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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Formula One Race Analysis Using Machine Learning
Formula One (also known as Formula 1 or F1) is the highest class of international auto-racing for single-seater formula racing cars sanctioned by the Fation International de automobile (FIA). The World Drivers Championship, which became the FIA Formula One World Championship in 1981, has been one of the premier forms of racing around the world since its inaugural season in 1950. This article looks at cost-effective alternatives for Formula 1 racing teams interested in data prediction software. In Formula 1 racing, research was undertaken on the current state of data gathering, data analysis or prediction, and data interpretation. It was discovered that a big portion of the leagues racing firms require a cheap, effective, and automated data interpretation solution. As the need for faster and more powerful software grows in Formula 1, so does the need for faster and more powerful software. Racing teams benefit from brand exposure, and the more they win, the more publicity they get. The papers purpose is to address the problem of data prediction. It starts with an overview of Formula 1s current situation and the billion-dollar industrys history. Racing organizations that want to save money might consider using Python into their data prediction to improve their chances of winning and climbing in the rankings. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
3D CNN-Based Classification of Severity in COVID-19 Using CT Images
With the pandemic worldwide due to COVID-19, several detections and diagnostic methods have been in place. One of the standard modes of detection is computed tomography imaging. With the availability of computing resources and powerful GPUs, the analyses of extensive image data have been possible. Our proposed work initially deals with the classification of CT images as normal and infected images, and later, from the infected data, the images are classified based on their severity. The proposed work uses a 3D convolution neural network model to extract all the relevant features from the CT scan images. The results are also compared with the existing state-of-the-art algorithms. The proposed work is evaluated in accuracy, precision, recall, kappa value, and Intersection over Union. The model achieved an overall accuracy of 94.234% and a kappa value of 0.894. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel
In this paper, hybrid texture features are proposed for identification of scripts of bi-lingual camera images for a combination of 10 Indian scripts with Roman scripts. Initially, the input gray-scale picture is changed over into an LBP image, then GLCM and HOG features are extracted from the LBP image named as LBGLCM and LBHOG. These two feature sets are combined to form a potential feature set and are submitted to KNN and SVM classifiers for identification of scripts from the bilingual camera images. In all 77,000-word images from 11 scripts each contributing 7000-word images. The experimental results have shown the identification accuracy as 71.83 and 71.62% for LBGLCM, 79.21 and 91.09% for LBHOG, and 84.48 and 95.59% for combined features called CF, respectively for KNN and SVM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Soft Computing Approach for Student Dropouts in Education System
The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students performance and feedback. The students dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Efficient Machine Learning Classification model for Credit Approval
Credit authorization is a critical step for banks as well as every bank's main source of revenue is its line of credit. Thus, banks can profit from the loan interest they approve. Profitability or lost opportunity of a bank is highly dependent on loans that are whether consumers repay the debt or refuse. Loan collection is a significant factor in a bank's economic results. Forecasting the customer's ability to repay the loan in order to determine whether it should authorize or deny loan documents is a significant undertaking and a critical method in data analytics is being utilized to investigate the problem of loan default prediction: On the premise of assessment, the Logistic-Regression Classification Model, Random-Forest Classifier and Decision Tree Classification Models are compared. The mentioned classification algorithms were created as well as subsequently various evaluation metrics were obtained. By utilizing a suitable strategy, the appropriate clients for loan providing may be simply identified by assessing their probability of non-performing loans. This indicates that a bank really shouldn't simply prioritize wealthy consumers when giving loans, but it should also consider a client's other characteristics. This approach is critical in making credit judgments and forecasting default risk. 2023 IEEE. -
Artificial Intelligence in Disaster Management: A Survey
This paper provides a literature review of cutting-edge artificial intelligence-based methods for disaster management. Most governments are worried about disasters, which, in general, are unbelievable events. Researchers tried to deploy numerous artificial intelligence (AI)-based approaches to eliminate disaster management at different stages. Machine learning (ML) and deep learning (DL) algorithms can manage large and complex datasets emerging intrinsically in disaster management circumstances and are incredibly well suited for crucial tasks such as identifying essential features and classification. The study of existing literature in this paper is related to disaster management, and further, it collects recent development in nature-inspired algorithms (NIA) and their applications in disaster management. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Effective Deep Learning Classification of Diabetes Based Eye Disease Grades: An Retinal Analysis Approach
Diabetic Retinopathy (DR) is a common misdiagnosis of diabetes mellitus, which damages the retina and impairs eyesight. It can lead to vision impairment if it is not caught early. Tragically, DR is an unbreakable cycle, and treatment only serves to reinforce the perception. Early detection of DR and effective treatment can significantly lower the risk of visual loss. In comparison to PC-aided conclusion frameworks, the manual analysis process used by ophthalmologists to diagnose DR retina fundus images takes a lot of time, effort, and money and is prone to error. As of late, profound learning has become quite possibly the most well-known procedure that has accomplished better execution in numerous areas, particularly in clinical picture examination and classification. Thereby, this paper brings an effective deep learning-based diabetes-based retinography in which the following are the stages: a) Data collection from MESSIDOR which contains 1200 images classified into 4 levels and graded from 03 followed by b) Preprocessing using grayscale normalized data. Then followed by c) feature extraction using Discrete Wavelet Transform (DWT), d) feature selection using Particle Swarm Optimization (PSO) and finally given for e) classification using Densenet 169. Experimental states that the proposed model outperforms and effectively classified grades compared to other state-of-art models (accuracy:0.95, sensitivity:0.96, specificity;0.97). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Optimal Shortest Path Routing over Wireless Sensor Networks Using Constrained Genetic Firefly Optimization Algorithm
