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CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer
Cervical cancer is a leading cause of mortality in third world countries. Although there are multiple ways of screening cervical cancer, colposcope image analysis is considered to be standard routine method of diagnosis. Due to factors like lack of skilled personnel and interobserver variability, there is a need for automated diagnostic support for cervical cancer. However, artificial intelligence solutions for medical image analysis done through deep and machine learning models require high quality, non-erroneous and sufficient amount of data. Owing to the lack of such established benchmark datasets for the colposcope images, this work aims at establishing a standard benchmark multi state colposcope image dataset that also contains clinical findings pertaining to each case. In order to establish the quality of the images, mask R-CNN method is used for segmenting the images. Subsequently, a series of IMAGENet pretrained deep learning models are deployed on the dataset to evaluate the performance. The dataset will be made available upon request for strictly research purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models
In recent years, cerebral stroke has ascended as a paramount concern in global public health. Proactive strategies emphasizing metabolic control over salient risk factors present a superior approach compared to relying solely on physiological indicators, which may not delineate clear preventive directives. In this research, we present the SPX-CerebroPredict modela novel machine learning framework designed to classify imbalanced cerebral stroke data for clinical diagnostics. The study delves into feature selection methodologies, employing both information gain and principal component analysis (PCA). To address the class imbalance dilemma, the Synthetic Minority Over-sampling Technique (SMOTE) was harnessed. The empirical evaluation, conducted on the cerebral stroke prediction dataset from Kagglecomprising 43,400 medical records with 783 stroke instancespitted well-established algorithms such as support vector machine, logistic regression, decision tree, random forest, XGBoost, and K-nearest neighbor against one another. The results evince that our SPX-CerebroPredict model, integrating SMOTE, PCA, and XGBoost, surpasses its contemporaries, achieving an impressive accuracy rate of 95%. This discovery underscores the models potential for clinical applicability in cerebral stroke diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ceria doped titania nano particles: Synthesis and photocatalytic activity
Ceria (0.5, 1 and 2 mol%) doped titania nano catalysts were prepared by combustion synthesis method, using titanium isopropoxide as the starting material. The prepared catalysts were characterized by X-ray diffraction (XRD), Energy dispersive X-ray analysis (EDX), Scanning electron microscopy (SEM) and Infra red spectroscopy (FTIR). Total acidity of the prepared catalysts were determined by temperature programmed desorption of ammonia (TPD - NH3). XRD pattern of 1% ceria doped titania obtained by calcinations at 873 K indicated that the samples were crystalline with a mixture of anatase and rutile phase. No peaks corresponding to cerium oxide were observed XRD patterns indicating that the amount of cerium is negligible on the surface of titania catalyst. The photo catalytic activity was evaluated for the degradation of methylene blue (MB) under visible light irradiation. The degradation rates of MB on cerium doped TiO2 samples were higher than that of pure TiO2. The introduction of structural defects (cationic ceria dopant) into the titania crystal lattice leads to the change of band gap energy. As a result, the excitation energy is expanded from UV light of anatase TiO2 to visible light for ceria doped titania. 2016 Elsevier Ltd. -
Certificate Generation and Validation Using Blockchain
Verifying academic credentials is a standard procedure for employers when making job offers. After the interview procedure is complete, the employer takes a long time to supply the offer letter. The employer must have the certificate authenticated by the organization that issued it to confirm its originality. While confirming the authenticity of a certificate, the employer takes a long time. The selection procedure takes longer overall because of the long process involved in certificate verification. Blockchain offers a verified distributed ledger with a cryptography technique to combat academic certificate forgery to address this issue. The blockchain also offers a standard platform for document storage, access, and minimization of verification time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Challenges and Opportunities: Quantum Computing in Machine Learning
Many computing applications are being developed and applied in almost every aspect of life and in every discipline. With increasing number of problems and complexities, there is requirement for more computational power, faster speed and better results. To overcome these computational barriers, quantum computers, which are based on principles of quantum mechanics were introduced. Faster computation is the main reason behind the evolution of quantum computers which is achieved by using quantum bits instead of bits as quantum bits store both the values 1 and 0 together in superposition. The article focuses on basics of quantum computing in brief and the underlying phenomenon behind quantum computers. Also this article exposes recent trends and the problems that are being faced in this quantum technology. The major impact of quantum machine learning is also discussed. The quantum machine learning is providing better application in this modern field. This article analyses the different research gaps and possible solutions in quantum computing. Recent days quantum computing is implemented in different applications which is also described. 2019 IEEE. -
Challenges in Plasma Spraying of 8%Y2O3-ZrO2 Thermal Barrier Coatings on Al Alloy Automotive Piston and Influence of Vibration and Thermal Fatigue on Coating Characteristics
