bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge graph augmented advanced learning models for commonsense reasoning
Authors: Pothuri Akhilesh , Raghuram Veeramallu Hari Sai , Malik Pooja
Published: January 2021

Machine learning is the key solution to many AI issues, but learning models rely heavily on specific training data. While a Bayesian setup can be used to incorporate some learning patterns with previous knowledge, those patterns can not access any organized world knowledge on requirements. The primary objective is to enable human-capable machines in ordinary everyday circumstances to estimate and make presumptions. In this paper we propose to respond to such common sense issues through a textual inference system with external, organized common sense graphs for explanatory inferences. The framework is based on a schematic map as a pair of questions and answers, a linked subgraph from the semantine to the symbolic space of knowledge-based external information. It displays a schematic map with a new network graphic module for information knowledge and performance with graph representations. LSTMs and graphical networks with a hierarchical attention-based direction are the basis of our model. It is flexible and understandable from the intermediate attention scores, leading to confident results. We also achieved state-of-the-art reliability on CommonsenseQA, a broad database of common sense reasoning utilizing ConceptNet as the only external tool for BERT-based models.
Brief Study to Explore Trust and Security Challenges in Cloud Computing
Authors: Singh Amanpreet , Kaur Amandeep , Gupta Deepali
Published: January 2021

Cloud Computing is an online strategy for dynamic allocation of services by reducing the usage of hardware and software resources. Existing models of cloud computing include different components: end-user computers, contact networks, access control frameworks, and software infrastructures. The data and the cloud infrastructure must be protected from any known and unknown attacks on all cloud platforms to achieve comprehensive cloud security. Trust issues arise from uncertainty about certain actions that can be defined through the concept of fuzzy belief. Trust is faith in quality provided by the cloud service provider and third-party agent on consumers and vice-versa where quality can be considered in terms of functionality, security, privacy, data safety, and reliability on cloud resources. Although the members of a social network have a strong level of trust, this trust may not be adequate in certain cases and becomes an obstacle in the successful implementation of cloud services. For example, a storage facility where users are vulnerable to missing, infected, or corrupted data, while providers are at risk of unknown data being hosted from their network. The relationship between two business partners decays more rapidly relative to the trust of users in the social network of the cloud. This paper aims to address the trust issues in existing cloud paradigms and highlights the existing flaws which will be resolved in the future course of action.
Image Retrieval System-An Integrated Approach
Authors: Chugh Himani , Gupta Sheifali , Garg Meenu
Published: January 2021

With the development of multimedia technology, the usage of large image database becomes possible and is rapidly increasing. These can be used for the purpose of retrieving files, which works on the mechanism of image search. Different databases are available on different websites like Instagram, Facebook, Twitter, Flickr, and Picasa. This paper shows the advantage of content-based image retrieval system, as well as its key technologies. Comparing to the shortcoming, only certain feature are used in the traditional system. This paper presents a review on different techniques of image retrieval techniques which are based on color, texture and shape of images. It also focuses on the feature extraction and representation, several commonly used algorithms and different methods used for matching of images.
Ensemble classifier to support decisions on soil classification
Authors: Motia Sanjay , Reddy SRN
Published: January 2021

Soil performs a significant role in the agricultural ecosystem by supplying essential nutrients and a conducive environment for plants’ growth and crop yield. Inside the agribusiness space, the soil classification is a crucial work that gives good classification results for different soil types. The taxonomy provides an excellent rating for inherent soil elements. This work investigates the accuracy of three well-known classification models like K-Nearest Neighbor (k-NN), Naive Bayes (NB) and, Decision Tree (DT) using a publically available agricultural soil dataset. Post investigation, an Ensemble Classifier (EC) is proposed by fusing the above mentioned three classifiers. The experimental results indicate that EC has the highest accuracy of 84% in comparison to the NB (72.90%), k-NN (73.56%), and DT (80.84%). So it performs better than the other classifiers. The results infer that EC would be useful for accurate classification of soil types in the agricultural domain.
Comparative Study Of Efficient Data Hiding Techniques
Authors: kumar Lalan , Mishra Deepanjali , Kumar Ayush , Kumar Amit , Bajpai Ashish
Published: January 2021

Data security is one of the fundamental concerns when it comes to modern networking and information sharing as any breach or data loss can end up causing major losses in terms of credibility, personal data and/or law suites. This paper studies data encryption and decryption techniques using algorithms like DES algorithm, 3 DES algorithm, AES algorithm and RSA algorithm. Cryptography was introduced for data security while transmitting data. However, only cryptography cannot give a robust security approach; as a result, a disorganized message can still be snooped [9]. This leads to a desire for information security. There are several techniques available, among those have taken a few of them and will review them considering cryptography measures. We will analyse the algorithm mentioned above, and try to find out better among them.
Secure IoT via Blockchain
Authors: Garg Ruchi , Gupta Poonam , Kaur Amandeep
Published: January 2021

