Comprehensive Comparative Analysis Research Articles

Multidisciplinary Capstone Senior Design Projects: A Comparative Analysis of Industry–Sponsored and Faculty–Sponsored Projects Using Comprehensive Performance Metrics

Engineering education has continually evolved to embrace Project–Based Learning (PBL), a dynamic classroom approach emphasizing learning through engagement in real–world projects. The study conducts a comparative analysis of multidisciplinary Capstone Senior Design Projects across Electrical Engineering, Mechanical Engineering, and Computer Science at Texas A&M University at Texarkana. The research emphasizes understanding the dynamics of student collaboration within these disciplines and scrutinizes the impact of industry and faculty sponsorship on these projects. The methodology involves a comprehensive comparative analysis, employing diverse performance metrics to evaluate the effectiveness of different sponsorship models. This approach aims to uncover the influence of these models on project outcomes and students’ educational experiences. The results reveal notable differences between industry–sponsored and faculty–sponsored projects. Industry sponsorship leads to higher performance in overall project execution and professional skills development. In contrast, faculty–sponsored projects are more effective in nurturing teamwork and communication abilities among students. The findings suggest that each sponsorship type presents unique benefits and challenges. Industry–sponsored projects provide valuable hands–on problem–solving experience, though they may suffer from inconsistencies in mentorship and varying expectations. Faculty–sponsored projects offer a more stable and consistent educational environment but might lag slightly in performance metrics. Integrating elements from both sponsorship models could provide students with a more balanced and enriching learning experience.

Navigating the electronic landscape: Exploring FPGAs and MCUs architectures in electronic design

Field-Programmable Gate Arrays (FPGAs) and Microcontroller Units (MCUs) are foundational pillars in electronic design, each possessing distinct attributes and applications. This paper embarks on an exploration of these components, delving deep into their unique architectures, performance characteristics, and practical uses. This study, through a comprehensive comparative analysis, examines the strengths and limitations inherent to each of these components. It seeks to serve as a guiding compass for technologists, aiding them in making informed decisions when embarking on electronic design projects. FPGAs emerge as the frontrunners in high-computation endeavors, flexing their computational muscles with finesse. Conversely, MCUs establish their dominance in real-time, low-power applications, where their unwavering reliability is paramount. As electronic systems continue to evolve and infiltrate various aspects of our lives, comprehending the intricacies of FPGA and MCU architectures assumes an increasingly pivotal role for engineers, designers, and researchers. Armed with this knowledge, they can confidently navigate the intricate landscape of electronics, strategically harnessing the power of these two fundamental building blocks to bring innovation and efficiency to the forefront.

Analyzing the Impact of Trade Liberalization in Morocco through Synthetic Control Analysis and the Role of Preferential Policies

This study explores the impact of trade liberalization in Morocco post the 1983 structural adjustment program, utilizing the Synthetic Control method for a comprehensive comparative analysis. Initial findings show a negative impact until 2010, indicating a divergence from the Synthetic Control simulation. However, a positive shift occurs post-2000 due to preferential liberalization policies, leading to sustained economic growth from 2010 onwards. The study underscores the importance of additional liberalization initiatives and structural reforms. While acknowledging contributions, limitations include potential biases in the Synthetic Control method. The research highlights temporal nuances in economic reform impacts, advocating for a long-term perspective in assessing trade liberalization. Beyond Morocco, the study offers insights for policymakers in developing economies, emphasizing the need for selective reform implementation. Overall, it contributes to the understanding of global economic dynamics, prompting further exploration of growth strategies and the intricate interplay between economic reforms, trade liberalization, and sustainable development in developing nations.