Although Thermal Barrier Coatings (TBCs) have found extensive application in automotive engines to enhance performance and to reduce fuel consumption and pollution, challenges of obtaining uniform and consistent coatings on non-uniform and irregularly shaped components are overcome only when the coatings are deposited via robot controlled APS or EBPVD. Atmospheric Plasma Spraying (APS) is the most commonly used and relatively cost-effective method to make TBCs: but not all APS facilities are equipped with comprehensive coating accessories. In a reciprocating diesel engine, the bowl at the piston crown forms one side of the combustion chamber and includes the space between piston crown (generally 9% Si-Al alloy in light - medium duty diesel fuel vehicle) and cylinder head. To achieve maximum effective fuel spray distribution and combustion, normally the crown of the piston has complex contours. One of the many service related parameters to be monitored to reduce the innumerable faults contributing to the performance of the engine is vibration. This paper addresses the issue related with the challenges associated with the plasma spraying of consistent and adherent TBC on Al-9% Si research pistons and its complex contours by APS, subjecting the coated pistons to thermal fatigue tests and evaluation of the coating characteristics after subjecting to vibration. 2018 Elsevier Ltd. -
Challenges of Digital Transformation in Education in India
Online learning has been present since the 1960s and has risen in popularity over time. World-class universities have been using online teaching-learning methodologies to fulfill the needs of students who reside far away from academic institutions for more than a decade. Many people predicted that online education would be the way of the future, but with the arrival of COVID-19, online education was imposed upon stakeholders far sooner and more suddenly than expected. When the COVID-19 pandemic broke out, educational institutions began to explore digital ways to keep students studying even when they couldn't be together in person as governments enacted legislation prohibiting large groups of people from gathering for any reason, including education. The future of such a transition looks promising. However, transitioning from one mode of education to another is not easy. Historically, when educators adopt new tools, learning still continues in the conventional manner. Based on the responses of 176 students, this paper studies the challenges of Digital transformation in the Education sector. The research is extremely beneficial in evaluating the scope of societal opposition to change. 2022 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. -
Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study
Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently. 2022 IEEE. -
Character recognition for Malayalam palm leaf manuscripts: An overview of techniques and challenges
Kerala is a small, ocean-facing state in South India and has been home to several ancient civilizations in the past. The yesteryears have rewarded the state with great cultural heritage, monuments, historic artifacts and the like. Palm leaf manuscript is one such antiquity. Before paper became common, palm leaf was the medium for writing in Kerala. Such manuscripts capture the glory of our past and deals with different domains such as arts, astrology, medicine, science, religion and spirituality. Palm leaf manuscripts have value both as a cultural asset and as a knowledge repository. Palm leaf manuscripts are organic and degrades with age. The environmental conditions can also accelerate its degradation. A viable solution in preserving the knowledge contained in these manuscripts is Handwritten Character Recognition (HCR). Digitized manuscripts have infinite life. Character recognition in Indian languages, including Malayalam, is considered a complex process mainly due to the size of character set, the similarity of characters and the presence of compound characters. This paper surveys existing works in the field of HCR relevant to Malayalam palm leaf manuscripts. 2023 Author(s). -
Characteristic Mode Analysis of Closed Metal Geometric Ring Shapes
In this study, the characteristic mode theory is used to better explain the physical behavior of a few simple closedshaped geometries. The bandwidth coverage, resonant behavior, and modal current distributions for several ringshaped geometries are shown and discussed. It has been demonstrated that the triangular, rectangular, and square ring geometries can result in multi-band performance, whereas the hexagonal, circular, square, and triangular rings are promising candidates for circularly polarized antenna designs. 2024 IEEE. -
Characteristic Mode Analysis of Fashion Brands Conductive Logos as Potential Radiators
A few popular fashion brand logos, which can be employed as potential radiating elements, are investigated in this paper based on the theory of characteristic mode (TCM). Such an analysis would further help design multi-band wearable antennas within the frequency range from 1 to 6 GHz. The resonant behavior and bandwidth capability for various modes are presented and discussed. It is observed that all the studied logos demonstrate a first resonant frequency around 1.5 GHz, while both Lacoste and Louis Vuitton logos show wider modal bandwidths capabilities. 2023 IEEE. -
Characteristic Mode Analysis of Metallic Automobile Logo Geometry
This paper presents a characteristic mode analysis of a few popular automobile logo geometries. It is performed to get an insight into the physical behavior of those geometries which can be employed as a radiating element, such as an antenna. Such an analysis helps design multi-band and multi-mode antennas suitable for 5G sub-6 GHz bands. The resonant behavior, bandwidth capability, and modal current distribution analysis are presented for various modes of different automobile logo geometries, demonstrating that Audi, Suzuki, and Volkswagen logos show multi-band performance. Moreover, due to having symmetric modes, the BMW logo was found to be suitable for designing a circularly polarized antenna. 2023 IEEE. -
Characterization and comparison studies of Bentonite and Flyash for electrical grounding