Block chain technology had gained popularity because of its use in crypto-currencies like bitcoin. Today uses of blockchain are growing in number of areas like banking, industries, health centers and even security of IOT. Moreover, the use of IOT is growing exponentially every year with its aim in 5G technologies like e- health, smart homes, distributed intelligence etc. but it faces challenges in security and privacy. The privacy of a user data is at a risk because of its (i.e. IoT) centralized client-server model. This centralized approach of the server poses a serious vulnerability to the data security. This data at the server attracts the attackers to enter into the network and invade through the data and schedule attacks or inject a malware. It indicates that the central architecture of IoT possess a compromised confidentiality, integrity, and security of data which disrupts its use as the widespread adoption of this technology. Therefore, it is essential to evade the hostile centralized server architecture for IoT to enhance its security. It implies a need for decentralized architecture to maintain the data. The data can be kept at the different users without any central control with the help of blocks as suggested by Blockchain. This paper addresses the various security issues in IOT and how block chain helps in solving these issues.
Analysis and visualization of the pandemics using Artificial Intelligence
Authors: Srivastava Shagun , Prithivi P P R , Srija K , Vaishnavi P. , Savitha H S S , Grover Alka , Saxena Mohit , Chandra Shivani , Saxena Ankur
Published: January 2021

Human race has faced many epidemics and pandemics in past. The trajectory reveals that there is a pandemic almost every century. Our generation has witnessed the outbreak of coronavirus disease (COVID-19) pandemic, which turned out to be largest pandemic ever. Viruses have affected global population in the past century can answer the questions of the post Corona trauma. It has been noticed that most of the epidemics and pandemics are inflicted by the influenza viruses. Scientists have elucidated the structures of the viruses that have infected earlier. The homology between them and COVID-19 can be studied in order to fight the virus. In this paper, journey of novel corona virus has been discussed from a simple flu to become a pandemic within a short span of time. All the factors including the major countries responsible for spreading the pandemic owing to their volume, speed of human travel and socio-economic reasons seems to be the reason according to the review. We also compared the structures of the epidemic and pandemic causing viruses. Some of the COVID-19 studies mentioned which used machine learning models, and then by the use of machine learning we analyzed characteristics of three diseases (H1N1, COVID-19, and SARS) to obtain the plots of the autocorrelation against the lag of the number of confirmed cases, recovered cases, and deaths of/caused by COVID-19.
Energy Aware Metaheuristic Approaches to Virtual Machine Migration in Cloud Computing
Authors: Gupta Gifty , Mangla Neeraj
Published: January 2021

In mandate to drop the power utilization, virtual machine migration is utilized for cloud computing. At present many algorithms are there, that diminishing the energy consumption with the assistance of virtual machine migration and power proficient planning of VMs. The target to migrate the virtual machine from one under-burden server to other over-burden server is to have ideal outcomes. Over-burden or Under-burden host detection strategy utilized as a part of proposed work is Inter Quartile Range. After detection of an Over-burden or Under-burden host, the following stage is to pick the specific virtual machines to relocate from one under-burden host to the over-burden host. VM choice arrangements Minimum Migration Time (MMT) along with various Meta heuristic optimizations are executed. For assessing proposed algorithm, CloudSim is used to assess and look at the execution of proposed algorithm. To analyze the effectiveness of planned system seven parameters the power utilization, standard deviation of host, standard deviation of virtual machine placement, standard deviation of virtual machine migration, SLA performance degradation due to migration, and total SLA violation are considered.
IoT Based an Eye-Ware to Assist in Ocular Communication
Authors: Verma Vishal , Gupta Deepali , Gupta Sheifali , Chauhan Harsha
Published: January 2021

This paper is completely focused on the difficulties faced by the paralyzed patients and the today technology help these patients to relief them. Currently the physical therapy, occupational therapy, respiratory therapy and the speech therapy are used to treat the patient who is suffers from paralysis. There is need of the device with this the paralyzed person convey his feelings like pain, hunger and loo. In this paper author purposed an electronic googles that can help the paralyzed person to convey his feelings using the eye blink pattern. The infrared sensor (IR) pair are used to detect the eye blinking pattern. When the IR rays strike to pupil it, large intensity of IR rays is reflected back. When the eye lashes are closed the less amount of IR radiation is reflected back. Two pair of infrared sensors are installed on the both glasses of the google. The blink pattern is pre-programmed in the microcontroller. ESP8266 microcontroller is used to process the all data coming from IR sensor. When the blink pattern is detected the message is sent to the care taker through internet connection.
NER based Biomedical Entities Association Extraction Using Transfer Learning Technique
Authors: Kumar Ashutosh , Sharaff Aakanksha
Published: January 2021

