. Reasoner evaluation is very challenging as reasoner’s efficiency may vary on different ontologies with the same complexity level. Different reasoners give different inference for the same ontology. Thus, reasoner could be enhanced for some however not for all ontologies. Here, paper focus on reasoner performance variability of reasoner and how ontology features affect reasoner performance. The main goal is to provide simple, efficiently computable guidelines to users. For prediction, supervised machine learning is used as a machine learning technique which help us to capture these dependencies. First introduced a new collection of efficiently computable ontology features, that characterize the design quality of an OWL ontology. Second, modeling of two learning problems: first, predicting the overall empirical hardness of OWL ontologies regarding a group of reasoners; and then, anticipating single reasoner robustness when inferring ontologies under some online usage constraints. To fulfill this goal, a generic learning framework is used, which integrates the introduced ontology features. The framework employs a rich set of machine learning models and feature selection methods. Furthermore, we used multi-label learning by analyzing the learned models unveiled a set of crucial ontology features likely to alter the empirical reasoner robustness.