An innovative image encryption algorithm enhanced with the Pan-Tompkins Algorithm for optimal security

AbstractThis study introduces a cutting-edge image encryption algorithm aimed at elevating security standards. The Pan-Tompkins Algorithm (PTA) for key generation is proposed for the first time in this study. Additionally, employing steganography through the Least Significant Bit (LSB) method for embedding keys within the encrypted image enhances secure key distribution, thereby fortifying the encryption process. On the other hand, the integration of advanced algorithms, such as Zigzag scanning, the Affine Image Encryption Algorithm (AA), and the Vigenere Image Encryption Algorithm (VA), constitutes the fundamental innovation of the proposed image encryption algorithm. The proposed algorithm is named PanAAVA:Affine Algorithm and Vigenere Algorithm Encryption with PTA-Based Key Generation. The PanAAVA algorithm ensures unparalleled security by encrypting the positions and values of pixels using AA and VA. Notably, using PTA for key generation marks a distinctive and new key generation method feature of the algorithm. To assess the effectiveness of the PanAAVA, a comprehensive comparative analysis is conducted against well-established encryption methodologies, including Lena, Baboon, Airplane, and Pepper.The PanAAVA demonstrates exceptional proficiency in histogram analysis. The PanAAVA demonstrates a Unified Average Changing Intensity (UACI) of 33.4044%. Additionally, the Number of Pixels Change Rate (NPCR) is measured at 99.7442%, showcasing the algorithm’s effectiveness in inducing significant pixel changes. The proposed algorithm’s Mean Square Error (MSE) is calculated at 3.20679E5%. The proposed algorithm’s Peak Signal to Noise Ratio (PSNR) is recorded at 9.512475. The Key Space Size of the proposed algorithm is measured at 2209. Regarding correlation analysis, the PanAAVA achieves a high correlation score of 7.9996. The proposed algorithm successfully passes the National Institute of Standards and Technology (NIST) analysis, demonstrating a remarkably strong correlation close to 0 and a Structural Similarity Index Measure (SSIM) of 0.9977. Furthermore, regarding quantum communication, the proposed algorithm maintains stable key rates of 47.5 ± 0.8 kHz during the day and 50.9 ± 0.7 kHz at night. Additionally, PanAAVA achieves low Quantum Bit Error Rate (QBER) values of 4.77 ± 0.02, ensuring reliable and secure communication. The PanAAVA also demonstrates robust asymmetries at 49.81 ± 0.02 and 50.14 ± 0.03 for a crystal length of 20 mm. highlighting PanAAVA’s adaptability and effectiveness in different scenarios. PanAAVA outperforms other encryption algorithms concerning performance measurements and comparisons. In conclusion, the PanAAVA emerges as a beacon of superior security capabilities and innovation in image encryption, showcasing the potential to redefine standards in the field.

A Comparative Study on the Removal of Fluorides from Groundwater: Strategies and Technologies

This study conducts a comprehensive comparative analysis of various strategies and technologies employed for the removal of fluorides from groundwater, addressing the increasing concern over elevated fluoride levels in water sources. High fluoride concentrations pose significant health risks, including dental and skeletal fluorosis, necessitating effective and sustainable removal methods. The research evaluates the efficiency, cost-effectiveness, and environmental impact of different fluoride removal techniques, providing valuable insights for water treatment practitioners, policymakers, and communities grappling with fluoride-contaminated groundwater. The study begins by outlining the prevalence of fluoride contamination in groundwater and the associated health risks, establishing the critical need for reliable removal methods. Subsequently, a review of existing literature presents an overview of conventional and emerging technologies utilized for fluoride removal. Conventional methods such as coagulation, precipitation, and adsorption are examined, considering their efficiency, operational ease, and cost implications. The study then delves into advanced technologies like membrane processes, ion exchange, and electrochemical methods, assessing their applicability and effectiveness in diverse groundwater contexts. Environmental sustainability is a key focus throughout the analysis, with a critical examination of the by-products and waste generated by each removal method. The ecological footprint, including energy consumption and material usage, is considered to provide a holistic understanding of the environmental impact associated with fluoride removal technologies. Furthermore, the economic feasibility of each method is evaluated, considering capital and operational costs. Comparative cost analyses offer insights into the affordability and scalability of different removal technologies, aiding decision-makers in selecting suitable methods based on financial constraints. The study concludes with a synthesized overview of the comparative analysis, emphasizing the strengths and limitations of each fluoride removal method. In summary, this comparative study contributes to the broader understanding of fluoride removal technologies, facilitating informed decision-making in addressing groundwater contamination. The findings are anticipated to inform researchers, water treatment professionals, and policymakers, guiding the selection of appropriate and context-specific fluoride removal methods to ensure safe and potable groundwater sources

Cricket data analytics: Forecasting T20 match winners through machine learning

In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting.