Deciphering the interaction of gene-disease is a key step in the development of therapeutic disease control strategies. The identification of the gene-disease associations by experimental methods, like genome-wide association studies and linking analyses, may be costly and time-consuming. Consequently, various silico methods were developed with different approaches to predict associations between gene and disease. A deep neural network based on attention mechanism has been proposed for the prediction of semantic linkage between biomedical terms. The proposed method uses a paradigm based on transfer learning, which enhances the prediction performance considerably. The main key feature of the proposed approach is, it is not dependent on any handcrafted features on sentence information. Sentence information is converted into embedding vectors. Extensive evaluations show that the state-of-the-art domain of the proposed approach achieves competitive performance, although no domains specific biomedical features are needed. The proposed methodology for the gene-disease association based on the deep learning approach, shows promising results of 78.10% of F-score on Genetic Association Databases (GAD) corpora.
Recommender System: A bibliometric analysis
Authors: Sharma Sheetal , Gupta Kamali , Gupta Deepali
Published: January 2021

The exponential growth in the online share of businesses has to lead to a gigantic wave of options available to the active user. Recommender systems, therefore assist the users to go through the tailored list of products to match their preferences. A range of recommender systems is available to serve the purpose. This article will navigate through the basic of recommender systems, and its classifications types viz. collaborative filtering, content-based filtering, demographic, hybrid, and knowledge-based recommender system. It aims to analyze publications of the Scopus database using biblioshiny tool of RStudio software. A bibliometric analysis is conducted on 556 papers to analyze the recent research trends in recommendation systems. Further, challenges have also been discussed that need to be dealt with the recommender system.
Analytical Comparison of Cloud Data Centre Services and Cost
Authors: Rayjada H , Shukla P
Published: January 2021

Comparing with the traditional data centre, cloud computing makes it easier for enterprises to scale their services and lowers the cost of access for smaller companies. The cost comparison between cloud computing and the old-style data centre is an important issue of concern. In this paper, the cost roles traditional cloud data centre and server room are settled up, the performance of the data centre and cloud computing were tested based on the compared with the data centre with low workload intensity strength. More than a few data centre have a primary backup of data and servers. The cloud data centre provider like Amazon, Google Facebook data centre are called Availability zones. The availability zones are a within the same region, distinct colocation centre is connected to Virtual Private Cloud network. This research discovering new network architectures for the data centre. In this paper, we considerate the among different data centre charges and network architectures. the comparison on cost using current and predicted trends in data centre cost and power consumption.
Analysis and Comparison of Different Blockchain Algorithms in IoT Security
Authors: Parmar Monika , Kumar Neeraj , Kaur Harsimran Jit , Sharma Abha , Sharma Sandhya , Sandhu Mamatha
Published: January 2021

Internet of things (IoT) and Blockchain are two advancements that are picking up notoriety since the hour of their creation. Sooner rather than later, IoT is going to impact everything we use in our daily lives. As the utilization of this innovation expands, the danger to misuse it likewise increments. Existing advancements are insufficient to manage this. Along these lines, Blockchain has risen as a successful answer for understanding the security issues identified with IoT. Information put away in Blockchain is permanent and can’t be changed very effectively. Likewise, the information is added to the block only after it is affirmed by everybody in the system, and in this manner, it is permitting secure exchanges. The individuals who approve the exchanges or transactions and include them in a block are known as miners or excavators. In this article, a detailed overview of the different Blockchain algorithms that are being used for IoT security has been explored. Also, an attempt has been made to provide a detailed overview regarding Blockchain which can act as a reference for the researchers who wish to pursue research in the Blockchain domain.
IoT in Building Smart Cities and Smart Environment: Your Way to Success
Authors: Kumar Mukesh , Mehta Gaurav , Nayar Nandini , Gupta Manik
Published: January 2021

