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An Analysis of Levenshtein Distance Using Dynamic Programming Method
An edit distance (or Levenshtein distance) amongst dual verses refers to the slightest amount of replacements, additions and omissions of signs essential to turn one name addicted to the additional is referred to as the edit distance (or Levenshtein distance) amongst dual verses. The challenge of calculating the edit distance of a consistent verbal, that is the set of verses recognised by a fixed mechanism, is addressed in this research. The Levenshtein distance is a straightforward metric for calculating the distance amongst dual words using a string approximation. After witnessing its efficiency, this approach was refined by combining certain comparable letters and minimising the biased modification between associates of the similar set. The findings displayed a considerable enhancement over the old Levenshtein distance method. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
IoT Cloud Systems: A Survey
IoT has gained a massive prevalence in the last decade. Various businesses are leveraging IoT Applications for industrial and commercial use cases. IoT also presents use cases in research and academia. However, setting up IoT Systems is complex due to the distributed and multi-disciplinary nature of IoT Systems. As a direct consequence of this complexity, the entire service industry has emerged that assists users to deploy and manage IoT systems. This paper aims to survey some of the Cloud management systems that help simplify and shorten the deployment process of IoT Systems. 2023 IEEE. -
Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry
Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. 2023 IEEE. -
Enhanced Edge Computing Model by using Data Combs for Big Data in Metaverse
The Metaverse is a huge project undertaken by Facebook in order to bring the world closer together and help people live out their dreams. Even handicapped can travel across the world. People can visit any place and would be safe in the comfort of their homes. Meta (Previously Facebook) plans to execute this by using a combination of AR and VR (Augmented Reality and Virtual Reality). Facebook aims to bring this technology to the people soon. However, a big factor in this idea that needs to be accounted for is the amount of data generation that will take place. Many Computer Science professors and scientists believe that the amount of data Meta is going to generate in one day would almost be equal to the amount of data Instagram/Facebook would have generated in their entire lifetime. This will push the entire data generation by at least 30%, if not more. Using traditional methods such as cloud computing might seem to become a shortcoming in the near future. This is because the servers might not be able to handle such large amounts of data. The solution to this problem should be a system that is designed specifically for handling data that is extremely large. A system that is not only secure, resilient and robust but also must be able to handle multiple requests and connections at once and yet not slow down when the number of requests increases gradually over time. In this model, a solution called the DHA (Data Hive Architecture) is provided. These DHAs are made up of multiple subunits called Data Combs and those are further broken down into data cells. These are small units of memory which can process big data extremely fast. When information is requested from a client (Example: A Data Warehouse) that is stored in multiple edges across the world, then these Data Combs rearrange the data cells within them on the basis of the requested criteria. This article aims to explain this concept of data combs and its usage in the Metaverse. 2023 IEEE. -
Enhanced Horse Optimization Algorithm Based Intelligent Query Optimization in Crowdsourcing Systems
Crowdsourcing is a strategy of collecting information and knowledge from an abundant range of individuals over the Internet in order to solve cognitive or intelligence intensive challenges. Query optimization is the process of yielding an optimized query based upon the cost and latency for a given location based query. In this view, this article introduces an Enhanced Horse Optimization Algorithm based Intelligent Query Optimization in Crowdsourcing Systems (EHOA-IQOCSS) model. The presented EHOA-IQOCSS model mainly based on the enhanced version of HOA using chaotic concepts. The proposed model plans to accomplish a better trade-off between latency and cost in the query optimization process along with answer quality. The EHOA-IQOCSS is used to compute the Location-Based Services (LBS) namely K-Nearest Neighbor (KNN) and range queries, where the Space and Point of Interest (POI) can be obtained by the conviction level computation. The comparative study stated the betterment of the EHOA-IQOCSS model over recent methods. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Change in Outlook of Indian Industrial OEMs Towards IIoT Adoption During COVID-19
Industrial Internet of Things (IIoT) is witnessing a steady increase in adoption by infrastructure and process industries. Industrial equipment manufacturers are one of the key stakeholders in this digitalization journey. The adoption of IIoT by the equipment manufacturers has been slower due to various valid reasons. The present pandemic COVID-19 created disruption in the factory operations in many parts of the world. This consequence has been hard on the manufacturing industry including the equipment manufacturers, and many of their strategic projects are slowing down or derailed. In India, a strict lockdown of three weeks which was later extended for another seven weeks was by far the longest lockdown effecting the industry and the equipment manufacturers. This study probes the impact of COVID-19 on the mindset of original equipment manufacturers (OEMs) towards adoption of IIoT. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Football Player Substitution Analysis using NLP and Survival Analysis