Mechanical Behavior of Precast Circular Semi-Continuous CFST Columns under a Uniaxial Eccentric Load

For this study, we conducted a detailed examination and comprehensive comparative analysis of the structural responses and mechanical behavior of bolted sleeve connections in precast circular semi-continuous steel tubular concrete (PCSCFST) columns. The research involved fourteen specimens, and we considered the impacts of various parameters, including eccentricity, external steel sleeve thickness, bolt diameter, and slenderness ratio. The findings revealed that the external steel sleeve significantly enhances the protection of the connection area, enabling the bolts to effectively withstand eccentric loads. However, sleeves that are too thick may lead to premature bolt failure, reducing their ultimate load-bearing capacity. Using bolts to transfer loads to the concrete significantly strengthens the restraining effect of the steel sleeve. Nonetheless, increasing the bolt diameter beyond a certain threshold may diminish this beneficial impact, potentially leading to connection failure and a decrease in ultimate load-bearing capacity. A new ‘cooperative value q’ measures component collaboration at ultimate capacity, showing that shorter columns offer less effective coordination than longer ones. Through regression analysis, we formulated a prediction for axial ultimate bearing capacity, closely aligning with the experimental data (Npre-a/Nu average value of 1.003, variance 0.00248). Three N–M curves, including the Eurocode 4 method, offered conservative predictions, with Eurocode 4 closely matching the experimental results. A refined prediction method following Eurocode 4 was developed, yielding an average Ppre-U/Pu value of 0.971 and a variance of 0.0107.

Comparison of glycation and glycosylation level between bovine milk-based and goat milk-based infant formula through label-free proteomics techniques

Heat treatment is unavoidable in the processing of infant formula; however, heat treatment causes the proteins to undergo Maillard reaction and form glycation products, which can negatively affect the nutrition and function of the protein. At the same time, glycosylation is a broad post-translational protein modification that is important in a variety of biological processes. However, glycated proteins as well as N-glycoprotein components in infant formula have not been well characterized. Here, a comprehensive comparative analysis of the glycated proteome as well as the N-glycosylated proteome was conducted in both bovine milk-based (BF) and goat milk-based (GF) infant formula. In total, 918 whey proteins were identified from the whey fraction, including 200 glycated proteins and 219 glycosylated proteins. Furthermore, 33 N-glycosylated proteins containing 101 N-glycosylation sites and 64 glycosylated peptides derived from 30 proteins were significantly different (p < 0.05) between BF and GF. Most of the significantly different proteins are associated with innate immunity as well as defense responses. With respect to the differences between BF and GF, ɑ-lactalbumin (LALBA) had higher glycation level in BF than GF; whereas β-lactoglobulin (LGB) had higher glycation level in GF than BF. For glycosylation level, transferin (TF) was highly glycosylated in GF than BF; while serum albumin (ALB) was highly glycosylated in BF than GF. Our results elucidated the proteome, glycated proteome, N-glycosylated proteome in bovine milk-based and goat milk-based infant formulas, which may be useful to researchers in related fields and provide guidance on the improvement of processing technology for infant formula.