Internet of Things has given us a new epoch of computing technology. As we know that Internet, a rebellion innovation has transformed everything, Internet of Things has made a hope for excellent future of Internet with Machine-Machine type of communication in addition. Thoughts of creation that correlates a different means of considering the events with invented concepts that avail the possibilities proposed by new approaches in intelligent ways. Framing an intelligent environment depends on smart education system, smart traffic management system, smart vehicle management system, smart waste management system, industrial control, smart agriculture etc. Today, safety on roads, cleanliness in residential areas, security at home has become an essential component in our daily life. So there is a necessity to avail new approach of innovative technologies based on Internet of Things to solve the environmental problems. In this paper we surveyed some of these problems and suggested various executable plans also. The actualization of smart sensing and transferring of data has offered a significant assurance for building the blueprints. This review paper has also shown that it is possible and affordable to construct these smart systems based on Internet of Things (IoT). We have studied various blueprints which can sense unforeseen happenings such as landslides, traffic jam, accidents, etc. using different sensors and demonstrate the facts accumulated on LED display. Observational outcomes have shown good agreement with the hypothetical statements.
Secured Communication in Vehicular Adhoc Networks (VANETs) using Blockchain
Authors: More Shivaprasad , Sonkamble Rahul , Naik Udaykumar , Phansalkar Shraddha , More Priyanka , Saini Baljit Singh
Published: January 2021

In the last decade, the vehicular ad-hoc network (VANET) field is growing drastically. In VANET the communication is the most important aspect through which the data transmission is taking place. Data transmission is maybe related to data in the form of multimedia messages, notifications, announcements, or some warning messages that are involved in VANETs. Data transmission among different vehicles would lead to an exchange of audio-video files in the formed network. These multimedia messages should be transmitted in the fraction of second as this network is built for temporary communication. Vehicles involved in this communication should be trustworthy otherwise other vehicles those who are part of this network would be misguided due to intruder present in the network. To achieve security and integrity in the constituted network, system has proposed the blockchain security environment as a part of this proposed system. Blockchain induces high-end communication in the ad-hoc network. It also enhances the overall security aspect of the constituted network. In this paper, we have proposed blockchain-based security system for vehicular communication that handles this communication securely in a well efficient manner. The performance of the proposed system will be measured based on parameters like end-to-end delay, reliability, and packet delivery ratio.
Cloud computing: a walk through in models, challenges, and energy solutions
Authors: Gupta N , Gupta K , Singh A , Kaur A , Gupta D
Published: January 2021

The paradigm of cloud computing in recent times has engrossed a lot of working populace in its service stack offerings. This considerable rise of human traffic landing onto cloud platform for service extraction has consequently led to exponential rise in energy consumption during dynamic and heterogeneous workload situations encompassing huge cloud infrastructure in form of CPU, Hard Disks, network devices etc. The energy released not only poses costs on its stakeholders but is affecting the environment due to rise in carbon footprints. As per available stats [1], the total power consumption of cloud has significantly arisen from 632 billion kilowatt hours in year 2007 to 163 billion kilowatt hours in 2020 which is adequate to leave a devastating effect on environment. Though research is being carried out in this direction, still efficacious measures are far away from its pragmatic implementations. Therefore, the research study conducted here discusses about existing solutions in the direction of energy management in cloud computing. The latter half of paper presents abrief discussion on gaps and future directions.
Heart disease prediction using machine learning algorithms
Authors: Jindal Harshit , Agrawal Sarthak , Khera Rishabh , Jain Rachna , Nagrath Preeti
Published: January 2021

Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease It is implemented on the.pynb format.
Detection of Prostate Cancer Using Deep Learning Framework
Authors: Patel Abhishek , Singh Sanjay Kumar , Khamparia Aditya
Published: January 2021

Recent studies in Prostate Cancer signifies as magnetic resonance imaging targets to biopsy shows more enhanced result. The systematic study of Medline, Embase, Scopus, Cochrane helps in meta-analysis. Prostate specific antigen is obtained from curative radiotherapy. Prostate-specific membrane antigen positron emission tomography helps to localize recurrence prostate cancer whether it has increased. Prostate specific antigen rising helps to identify and prompted the reason of Prostate-specific membrane antigen positron emission tomography imagining. angiogenesis play an important role for diagnosis noninvasive cancer with technique contrast-enhanced ultrasound. Prostate Cancer accuracy were determined by MRI-targeted biopsy and the transrectal ultrasound-guided biopsy.
Body Sensor Networks Architecture and security issues in Healthcare application
Authors: Kumar Vinod , Badal Neelendra , Mishra Rajesh
Published: January 2021