Football player substitution is extremely significant in situations where the team is down by goals or attempting to retain a lead that can add value to the team's performance. However, substituting players based on their prior performance would not assist the squad in making good decisions. In one of the papers, they used an inverse gaussian hazard model to determine the survival rate of players. However, the main issue arises when players do not give their all due to their mental state, which plays a critical role during the game. Furthermore, most of the research papers relied solely on past performance of players and various analyses, which was insufficient. This study discovered that the player's mindset should be mentally stable and competitive which is also very crucial during the match by reading various research articles. Hence, this study proposes a framework which comprises of two models, namely Survival Analysis (Kaplan-Meier Fitter) and Natural Language Processing (Sentimental Analysis). Sentimental Analysis would hel p in determining a player's mindset before the match and Kaplan-Meier Fitter is used to find out the survival rate of player's performance based on several factors like goal scored, passing accuracy etc. which would allow the team to make better informed decisions. Comparison of these two models would yield the best results for substitute players on the bench on the basis of their past performance and their mental health which will allow them to make team management to make better judgments. 2023 IEEE. -
Machine Learning Techniques for Automated Nuclear Atypia Detection in Histopathology Images: A Review
Nuclear atypia identification is an important stage in pathology procedures for breast cancer diagnosis and prognosis. The introduction of image processing techniques to automate nuclear atypia identification has made the very tedious, error-prone, and time-consuming procedure of manually observing stained histopathological slides much easier. In the last decade, several solutions for resolving this problem have emerged in the literature, and they have shown positive incremental advancements in this fieldof study. The nuclear atypia count is an important measure to consider when assessing breast cancer. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and future prospects for this critical undertaking, which will aid humanity in the fight against cancer. In this study, we examine the various techniques applied in detecting nuclear atypiain breast cancer as well as the major hurdles that must be overcome and the use of benchmark datasets in this domain. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and prospects for this critical undertaking, which will aid humanity in the fight against cancer. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Improvement of Speech Emotion Recognition by Deep Convolutional Neural Network and Speech Features
Speech emotion recognition (SER) is a dynamic area of research which includes features extraction, classification and adaptation of speech emotion dataset. There are many applications where human emotions play a vital role for giving smart solutions. Some of these applications are vehicle communications, classification of satisfied and unsatisfied customers in call centers, in-car board system based on information on drivers mental state, human-computer interaction system and others. In this contribution, an improved emotion recognition technique has been proposed with Deep Convolutional Neural Network (DCNN) by using both speech spectral and prosodic features to classify seven human emotionsanger, disgust, fear, happiness, neutral, sadness and surprise. The proposed idea is implemented on different datasets such as RAVDESS, SAVEE, TESS and CREMA-D with accuracy of 96.54%, 92.38%, 99.42% and 87.90%, respectively, and compared with other pre-defined machine learning and deep learning methods. To test the real-time accuracy of the model, it has been implemented on the combined datasets with accuracy of 90.27%. This research can be useful for development of smart applications in mobile devices, household robots and online learning management system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Impact of Using Partial Gait Energy Images for Human Recognition by Gait Analysis
Gait analysis is a behavioral biometric that classifies human, based on how they walk and other variables involved in the forward movement. In this study, we have attempted to comprehend the significance of the upper portion of the body in gait analysis for human recognition. The data for this study came from the CASIA dataset, which was donated by the Chinese Academy of Sciences Institute of Automation. We began by extracting the gait energy image (GEI) from the dataset and employing principal component analysis to minimize the dimensionality (PCA). For classification, random forest, support vector machine (SVM), and convolution neural network (CNN) algorithms are implemented to recognize the human subjects. This paper provides experimental results to show the accuracy attained when classification is done on GEI of full-body images is higher than the accuracy attained when classification is done on GEI of the lower portion of the body only. It also shows the significance of the GEI of the upper portion of the body. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimization in the Flow of Scientific Newspapers
The evolutions that occurred in the past decades have provoked variations in the market as well as academic and research. Given this scenario, the research explored in this article was aimed to analyze the contribution of the management of PMBOK methods for the optimization of Scientific Editorial Flow. The methodology used presented a quantitative approach, of descriptive character based on a survey, made available on social networks and Facebook groups, through the google forms platform. The sample is given by Snowball, this type of sampling enables the researcher to study specific groups and is difficult to reach. The analysis was by descriptive statistics, using the Likert scale, as well as the weighted average and fashion responses. It was identified that the Critical Success Factors of a Project that can contribute to the optimization of the editorial flow of a Scientific Periodical are efficient communication, empowerment, change management, client involvement, supplier involvement and conflict management. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Empirical Study on Categorized Deep Learning Frameworks for Segmentation of Brain Tumor
In the medical image segmentation field, automation is a vital step toward illness detection and thus prevention. Once the segmentation is completed, brain tumors are easily detectable. Automated segmentation of brain tumor is an important research field for assisting radiologists in effectively diagnosing brain tumors. Many deep learning techniques like convolutional neural networks, deep belief networks, and others have been proposed for the automated brain tumor segmentation. The latest deep learning models are discussed in this study based on their performance, dice score, accuracy, sensitivity, and specificity. It also emphasizes the uniqueness of each model, as well as its benefits and drawbacks. This review also looks at some of the most prevalent concerns about utilizing this sort of classifier, as well as some of the most notable changes in regularly used MRI modalities for brain tumor diagnosis. Furthermore, this research establishes limitations, remedies, and future trends or offers up advanced challenges for researchers to produce an efficient system with clinically acceptable accuracy that aids radiologists in determining the prognosis of brain tumors. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Are Women Employees in Engineering Institutions Suffer from the Stress: An Investigation Approach
Workplace stress could be detrimental to both the employer and its employees. The greatest ways to keep distress at bay in the workplace are via competent management and well-organized processes. Supervisors must recognize signs of staff distress and be prepared to provide assistance. Whenever an individual is confronted with job expectations and pressures exceeding their skillset and expertise, they experience stress connected to their employment. Workplace stress is common and could exacerbate stress when workers don't perceive they have the backing of management or coworkers in dealing with the challenges they face. The term 'stressed' is frequently used as a justification for ineffective management and inadequate supervision, even though it is typically caused by a misunderstanding of the difference between pressure and challenge. Stress and other forms of adversity are at an all-time high, both in the job and in personal life. Employee stress could be further exacerbated by things like job uncertainty, excessive hours, frequent changes, workload, and unattainable targets. The purpose of this article is to understand the variables that contribute to high-stress levels amongst female faculty members in engineering institutions strain in the job atmosphere. One's ability to manage stressful work situations, the amount of social help and assistance one receives, and the coping mechanisms one employs all play significant roles in how much stress one endures on the job. This research was done because it was necessary and important. In addition, women play a larger role in society than males do. This study suggests that women experience much high levels of stress than men do employment reasons, repercussions, roles and obligations of women faculty in engineering education, and possible remedies are all explored in this paper. 2023 IEEE. -
Self-adaptive Butterfly Optimization for Simultaneous Optimal Integration of Electric Vehicle Fleets and Renewable Distribution Generation
Fuel prices and environmental concerns have prompted an increase in the use of electric vehicle (EV) technology in recent years. Charging stations (CSs) are a great way to support this shift to sustainability. This has increased the demand for EV charging on electrical distribution networks (EDNs). However, optimal EV charging stations along with renewable energy sources (RES) integration can maintain EDN performance. This paper proposes a novel hybrid approach based on self-adaptive butterfly optimization algorithm (SABOA) for optimal integration of EV CSs and RES problems under various EV load growth scenarios. A multi-objective function is created from distribution losses, GHG emissions, and VSI. The ideal locations for CSs and RES are found using SABOA while minimizing the proposed multi-objective function. The simulation results on IEEE 33-bus EDN validate the suggested technique's superiority in terms of global optima. This type of hybrid strategy is required for optimal real-time integration of EV CSs and RES, taking into account emerging high EV load penetrations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Mathematical Model to Explore the Details in an Image with Local Binary Pattern Distribution (LBP)