In Wireless Sensor Networks (WSNs), a large number of sensor nodes are placed over a specific area in any real-life application. The sensor node is small, with limited battery life, memory, and computing capacity. Due to the limited power of the battery, WSNs must expand the system life by minimizing the energy usage. In the existing system, the methods have limitations related to optimal shortest routing path, node energy consumption, network reconfiguration, and so on. In order to overcome these issues, aConstrained Genetic FireFly Optimization Algorithm (CGFFOA) is proposed. The CGFFOA algorithm is designed to select the best shortest path routing through the selection of Cluster Head (CH) nodes based on the better energy utilization, delay, and high throughput sensor nodes. It is used to optimize the routing path based on the energy, hop count, inter and intra cluster delay, and lifetime. The simulation findings therefore conclude that, with regard to reduced energy consumption, higher throughput, and lower end-to-end delay, the proposed CGFFOA algorithm is preferable to existing methods such as Particle Swarm Optimization (PSO) and Dynamic Source Routing (DSR). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Gender Identification of Silkworm Pupa and Automated Cocoon Cutting Machine for Benefiting the Sericulture Grainages in Karnataka
Sericulture is the backbone of a mediocre farmer family in India. Sericulture provides a major financial support to the farmers with minimum infrastructure and maintenance. Farmers collect the seed cocoons from the grainages also known as seed factories. Grainages produce high quality seeds by mating the male and female cocoons. There is a huge demand of labor in these seed factories to process cocoons. The process includes deflossing, removal of pupa from the cocoon, gender identification, mating, storage of seeds, dispersal of eggs to farmers. Removal of pupa from the cocoon requires the labor to cut open a small portion of the cocoon and remove the pupa from inside. Presently in India, most of the grainages induce female laborers to perform the above job. Pupa is removed from the cocoon by cutting the cocoon using a stainless-steel blade. Each labour is given certain amount of cocoons to cut in a day. This requirement would force the laborers to perform the job at a higher speed which poses a threat of getting wounded by the blade. Hence the process of removal of pupa from the cocoon and sex identification of pupa to be automated. Thereby it is important to automate the possible processes in the grainages which could reduce human intervention and increase productivity. Bivoltaine hybrid race of silkworm namely FC1 and FC2 are the varieties under consideration for the research. Theses silkworm varieties are majorly used in grainages for seed production and hence the proposed machine was introduced. This semi automated cocoon cutting machine identifies the gender of the cocoon and later cut the required amount of cocoons minimally. This process would help in maintaining the maximum reliability of silk thread. Thereby the silkworm gender identification has to be non destructive. Image classification were done using Convolution Neural Network (CNN), Visual Geometry Group 16 (VGG16) and Efficient net methods, among which the latter produced highest accuracy. The Efficient Net method has produced the validation accuracy of 98.99% for FC1 and 99.9 for FC2 variety. An automated cocoon cutting machine was developed to cut open the cocoons at a high speed. It is important to automate the possible processes in the grainages which could reduce human intervention and increase productivity. This paper focuses on automating the gender identification and removal of the pupa from the cocoon. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization
The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62% 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Review of Deep Learning Methods in Cervical Cancer Detection
Cervical cancer is one of the most widespread and lethal malignancy that affects women aged 25 to 55 across the globe. Early detection of cervical cancer reduces burden of living and mortality drastically. Cervical cancer is caused through human papillomavirus transmitted sexually. Since the hereditary aspect is absent in cervical cancer, it can be cured completely if diagnosed early. Cervix cell image analysis is gold standard for classifying cervical cancer. Also known as pap smear, this histopathological test can provide dependable, and accurate diagnostic support. The current study examines the most recent research breakthroughs in deep learning models to classify cervical cancer. Three benchmark datasets are comprehensively described. Selective key classification models were implemented and comparative analysis was conducted on their performance. The findings of this study will allow researchers, publishers, and professionals to examine developing research patterns. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Experimental Investigations on Turbine-Generator Shaft Under Subsynchronous Resonance
Energy exchange takes place between turbine and generator in the power system during subsynchronous resonance (SSR) which leads to torsional interaction between the shafts. Resonance in the power system is caused by the series capacitors connected to the transmission line. This paper aims to present an electromechanical approach to analyse and interpret subsynchronous resonance using the Finite element method. Subsynchronous resonance is introduced in two test rigs consisting of turbine, generator, shaft, and coupler with capacitors. Experiments and simulations (torque analysis and frequency response analysis) are conducted in test rigs and ANSYS workbench 16.0. Moreover, a spring damper is modelled to improve the stability of the shaft. From the results, it is clear that mechanical stress is increased when capacitors are connected to the test rig. A spring damper is installed at the point where the deformation is high. The damper reduced the stress and the vibration. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.