This paper exhibits the idea of Body Sensor Systems (BSNs) which can be broadly utilized in healthcare applications and all. It investigates systems and related applications for medical issues and discussed vital challenges and metrics of BSNs. Of late, miniaturized health observing gadgets have turned out to be essentially plausible with other current advances in the Wireless Sensor Networks (WSNs) and Embedded Designing technologies for computing. However, BSN provides the means to analyze the low cost which can sense the existing environment as well as their wireless nature for which it turns out to be suitable to develop the subject. Sooner or later, the reconciliation of a huge swath of Wireless Network into existing specific medical technology and so forth can be seen. These kinds of systems, by and large, comprise radio frequency-based correspondence abilities. Every sensor in a sensor network comprises of the three kinds of subsystems: first is the sensor subsystem which can sense the environment, the second subsystem helps to apply the calculations on the detected information as well as the third subsystem handles the exchanging of the messages with their neighboring sensors. Wireless Sensor Networks (WSNs) consist of small wireless computers that sense, process and impart environmental stimuli including temperature, etc.
Evaluation of Bernstein Polynomial as a Machine Learning technique
Authors: Wadhwa Deekshant , Singh Nitesh Kumar , Jatana Nishtha
Published: January 2021

Regression algorithms are used to device a function to predict the output based on that function for new unseen inputs. We have used Bernstein polynomial as a type of approximation curve to fit a polynomial between all features of a dataset so as to perform regression on it. As approximation curves do not generally meet most of the input points, which are used to create it, hence the chances of it over-fitting to a model are low. In this paper, we have used Bernstein polynomial separately for each feature versus output graph, assuming that each feature in the dataset is independent of each other and combines the outcome of each curve to predict the final result.
Symptomatically Brain Tumor Detection Using Convolutional Neural Networks
Authors: Totakura Varun , Madhusudhana Reddy E. , Vuribindi Bhargava Reddy
Published: January 2021

In the human body, the most important and the complex organs work with billions of cells in the brain. The abnormal growth or uncontrolled division of cells around the brain will cause a brain tumor. These group of cells which affect the functioning of the brain and also destroys the human cells. In the olden days, the detection of brain tumors is way much harder than nowadays. The usage of modern computer vision techniques has made the detection to be more accurate and easy. In this paper, firstly the detection of tumor in the brain was performed using a Sequential Neural Network (SNN) model which classifies the symptoms, as the brain tumor and then Magnetic Resonance Images (MRI) Scans are used for the further confirmation. The SNN model has an accuracy of 99.36% whereas the Convolutional Neural Network (CNN) Model used in this paper is 99.89% accurate.
A Data Mining Inspired Methodology towards the Identification of Diabetic Retinopathy
Authors: Pradeep Kumar KG , Karunakara K , Thyagaraju GS , Dixit Sunanda
Published: January 2021

The Biomedical image analysis technique used in most of the clinical diagnosis activities, which is one of the explorative areas that appeal intense significance among scientists. The retinal fundus images are utilized in clinical diagnosis extensively for the treatment and to observe various eye diseases. Diabetic retinopathy is one of the foremost sources for blindness. The major diagnostic sign of diabetic retinopathy is the damage of blood vessels due to various reasons in the eye and then establishment of lesions in the retina. The screening and detection of Diabetic Retinopathy can be performed using retinal fundus images. The identification and analysis of diabetic retinopathy (DR) by means of color fundus images involves experienced practitioners to recognize the existence of many small topographies with a detailed grading system, makes this a complex and time-consuming mission. In this paper, a novel systematized method for the discovery of exudates in retinal images to diagnose diabetic retinopathy. The color fundus images are characterized and analyzed to find microaneurysms on the retina and provides the severity. The algorithm is tested on datasets provided by ophthalmologists and Messidor dataset, which gave excellent and promising results.
Automatic Plant Identification Using Transfer Learning
Authors: Sachar S , Kumar A
Published: January 2021

Plant identification is a widely researched area in the field of computer vision. Many attempts have been made to automate the process of plant identification using an image of a part of plant including flower, leaf and bark. Leaf has proven be the most reliable source of information. After exhaustive experiments, we chose to apply transfer learning to compare the feature extraction capabilities of VGG-16, Xception, MobileNetV2 Convolutional Neural Network (CNN) and DenseNet121 architectures to freely available Swedish, Flavia and MalayaKew leaf image datasets. Random Forest is used as classifier to identify the species of given leaf. The evaluations and comparisons of the specified feature extractor models are provided. DenseNet121 achieved maximum accuracy of 100%, 99% and 92.4% respectively in the three datasets.
Optimization based internal model control design
Authors: Rajasekhara Reddy B , Tulasiram K , Akshay Naregalkar , Pranavanand S
Published: January 2021