An integrated multi-criteria approach to formulate and assess healthcare referral system strategies in developing countries

This study aims to identify challenges in implementing a quality healthcare referral system in developing countries and explore the strategies to overcome these challenges. Data for this study were collected through consultations with experts in the field. We introduce a novel hybrid method called Criteria Importance Assessment (CIMAS) and Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN). CIMAS determines the relative importance of criteria, and AROMAN is employed to rank the strategies. The primary challenges identified include inadequate infrastructure facilities and deficient health information systems. The most appropriate strategy involves focusing on improving infrastructure facilities. We also carry out comprehensive sensitivity and comparative analyses to validate the applicability of the proposed model. This study identifies and elucidates the challenges of establishing a high-quality healthcare referral system in developing countries and substantially contributes to the existing body of knowledge by effectively delineating and prioritizing the strategies to tackle these challenges.

A Comparative Analysis of Machine Learning and Deep Learning Approaches for Prediction of Chronic Kidney Disease Progression

Chronic kidney disease is a significant health problem worldwide that affects millions of people, and early detection of this disease is crucial for successful treatment and improved patient outcomes. In this research paper, we conducted a comprehensive comparative analysis of several machine learning algorithms, including logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, Support Vector Machine, X Gradient Boosting, Decision Tree Classifier, Grid Search CV, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, XgBoost, Cat Boost Classifier, Extra Trees Classifier, KNN, MLP Classifier, Stochastic gradient descent, and Artificial Neural Network, for the prediction of kidney disease. In this study, a dataset of patient records was utilized, where each record consisted of twenty-five clinical features, including hypertension, blood pressure, diabetes mellitus, appetite and blood urea. The results of our analysis showed that Artificial Neural Network (ANN) outperformed other machine learning algorithms with a maximum accuracy of 100%, while Gaussian Naive Bayes had the lowest accuracy of 94.0%. This suggests that ANN can provide accurate and reliable predictions for kidney disease. The comparative analysis of these algorithms provides valuable insights into their strengths and weaknesses, which can help clinicians choose the most appropriate algorithm for their specific requirements.

Low-coverage whole genome sequencing of diverse Dioscorea bulbifera accessions for plastome resource development, polymorphic nuclear SSR identification, and phylogenetic analyses.

Dioscorea bulbifera (Dioscoreaceae), a versatile herbaceous climber native to Africa and Asia, holds significant nutritional and medicinal value. Despite extensive characterization and genetic variability analyses of African accessions, studies on the genetic variation of this species in China are limited. To address this gap, we conducted low-coverage whole genome sequencing on D. bulbifera accessions from diverse regions across mainland China and Taiwan island. Our initial investigation encompassed comprehensive comparative plastome analyses of these D. bulbifera accessions, and developing plastome resources (including plastome-derived repetitive sequences, SSRs, and divergent hotspots). We also explored polymorphic nuclear SSRs and elucidated the intraspecific phylogeny of these accessions. Comparative plastome analyses revealed that D. bulbifera plastomes exhibited a conserved quadripartite structure with minimal size variation mainly attributed to intergenic spacer regions, reinforcing prior observations of a high degree of conservation within a species. We identified 46 to 52 dispersed repeats and 151 to 163 plastome-derived SSRs, as well as highlighted eight key divergent hotspots in these D. bulbifera accessions. Furthermore, we developed 2731 high-quality candidate polymorphic nuclear SSRs for D. bulbifera. Intraspecific phylogenetic analysis revealed three distinct clades, where accessions from Southeast China formed a sister group to those from South China and Taiwan island, and collectively, these two clades formed a sister group to the remaining accessions, indicating potential regional genetic divergence. These findings not only contributed to the understanding of the genetic variation of D. bulbifera, but also offered valuable resources for future research, breeding efforts, and utilization of this economically important plant species.

Exploring the dependability of Combined Ratio Estimators in Stratified Ranked Set Sampling: Insights from COVID-19 data

In this study, we introduce a novel combined ratio type estimator within the framework of Stratified Ranked Set Sampling to estimate the population mean of the study variable by incorporating bivariate auxiliary information. We conduct a comprehensive comparative analysis, including traditional combined ratio, combined regression, Shabbir and Khan [13], and Bhushan and Kumar [37] estimators. We assess the bias and mean squared error of the proposed estimator under the initial degree of approximation. The data source consists of COVID-19 data up to July 2023. Through empirical investigation and simulation studies, our proposed estimator consistently demonstrates superior performance compared to its counterparts, exhibiting the highest relative efficiency. These findings underscore the practical significance of our research in public health and decision-making, emphasizing the potential of this estimator to provide more accurate and reliable estimates in various applications involving ranked set sampling and auxiliary information.

