Finite element analysis and optimization studies on tibia implant of SS 316L steel and Ti6Al4V alloy.

Tibial fractures account for approximately 15% of all fractures, typically resulting from high-energy trauma. A critical surgical approach to treat these fractures involves the fixation of the tibia using a plate with minimally invasive osteosynthesis. The selection and fixation of the implant plate are vital for stabilizing the fracture.

Validation of effect of composite additive on optimized combustion characteristics of CI engine using AHP and GRA method.

The primary focus of this study is the validation of composite additives with the help of additional optimization methods and the analysis of its effect on the combustion characteristics of compression ignition (CI) engines. Previous work on the identification of the correct multi additive combination by Taguchi and the TOPSIS optimization method has shown substantial improvements in the performance and emission characteristics of CI engines. The same work was extended using the GRA Optimization method with the Multi-Criteria Decision-Making (MCDM) optimization technique known as the Analytic Hierarchy Process (AHP) to validate the optimization results from the previous optimization work.

Effect of Guar Gum Content on the Mechanical Properties of Laterite Soil for Subgrade Soil Application.

Using biopolymers for soil stabilization is favorable compared to more conventional methods because they are more environmentally friendly, cost-effective, and long-lasting. This study analyzes the physical properties of guar gum and laterite soil mixes. A comprehensive engineering study of guar gum-treated soil was conducted with the help of a brief experimental program.

Reducing non-value added (NVA) activities through lean tools for the precast industry.

Lean management is a strategic approach that is used in construction industry, specifically aims at minimizing and ultimately eliminating non-value-adding activities, commonly referred to as waste, within construction projects. However, an increase in non-value added (NVA) activities within the precast industry has the potential to diminish both productivity and efficiency. The aim of this paper is to investigate the use of lean tools for minimizing NVA activities in the construction industry.

CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer.

Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening for cervical cancer is the pap smear test.

Radiomics for Parkinson's disease classification using advanced texture-based biomarkers.

Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities.

Modification in Toxicity of l-Histidine-Incorporated ZnO Nanoparticles toward .

This paper presents a comparative study of the toxicity of pristine-ZnO and l-histidine-incorporated ZnO toward () as a Gram-negative model organism. Pristine-ZnO and l-histidine-incorporated ZnO with different l-histidine concentrations were synthesized using an open aqueous solution bath technique. XRD studies revealed the formation of polycrystalline wurtzite ZnO.

Dry fruit image dataset for machine learning applications.

Dry fruits are convenient and nutritious snacks that can provide numerous health benefits. They are packed with vitamins, minerals, and fibres, which can help improve overall health, lower cholesterol levels, and reduce the risk of heart disease. Due to their health benefits, dry fruits are an essential part of a healthy diet.

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error.

ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams.

Colposcopy plays a vital role in detecting cervical cancer. Artificial intelligence-based methods have been implemented in the literature for the classification of colposcopy images. However, there is a need for a more effective method that can accurately classify cervigrams.

An Intelligent Classification System for Cancer Detection Based on DNA Methylation Using ML and Semantic Knowledge in Healthcare.

To consistently assess a patient's internal and external wellness and diagnose chronic conditions like cancer, Alzheimer's disease, and cardiovascular disease, wearable sensing devices are being used. Wearable technologies and networking websites have become incredibly common in the medical sector in recent times. The condition of a patient's health can be influenced by a number of factors, including psychological response, emotional stability, and anxiety levels, which can be evaluated using social network analysis based on graph theory-based techniques and these ideas, known as "social network analysis" (SNA) are used to study relationship phenomena.

Synthesis, Structural and Optical Properties of ZrBiSe Nanoflowers: A Next-Generation Semiconductor Alloy Material for Optoelectronic Applications.

ZrBiSe nanoflower-like morphology was successfully prepared using a solvothermal method, followed by a quenching process for photoelectrochemical water splitting applications. The formation of ZrBiSe was confirmed by field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). The estimated value of work function and band gap were found to be 5.

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions.

With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users' emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks.

Blockchain-Based Optimization Model for Evaluating Psychological Mental Disease and Mental Fitness.

The current work describes a blockchain-based optimization approach that mimics the psychological mental illness evaluation procedure and evaluates mental fitness. Combining lightweight models with blockchains can give a variety of benefits in the healthcare business. This study aims to offer an improved review and learning optimization technique (SPLBO) based on the social psychology theory to overcome the biogeography-based optimization (BBO) algorithm's shortcomings of low optimization accuracy and instability.

Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer.

At present, early lung cancer screening is mainly based on radiologists' experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules.

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization.

DeepCyto: a hybrid framework for cervical cancer classification by using deep feature fusion of cytology images.

Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening cervical cancer is the pap smear test.

Identification and Classification of Depressed Mental State for End-User over Social Media.

In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression.

Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features.

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image.

Multi technique amalgamation for enhanced information identification with content based image data.

Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification.