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Hybrid AI Talent Acquisition Model: An Opinion Mining and Topic based approach
Artificial Intelligence models have found their usage in the human resource domain. In this paper, job reviewers' opinions on online discussion boards have been captured. The relative importance of factors has been established through an extensive literature review. First, LDA Topic modelling by adopting PCA is performed on unstructured text data has been analyzed. Second, sentiment analysis using the Li-Hu method has been employed to understand job seekers' satisfaction with job portals. The proposed model, 'Hybrid AI Talent Acquisition Model,' follows a novel approach to streamlining the jobseeker opinion related to online outlets. 2022 IEEE. -
A Comparative Investigation on the use of Machine Learning Techniques for Currency Authentication
In the present banking sector, identifying the real and the fake note is a very challenging task because if we do it manually, it takes a long time to check which is real and which is fake. This research study article aims to authenticate the money between real and fake by using different machine algorithms facilitating learning, such as K-means Clustering, Random Forest Classification, Support Vector Machines, and logistics Regression. Specifically, we consider the banknote dataset. The data of money is extracted from various banknote images by using the wavelet transform tool, which is primarily used to remove elements from the images. However, we are mainly concerned with the different machine learning algorithms, so we take the two variables, where the first variable indicates image variance and the second indicates image skewness. We use these two variables to train our machine learning algorithms. So, majorly, by applying the different machine learning algorithms, which are supervised and unsupervised, we find the accuracy for the respective machine learning algorithms and then visualize and classify the real and fake notes separately. Finally, the prediction is based on integrity, which means the efficiency value is based on how much the mechanism system can uncover the fake notes. Then, after calculating the accuracy of currency authentication, there is a high possibility that the accuracy of the particular algorithm is the best algorithm, so the application of currency authentication will be very useful for the bank to easily find duplicate notes. 2022 IEEE. -
An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
As object detection has gained popularity in recent years, there are many object detection algorithms available in today's world. Yet the algorithm with better accuracy and better speed is considered vital for critical applications. Therefore, in this article, the use of the YOLOV4 object detection algorithm is combined with improved and efficient inference methods. The YOLOV4 state-of-the-art algorithm is 12% faster compared to its previous version, YOLOV3, and twice as faster compared to the EfficientDet algorithm in the Tesla V100 GPU. However, the algorithm has lacked performance on an average machine and on single-board machines like Jetson Nano and Jetson TX2. In this research, we examine the performance of inferencing in several frameworks and propose a framework that effectively uses hardware to optimize the network while consuming less than 30% of the hardware of other frameworks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Fine Needle Aspiration Images by Using Hybrid Feature Selection and Various Machine Learning Classifiers
Women die of breast cancer most often worldwide. Breast tissue samples can be examined by radiologists, surgeons, and pathologists for evidence of this cancer. Fine needle aspiration cytology (FNAC) can be used to detect this cancer through a visual microscopic examination of breast tissue samples. This sample must be examined by a cytopathologist in order to determine the patient's risk of breast cancer. To determine if a tumor is malignant, the nuclei of the cells must be characterized by their chromatin texture patterns. A machine learning method is used in order to categorize FNA images into two classes, respectively Malignant and Benign. For detecting abnormalities, numerous feature collection methods and machine learning means are applied here. Using features extracted from the FNA image set, UCI machine learning datasets are used to validate the proposed approach. This paper compares three classification methodologies, namely random forests, Naive Bayes, and artificial neural networks, by examining their accuracy, specificity, precision, and sensitivity, respectively. With the ANN and PCA along with the Chi-square selection method, 99.1% of the classifiers are correctly classified. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Social Media Sentiment Analysis of Stock Market on Tweets
Sentiment categorization is utilized in today's world to analyze social media data about the stock market and estimate its future stock movement. We investigated the possible influence of 'public sentiment' on 'market trends' using sentiment analysis and machine learning concepts. Due to the enormous number of components involved, such as economic situations, political events, and other environmental factors that may affect the stock price, stock price prediction is an exceedingly complicated and challenging process. Because of these considerations, evaluating a single factor's influence on future pricing and trends is challenging. As more individuals spend time online, the popularity and impact of numerous social media platforms has skyrocketed in recent years. Twitter is one such social media tool that has exploded in popularity. Twitter is a terrific place to stay up to speed on current societal trends and perspectives. The 'Twittersphere' is a melting pot that supports diverse viewpoints, emotions, and trends, and it has the potential to be a crucial influencer in influencing and shaping perceptions. 2022 IEEE. -
Analysis of Challenges Experienced by Students with Online Classes During the COVID-19 Pandemic
In the current context of the COVID-19 pandemic, due to restrictions in mobility and the closure of schools, people had to shift to work from home. India has the worlds second-largest pool of internet users, yet half its population lacks internet access or knowledge to use digital services. The shift to online mediums for education has exposed the stark digital divide in the education system. The digitization of education proved to be a significant challenge for students who lacked the devices, internet facility, and infrastructure to support the online mode of education or lacked the training to use these devices. These challenges raise concerns about the effectiveness of the future of education, as teachers and students find it challenging to communicate, connect, and assess meaningful learning. This study was conducted at one of the universities in India using a purposive sampling method to understand the challenges faced by the students during the online study and their satisfaction level. This paper aims to draw insight from the survey into the concerns raised by students from different backgrounds while learning from their homes and the decline in the effectiveness of education. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Some Variations of Domination in Order Sum Graphs
An order sum graph of a group G, denoted by ? os(G), is a graph with vertex set consisting of elements of G and two vertices say a, b? ? os(G) are adjacent if o(a) + o(b) > o(G). In this paper, we extend the study of order sum graphs of groups to domination. We determine different types of domination such as connected, global, strong, secure, restrained domination and so on for order sum graphs, their complement and line graphs of order sum graphs. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Framework for Digital Forensics Using Blockchain to Secure Digital Data
Digital forensics (DF) requires evidence integrity and provenance across boundaries of jurisdiction, and blockchain technology is ideal for ensuring that. As part of this paper, we discussed a digital forensic framework designed to help prevent duplication of data and secure digital data. In order to accomplish such forensic capabilities, we provide a block-based forensics framework. Using it, examinations are validated, irreversible, traceable, robust, and demonstrate high levels of confidence among examiners and evidence entities. 2022 IEEE. -
Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities
Deep convolutional neural networks (CNNs) have turn out to be one of the most advanced approaches trendy distinguishing snapshots in extraordinary fields. White blood cell classification is crucial for diagnosing anaemia, leukaemia, and a variety of other hematologic illnesses. Transfer learning with CNNs is frequently used in biological image categorization. Traditional methods for WBC classification is costly is terms of time and money. In the paper three convolutional neural network architectures are proposed which is based on transfer learning for microscopic image classification and compare the performance of models. The paper compares Transfer learning models like VGG-16, VGG-19, VGG-19 SVM hybrid and AlexNet. VGG-16 gives the best classification performance in comparison. VGG-16 model is which has a train accuracy of 0.9538 and train loss of 0.1322. 2022 IEEE. -
Clustering-based Optimal Resource Allocation Strategy in Title Insurance Underwriting
Production of insurance policies in all types of Insurance requires a thorough examination of the entity against which the Insurance is to be issued. In health insurance, it is the past medical data of the individuals. Vehicle insurance needs the examination of the vehicle and the owner's data. Likewise, in Title Insurance, it is the historical data of the property which needs scrutiny before the policy issuance. Underwriters perform the job of examining the property records. The scrutiny of the property records requires a high degree of the domain and legal expertise, and title insurance underwriters are often associated with legal professions. They do the final round of validation of the examination process. There are examination teams that take care of the initial set of regular examination tasks associated with each title insurance order. Some human experts assign the orders to the team associates. Not all the orders are of the same complexity in terms of examination. The allocation of the tasks happens based on the gut feeling of the supervisor, considering their experience with the team members. Our research creates clusters of the orders based on specific parameters associated with the orders. It builds a cost model of the past associates working on orders belonging to different clusters. Based on this cost matrix, we have built an optimal task allocation model that assigns the orders to the associates with the promise of optimal cost using a Linear programming solution used frequently in operations research. 2022 IEEE. -
Comparison of Machine Learning Algorithms for Predicting Chronic Kidney Disease
Early detection and characterization of chronic renal disease are crucial to ensure that patients receive the best possible treatment. This study uses data mining techniques to uncover hidden information about patients. The outcomes of using the Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree, XGBoost, LGBM Classifier, GaussianNB, KNeighbors Classifier, and XGBRF classifier have been compared. In our study, we demonstrate that Random Forest and XGBoost algorithms are more effective in classifying and predicting the severity level of chronic kidney disease 2022 IEEE. -
Protection Against SIM Swap Attacks on OTP System
One-time password-based authentication stands out to be the most effective in the cluster of password-less authentication systems. It is possible to use it as an authentication factor for login rather than an account recovery mechanism. Recent studies show that attacks like SIM swap and device theft raise a significant threat for the system. In this paper, a new security system is proposed to prevent attacks like SIM swap on OTP systems, the system contains a risk engine made up of supervised and unsupervised machine learning model blocks trained using genuine user data space, and the final decision of the system is subject to a decision block that works on the principles of voting and logic of an AND gate. The proposed system performed well in detecting fraud users, proving the systems significance in solving the problems faced by an OTP system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis and Forecasting of Crude Oil Price Based on Univariate and Multivariate Time Series Approaches