Control of nonlinear and/or constrained processes is a challenging and important task, especially chemical processes that need special attention to maximize the product thereby the profit. In this work, an optimization-based internal model control design is presented for both linear and nonlinear systems. For controlling nonlinear systems, the process dynamics are linearized around the operating point. The use of an optimizer as an internal model controller will enable the control engineer to deal with the constraints posed by the various process parameters and the effect of various process parameter variations in the closed-loop response is reported. Simulation case studies are presented viz., a first-order LTI system, and Continuous Stirred Tank Reactor(CSTR) to prove the efficacy of the optimization based internal model controller in controlling linear and nonlinear constrained processes.
A Study on Classification Techniques based on Opinions
Authors: Pooja Bhukal , Jaswinder Singh
Published: January 2021

Sentiment analysis or opinion mining is one of the considerable undertakings of Natural Language Processing. Sentiment analysis is a quick-growing investigation area in computer science. It includes a way of improvement for the collection and assessment of comments or opinions about legislation, laws, and opinions of products, and so forth, which are posted on the internet. Opinion mining analyzes the opinions given by people. In sentiment analysis, the customer feedback, review, shopping site review, online purchase reviews, politics opinions are considered for the analysis. Sentiment analysis is about detecting the emotions i.e. sad, happy, angry, excited, etc. The main objective of opinion mining is characterizing what the public think and comment. This review paper features certain discussions concerning the opinion-based classification and used approaches for the extraction of feeling assessment and opinion mining. Paper presents a comparison between different classification techniques. Applications and challenges of opinion mining are also discussed in this paper.
Improving Data Quality using Big Data Framework: A Proposed Approach
Authors: Soni Shikha , Singh Amritpal
Published: January 2021

The field of Big Data and Big Data Analytics is one of the most emerging fields in today’s technology. Data being the most important aspect of Big Data, it is important to select it in such a way that it provides maximum efficiency when analysed. Data quality is a concept which is taken in account while measuring the quality of the dataset. Data quality imposes certain defined characteristics on data which needs to be fulfilled up to a certain extent for the data to be considered reliable for analysis. In this paper, we have proposed an approach which introduces a ‘believability factor’ to measure the reliability or believability of the dataset by taking a sample of the unstructured dataset. This paper along with believability factor proposes the methodology to calculate execution time of the sampled dataset and Mean Absolute Error between the believability of the sampled and unstructured dataset.
Smart City: A new phase of sustainable development using fog computing and IoT
Authors: Malik Swati , Gupta Kamali
Published: January 2021

Fog computing is a distributed technology which performs computations at the edge of the network. IoT devices are generating large amount of data that too Heterogeneous in nature. In order to handle applications which, require time sensitive data and quick decision making, a reliable platform is required which can handle all the tasks in a robust manner considering fault tolerance and state migration. To process request originating from end user, an efficacious and pragmatic approach is to get the request served at the edge of the network rather than cloud envisioned with the aim to minimize the latency. Quick decision-making characteristic of fog computing makes it a smart choice for smoother processing and execution of smart city applications. As utilization of internet and IoT devices has boomed the industry, it is important to have reliable applications for day to day activities like payment of bills, traffic light management, health support system etc. The research study conducted explores multi-dimensional abstractions that can extend technological contributions in current epidemic situation.
Essence of Scalability in Wireless Sensor Network for Smart City Applications
Authors: Dogra Roopali , Rani Shalli , Sharma Bhisham , Verma Sandeep
Published: January 2021

Internet of Things (IoT) is the emerging research area. Along with many physical objects, Wireless Sensor Networks (WSNs) play an impeccable role in the smart city applications. It is learned from the retrospective study that the interaction between different types of objects, gathering of data from all the zones, their analyses etc., are very difficult to handle without energy conserving algorithms and protocols. Furthermore, applications of IoT specifically smart cities require a fault tolerant, scalable and reliable framework for real time processing of collected data. In this paper, we have proposed a cluster-based routing methodology which could be used at the sensing layer of smart city IoT. We select Cluster Head (CH) based on energy, distance, network energy and node density. The consideration of above parameters helps in acquiring the optimized performance of the protocol Zone-Stable Election Protocol (Z-SEP). We have performed simulations in MATLAB to emphasize the role of scalability of WSN in smart city application. We found that this protocol under performs when it is encountered for scalablity.
Identification of Disturbances in Power System and DDoS Attacks using Machine Learning
Authors: Panthi Manikant
Published: January 2021

Power system disturbances are the most common problem that often occurs due to human-made or natural events. The most challenging issue is finding the main cause of power disturbance and making appropriate decisions in response to it. The accuracy of the human judgement is not good in predicting the power disturbance; it is not easy for operators to monitor the system’s situation. To make human findings easier, we incorporate machine learning techniques to differentiate between the categories of power disruptions and focus more on DDoS attacks, which are its main cause. We evaluate various machine learning techniques to discriminate between power disturbances and to discuss the advantage of incorporating machine learning to enhance the productivity of the power system design.
An Empirical Research and Comprehensive Analysis of Stock Market Prediction using Machine Learning and Deep Learning techniques
Authors: Singh Aryendra , Gupta Priyanshi , Thakur Narina
Published: January 2021