Mathematical understanding is required to prove the completeness of any research and scientific problem. This mathematical model will help to understand, explain and verify the results obtained in the experiment. The model in a way will portray the mathematical approach of the entire research process. This paper discusses the mathematical background of proposed prediction of lung cancer with all the parameters. Processes involved analyzing the 2D images, basic quantitative method, from, related equation and fundamental algorithmic understanding with slightly modified versions of prediction are represented in the below section with how the local binary pattern distribution can be modified so that we get reduced run time and better accuracy in the final result. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comprehensive Review onCrop Disease Prediction Based onMachine Learning andDeep Learning Techniques
Leaf diseases cause direct crop losses in agriculture, and farmers cannot detect the disease early. If the diseases are not detected early and correctly, the farmer must undergo huge losses. It may lead to the wrong pesticide or over pesticide, directly affect crop productivity and economy, and indirectly affect human health. Sensitive crops have various leaf diseases, and early prediction of these diseases remains challenging. This paper reviews several machine learning (ML) and deep learning (DL) methods used for different crop disease segmentation and classification. In the last few years, computer vision and DL techniques have made tremendous progress in object detection and image classification. The study summaries the available research on different diseases on various crops based on machine learning (ML) and deep learning (DL) techniques. It also discusses the data sets used for research and the accuracy and performance of existing methods. It does mean that the methods and available data sets presented in this paper are not projected to replace published solutions for crop disease identification, perhaps to enhance them by finding the possible gaps. Seventy-five articles are analysed and reviewed to find essential issues that involve additional study for future research in this domain to promote continuous progress for data sets, methods, and techniques. It mainly focuses on image segmentation and classification techniques used to solve agricultural problems. Finally, this paper provides future research scope and challenges, limitations, and research gaps. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Design and Implementation of Machine Learning-Based Hybrid Model for Face Recognition System
Face recognition technologies must be able to recognize users faces in a chaotic environment. Facial detection is a different issue from facial recognition in that it requires reporting the position and size of every face in an image, whereas facial recognition does not allow for this. Due to their general similarity in look, the photographs of the same face have several alterations, which makes it a challenging challenge to solve. Face recognition is an extremely challenging process to do in an uncontrolled environment because the lighting, perspective, and quality of the image to be identified all have a significant impact on the process's output. The paper proposed a hybrid model for the face recognition using machine learning. Their performance is calculated on the basis of value derived for the FAR, FRR, TSR, ERR. At the same time their performance is compared with some existing machine learning model. It was found that the proposed hybrid model achieved the accuracy of almost 98%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data science: simulating and development of outcome based teaching method
The educational researcher has a wealth of options to apply analytics to extract meaningful insights to improve teaching and learning due to the growing availability of educational data. Teaching analytics, in contrast to learning analytics, examines the quality of the classroom environment and the efficacy of the instructional methods used to improve student learning. To investigate the potential of analytics in the classroom without jeopardizing students' privacy, we suggest a data science strategy that uses simulated data using pseudocode to build test cases for educational endeavors. Hopefully, this method's findings will contribute to creating a teaching outcome model (TOM) that can be used to motivate and evaluate educator performance. In Splunk, the study's simulated methodology was carried out. Splunk is a real-time Big Data dashboard that can gather and analyze massive amounts of machine-generated data. We provide the findings as a set of visual dashboards depicting recurring themes and developments in classroom effectiveness. Our study's overarching goal is to help bolster a culture of data-informed decision-making at academic institutions by applying a scientific method to educational data. 2023 IEEE. -
The Role of Machine Learning Analysis and Metrics in Retailing Industry by using Progressive Analysis Pattern Technique
Analyzing customer purchasing data has been a challenging task for data analyzers. Even though lots of methods are introduced in this kind of research but still many barriers are there to finding the optimal pattern. Consider customer buying data is used to examine the types of parameters which is influence the customer. In this proposed work, Progressive Analysis Pattern Technique (PAPT) to predict future customer buying patterns in online shopping. We incorporated dynamic data handling prior to the proposed methodology. It will give ample purpose for the organization's perspective because the proposed work primarily focused on customer features related to the number of product quantities and product price variations of the previous purchase. Marketing strategies are most effective if they are focused to the exact client requirements. A Significant mission in campaign planning is deciding which customer to target. This research paper focusses on empirical targeting models. 2023 IEEE.