This paper discusses the notion of multivariate and univariate analysis for the prediction of crude oil price in India. The study also looks at the long-term relationship between the crude oil prices and its petroleum products price such as diesel, gasoline, and natural gas in India. Both univariate and multivariate time series analyses are used to predict the relationship between crude oil price and other petroleum products. The Johansen cointegration test, EngleGranger test, vector error correction (VEC) model, and vector auto regressive (VAR) model are used in this study to assess the long- and short-run dynamics between crude oil prices and other petroleum products. Prediction of crude oil price has also been modeled with respect to the univariate time series models such as autoregressive integrated moving average (ARIMA) model, Holt exponential smoothing, and generalized autoregressive conditional heteroskedasticity (GARCH). The cointegration test indicated that diesel prices and crude oil prices have a long-run link. The Granger causality test revealed a bidirectional relationship between the price of diesel and the price of gasoline, as well as a unidirectional association between the price of diesel and the price of crude oil. Based on in-sample forecasts, accuracy metrics such as root mean square logarithmic error (RMSLE), mean absolute percentage error (MAPE), and mean absolute square error (MASE) were derived, and it was discovered that VECM and ARIMA models can efficiently predict crude oil prices. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Monitoring and Controlling Data Through the Internet of Things (IOT) System: A Framework to Measure the Public Health
Associating and sharing information by means of the web between actual things, or 'things,' coordinated with sensors, programming, and different advances are known as the Internet of Things (IoT). In order to improve technology through IoT, there have been a number of important studies and investigations. This study exhibits how the Internet of Things might be utilized to screen wellbeing. In this research work, with the help of IoT based human wellbeing checking framework the information circulatory strain, beat rate, internal heat level, pulse, and other crucial signs are providing to the internet. The use of IoT for the human health monitoring system in later on future, need a very accurate assessment of risk and this is required to provide a long term information to the device. 2022 IEEE. -
Measuring Consumer Perception for P2P Platform: NLP Approach
The pandemic has forced lenders and borrowers to switch to alternative borrowing., investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further., Plutchik's wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments., whereas., only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower's., emotion of joy was seen in all 4 platforms., followed by emotions of sadness., surprise., anger., disgust., and fear. 2022 IEEE. -
On Circulant Completion of Graphs
A graph G with vertex set as {v0, v1, v2,.., vn-1} corresponding to the elements of Zn, the group of integers under addition modulo n, is said to be a circulant graph if the edge set of G consists of all edges of the form {vi, vj} where (i-j)(modn)?S?{1,2,,n-1}, that is, closed under inverses. The set S is known as the connection set. In this paper, we present some techniques and characterisations which enable us to obtain a circulant completion graph of a given graph and thereby evaluate the circulant completion number. The obtained results provide the basic eligibilities for a graph to have a particular circulant completion graph. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The Pendant Number ofLine Graphs andTotal Graphs
The parameter, pendant number of a graph G, is defined as the least number of end vertices of paths in a path decomposition of the given graph and is denoted as ? p(G). This paper determines the pendant number of regular graphs, complete r-partite graphs, line graphs, total graphs and line graphs of total graphs. We explore the bougainvillea graphs, e-pendant graphs and v-pendant graphs. If the pendant number is 2, then the number of paths in the path decomposition of the given graph is at most ? (G), the maximum degree of the graph. Hence, a large class of graphs give a more reasonable solution to Gallais conjecture on number of paths in the given path decomposition. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Critical Review of Applications of Artificial Intelligence (AI) and its Powered Technologies in the Financial Industry
The present research shed light on the applications of AI technologies for the financial industry of the UK. The research has also investigated the different types of powered technologies of AI and their impact on finance operations and activities. This research possesses the tools and techniques used by the researcher in gathering the research evidence for the proper completion of the research work. 2022 IEEE. -
Medical Ultrasound Image Segmentation Using U-Net Architecture
This research article discusses the implementation aspects of a Deep Learning architecture based on U-Net for medical image segmentation. A base model of the U-Net architecture is extended and experimented. Unlike the existing model, the input images are enhanced by applying a Non-Local Means filter optimized using a metaheuristic Grey wolf optimization method. Further, the model parameters are modified to achieve better performance. Tests were performed using two benchmark B-mode Ultrasound image datasets of 200 Breast lesion images and 504 Skeletal images. Experimental results demonstrate that the modifications resulted in more accurate segmentation. The performance of the modified implementation is compared with the base model and a Bidirectional Convolutional LSTM architecture. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Fake News Detection using Machine Learning and Deep Learning Hybrid Algorithms
Spreading misinformation or fake news for personal, political, or financial gain has become very common these days. The influence of this misinformation on peoples opinions can be significant, i.e., the 2016 presidential election in the United States was a perfect illustration of how false news may be used to deceive people. In todays fast-paced world, automatic detection of fake news has become an importantrequirement. In this paper, multiple machine learning algorithms have been implemented to perform classification. A proposition of a hybrid architecture consisting of CNN along with LSTM has also been made. The proposed model outperforms the other traditional approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.