Financial markets are inherently unpredictable. They continue to change based on the performance of the company, past records, market value and are also dependent on news & timings. By carrying out trend analysis, one has the ability to prejudge stock prices. Machine Learning Techniques that are available, have the potential to forecast future stock prices. Each stock represents a different trend, so a singular machine learning model can’t be applicable to other stocks. Thus, one model giving a high degree of precision can’t guarantee working on another. Too many variables are involved while predicting stocks-physical factors vs. psychological, irrational and rational behaviour, etc. All of these factors combined indicate stock prices as capricious and difficult to foresee. In order to resolve this challenge, a comprehensive study with historical data on stock prices of listed firms was performed. The main premise behind this research was to illustrate how to apply machine learning algorithms such as Averaging, Linear Regression including advanced deep learning techniques such as Long-Term Short Memory and applying technical tools like the Modern Portfolio Theory and Bollinger bands.
Integration of Internet of Things, Cloud Computing: Review
Authors: Patil Charushila , Chaware Anita
Published: January 2021

As the Network of Things has grown exponentially, the Internet of Things devices generate tremendous data. IoT provides virtualization with or without the presence of being with the services. Managing this vast volume of data becomes a very tedious task. So in diverse sectors, including such as healthcare, education, agriculture, smart cities, there are various iot applications. Using different technologies, sensor data is acquired and data collection, data cleaning, data processing, and analysis are done with the help of cloud computing. We present a survey of the integration of the Internet of Things, cloud computing, and healthcare in this paper. The paper focuses on the different IoT Cloud computing platforms used with the Internet of Things and is an advantage of the healthcare domain’s usefulness.
Impact of Similarity Measures in K-means Clustering Method used in Movie Recommender Systems
Authors: Rahul Mayur , Pal Parashuram , Yadav Vikash , Dellwar Devendra Kumar , Singh Swarnima
Published: January 2021

One of the most challenging tasks in today’s era is to deliver personalized information to the user or group of users according to their preferences. The decision making process is used as a tool by the recommender systems to suggests various products and items. The goal of recommender system is to deliver germane information to the user based on their likings. In this research work, we study and compare the effect of similarity measures used in k-means clustering in movie recommender systems. Our proposed method used the sampling, PCA and k-means clustering to recommend movie from the MovieLens dataset. In the whole process, some similarity measures are used in k-means clustering such as Euclidean, Minkowski, Mahalanobis, Cosine similarity and Pearson correlation. The aim of our work is to study the effect of similar measures in movie recommender systems in terms of standard deviation (SD), mean absolute error (MAE), root mean square error (RMSE), t-value, Dunn Matrix, average similarity and computational time using publicly available MovieLens dataset. The results achieved from the experiments indicates that Cosine similarity is best technique in movie recommender system in terms of accuracy, efficiency and processing speed and also able to get MAE of 0.65, which is best between all similarity measures.
Integration of Blockchain and IoT (B-IoT): Architecture, Solutions, & Future Research Direction
Authors: Aggarwal Vikash Kumar , Sharma Nikhil , Kaushik Ila , Bhushan Bharat , Himanshu
Published: January 2021

Internet of Things (IoT) plays a very important role in every field. With increase in its features it is almost used in every sector. As it is used everywhere so key elements of security are very much critical components which are to be preserved. For formulating these concepts, a new technology called blockchain can be used. In order to have secure transaction between various items, this technology is tremendously used without involving any third party. In this paper, we present introductory part of IoT enabled with blockchain, their key features, architecture layout, characteristic features of both the technologies, their futuristic solutions for different real-world problems, different communicational models etc. Both the technologies possess a number of characteristic features in every field but certain limitations too exist which gives futuristic direction for research.
Method for dataset preparation for soil data analysis in decision support applications
Authors: Motia Sanjay , Reddy SRN
Published: January 2021