Earthing or Grounding is an Electrical system consists of electrodes which serves as an electrical connection from an electric circuit in the system to the earth or ground. Traditional Earthing- where we mix charcoal and salt offers low resistance to the fault current flow developed from a Low operating Voltages. Since operating voltages are high now a days, Short circuit current also increased. Traditional method of Earthing is replaced by chemical Earthing.Bentonite which is mainly used in chemical Earthing serves the requirement of Low resistance Earthing pits and also have the property to retain the moisture. In this paper an attempt had been made to assure the Flyash usage in the grounding pit and this paper discusses the Characterization, Comparison and Field Studies on Earthing Pit constructed with Bentonite and Fly ash layers. 2015 IEEE. -
Characterization of interval-valued fuzzy bridges and cutnodes
In this paper, we characterize interval - valued fuzzy bridges and interval-valued fuzzy cutnodes in terms of ? strong arcs. We discuss about the behaviour of arcs in a strongest path of an interval - valued fuzzy graph. An example is provided to prove that strongest paths are not in general related to strong paths in an interval - valued fuzzy graph. Finally we give a particular condition under which strong paths and strongest paths are equivalent. 2019 Author(s). -
Charting the Future of Fintech: Unveiling Finoracle through an In-depth Comparison of LLAMA 2, FLAN, and GPT-3.5
The research paper compares three Large Language Models (LLMs): LLAMA 2, FLAN, and GPT-3.5, in summarizing financial technology (fintech) news. Using 100 articles and the Rouge scoring system, it focuses on LLAMA 2's superior performance in creating concise and precise summaries. The study also introduces FinSage, a new framework utilizing LLAMA 2, promising to enhance fintech text analysis and decision-making. It concludes that LLAMA 2 sets a new standard for AI in financial data processing and analysis. 2024 IEEE. -
Chemical Reaction-Driven Ferroconvection in a Porous Medium
The effect of chemical reaction on the outset of convection of a ferromagnetic fluid in a horizontal porous layer which is heated from below is studied using small perturbation method. Assuming an exothermic zero-order chemical reaction, the eigenvalues are found by employing the Galerkin method. The effect of magnetic parameters and Frank-Kamenetskii number is discussed. It is established that both magnetic forces and chemical reaction accelerate the threshold of ferroconvection. Further, the fluid layer is destabilized marginally when the nonlinearity of magnetization is strong enough. 2021, Springer Nature Singapore Pte Ltd. -
ChIPSeq Analysis with Bayesian Machine Learning
ChIP-sequencing, otherwise called ChIP-seq, is a technique used to identify protein co-operations with DNA. A crucial advancement to the field of bio-informatics, ChIP sequencing is conducted in research labs around the world to get a better understanding of the way transcription factors and other associated proteins influence the gene in many biological processes and in tackling disease states. ChIP-seq is predominantly a field under the domain of biotechnology, however recent advancements and development of tools to process ChIP data have turned the study into one involving bio-informatics, allowing computer scientists and lab technicians to work on an otherwise scholarly field of biochemistry, molecular biology, microbiology and biomedicine. This report illustrates the predominant work-flow undertaken to sequence chromatin from a cell and to gain insights on the gene/protein of interest. Another aspect added is to use Machine Learning with Bayesian statistical techniques for Peak Calling. The different stages enumerated in this paper have been completed either with the R language or on a Web Server titled Galaxy.org. 2019 IEEE. -
Circuit Breaker: A Resilience Mechanism for Cloud Native Architecture
Over the past decade, the utilization of cloud native applications has gained significant prominence, leading many organizations to swiftly transition towards developing software applications that leverage the powerful, accessible, and efficient cloud infrastructure. As these applications are deployed in distributed environments, there arises a need for reliable mechanisms to ensure their availability and dependability. Among these mechanisms, the circuit breaker pattern has emerged as a crucial element in constructing resilient and trustworthy cloud native applications in recent times. This research article presents a comprehensive review and analysis of circuit breaker patterns and their role within cloud-native applications. The study delves into various aspects of circuit breakers, encompassing their design, implementation, and recommended practices for their utilization in cloud native applications. Additionally, the article examines and compares different circuit breaker libraries available for employment in modern software development. The paper also presents a concept for improving the circuit breaker pattern, which will be pursued in our upcoming research. 2023 IEEE. -
Classification Algorithms Used in the Study of EEG-Based Epileptic Seizure Detection
Epilepsy is a neurological illness that has become more frequent around the world. Nearly 80% of epileptic seizure sufferers live in low- and middle-income nations. In persons with encephalopathy, the risk of dying prematurely is three times higher than in the general population. Three-quarters of people with brain illnesses in low-income countries do not receive the treatment they require. Recurrent seizures are a symptom of epilepsy, characterized by strange bursts of excess energy in mind. Experts agree that most people diagnosed with epilepsy may be managed successfully, provided the episodes are discovered early on. As a result, machine learning plays an essential role in seizure detection and diagnosis. Support Vector Machine(SVM), Extreme Gradient Boosting(Xgboost), Decision Tree Classifier, Linear Discriminant Analysis(LDA), Perceptron, Naive Bayes Classifier, k-Nearest Neighbor(k-NN), and Logistic Regression are eight of the most widely used machine learning classification algorithms used to classify EEG based mostly Epileptic Seizures. Almost all classifiers, according to the study, give an efficient process. Despite this, the results show that SVM is the most effective method for detecting epileptic seizures, with a 96.84% accuracy rate. For diagnosing Epileptic Seizures using EEG signals, the perceptron model has a lower accuracy of 76.21% percent. 2021 IEEE.