Nowadays, publically available soil health cards (SHC) are easily understandable and useful means of knowledge transfer about soil conditions to farmers for sustainable soil management in farms. The SHCs are the true representative of precise and useful information about the physicochemical properties and nutrient status of agricultural soil. Before SHCs, the soil test records generally lie with soil test laboratories (STLs) and their availability in form of soil datasets is very limited. Moreover, the datasets are not big enough to be utilized as test datasets in soil investigation and development of futuristic and intelligent decision support and advisory systems. To address the issue, this paper presents a simple method for soil dataset preparation from SHCs for machine learning (ML) and artificial intelligence (AI) based soil research and analysis applications. The method collects wide number of SHCs for particular location and applies simple parsing and data cleaning concepts, implemented by utilizing open-source python libraries, to set of SHCs to prepare soil dataset. The dataset prepared by the proposed method can be used in a variety of intelligent data-driven decision support applications such as soil classification, nutrient prediction, and fertilizer recommendation. The dataset thus generated by the proposed method would help both researchers and soil scientists in their future research on soil health monitoring and decision support for sustainable farm management.
Chaos Based Secure Medical Image Transmission Model for IoT- Powered Healthcare Systems
Authors: Rajendran Sujarani , Doraipandian Manivannan
Published: January 2021

Due to the extensive development of Internet of Things (IoT) in e-healthcare environment, security and integrity of the medical data especially medical images became a big issue. This paper proposes a chaotic security architecture for ensuring the security of the medical images during transmission and storage. The proposed model is built by comprising of three main stages. At first, message digest 5 algorithm is applied to the plain image for generating the seed key of Lorenz chaotic map. Subsequently Lorenz map is iterated to generate the chaotic key series utilized for further process. In second stage, dual confusion such as row-by-row and column-by-column confusion is executed on the plain image. At last, dual diffusion process is performed by applying binary reverse and compliment operation, in addition to that XOR operation is executed between diffused image and Lorenz chaotic key image. Simulation results and analysis of security level by applying different attacks indicates that the developed cryptosystem has the potential of satisfying the security requirements of IoT healthcare applications.
Handwritten Digits Identification Using Mnist Database Via Machine Learning Models
Authors: Gope Birjit , Pande Sagar , Karale Nikhil , Dharmale Shivani , Umekar Pooja
Published: January 2021

The identification of hand-written digits is among the most significant issue in the applications for pattern detection. In many application such as postal code, check online routing bank accounts, data form entry, etc., the applications of digits recognition include the center of the issue is the need to construct an appropriate algorithm that can identify hand-written digits and that users upload through a smartphone and scanner and other digital devices. In this paper, we took a repository of MNIST, which is a sub-set of the database of NIST results. The MNIST dataset accommodates the collection of hand-written scanned images from a broader variety of NIST repository produced by hand. The method proposed in this paper is centered on numerous machine learning methods to perform hand-written digit detection that is off-line in the python language platform. The primary objective of this paper is to render hand-written digits recognition reliable and precise. For the identification of digits using MNIST many machine learning algorithms have been used including Support Vector Machine, Multilayer Perceptron, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Random Forest.
Analytical Study of the Robustness of the Different Variants of Fractional-Order Self-Tuned Fuzzy Logic Controllers
Authors: Agrawal Alka
Published: January 2021

Due to the evolution in technologies, it has become necessary for the systems to work in real time and for efficient output they have to be adaptive. Since the conventional Proportional-Integral-Derivative (PID) controller lacks to control the less informative non-linear systems and has fixed parameters, therefore, artificial techniques with the inclusion of adaptability has been studied in this paper. It has been observed that by implementing an extra layer of fuzzy controller in parallel, the gains of the controller could be change in real time and can highly reduce the external effects on the system as compared to the non-adaptive controller. In addition to this, different variants of fuzzy controllers like self-tuned fractional-order fuzzy PD (STFOFPD), self-tuned fractional-order fuzzy PI (STFOFPI) and self-tuned fractional-order fuzzy PID (STFOFPID) are compared and it was found that the STFOFPID outperforms all the controllers in the servo-regulatory mechanism.
Empirical analysis of regression techniques by house price and salary prediction
Authors: Bansal U , Narang A , Sachdeva A , Kashyap I , Panda S P
Published: January 2021

Regression analysis is extensively used for prediction and prognostication, and its use has substantial overlap with the domain of machine learning. The main objective of this paper is to compare the performance of two regression techniques namely Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) algorithms by two cases: predicting the salary of employees after certain years and predicting the prices of real estates. An employee’s salary depends on numerous factors, such as total employee experience, certifications, and overall experience as a lead and manager. The factors in predicting house prices are the area of land (sqft_living), condition, waterfront, number of bedrooms, and so on. The dataset used in this experiment is an open-source dataset from KaggleInc. The algorithms were compared using parameters like R-squared value, Mean absolute error (MAE), Mean Squared Error (MSE), Median Absolute Error (MDAE), Variance Score, and Root Mean Square Error (RMSE). Results have shown that MLR provides the better efficiency in comparison to SLR.