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

This research focuses on a comprehensive comparative analysis of shear strength prediction in slab–column connections, integrating machine learning, design codes, and finite element analysis (FEA). The existing empirical models lack the influencing parameters that decrease their prediction accuracy. In this paper, current design codes of American Concrete Institute 318-19 (ACI 318-19) and Eurocode 2 (EC2), as well as innovative approaches like the compressive force path method and machine learning models are employed to predict the punching shear strength using a comprehensive database of 610 samples. The database consists of seven key parameters including slab depth (ds), column dimension (cs), shear span ratio (av/d), yield strength of longitudinal steel (fy), longitudinal reinforcement ratio (ρl), ultimate load-carrying capacity (Vu), and concrete compressive strength (fc). Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. PSOFNN predicted the punching shear of flat slab with maximum accuracy with R2 value of 99.37% and least MSE and MAE values of 0.0275% and 1.214%, respectively. The findings of the study are validated through FEA of slabs to confirm experimental results and machine learning predictions that showed excellent agreement with PSOFNN predictions. The research also provides insight into the application of metaheuristic models along with ANN, revealing that not all metaheuristic models can outperform ANN as usually perceived. The study also highlights superior predictive capabilities of EC2 over ACI 318-19 for punching shear values.

Optimal age replacement time for coherent systems under Geometric Point Process

In spatial context, the understanding of event dynamics and point distributions is a fundamental requirement. This study addresses a critical need by introducing the Geometric Point Process (GPP) and explaining its potential impact on optimizing the determination of optimal replacement ages for coherent systems. Our paper has three overarching aims: firstly, to bridge the gap by presenting the novel GPP model that effectively captures the geometric distribution of points, filling a void in point process theory. Secondly, we calculate the optimal replacement age for the coherent system based on the principles of GPP. This involves employing the Infinitesimal-Look Ahead stopping rule within the framework of Smooth Semi-Martingale (SSM) decomposition. A comprehensive comparative analysis is conducted to assess the distinct features and advantages of the GPP in relation to other point processes, offering valuable insights into its applicability and performance. Lastly, we extend the practical application of GPP by examining optimal replacement strategies for components in coherent systems, offering a bridge between theory and real-world practices, especially in the context of aircraft and wind turbine component deterioration. Additionally, we conduct reliability and sensitivity analyses, providing management suggestions, and present Monte Carlo simulations as an alternative method for validating optimal solutions. This research invites the industrial community to utilize the proposed replacement policy, paving the way for exploring geometric characteristics in stochastic processes. The timeliness and significance of this work are underscored by the relatively unexplored field of smooth semi-martingales, with the most recent publication dating back to 2015 by Bueno.

A machine learning approach to characterise fabrication porosity effects on the mechanical properties of additively manufactured thermoplastic composites

The investigation of the mechanical properties of additively manufactured (AM) composite has been the focus of several research over the past decades. However, testing constraints of time and cost have encouraged the exploration of more pragmatic methods such as machine learning (ML) for predicting these characteristics. This study builds on experimental investigations of the flexural, tensile, compressive, porosity, and hardness properties of 3D printed carbon fibre-reinforced polyamide (CF-PA) and carbon fibre-reinforced acrylonitrile butadiene styrene (CF-ABS) composites, proposing the application of ML for predicting these mechanical properties. A comprehensive comparative analysis of various machine learning approaches was executed, with a resultant accuracy ranging between 80 and 99%. The results unveiled the superior predictive performance of ensemble tree learners and the K-NN regressor algorithms when temperature and porosity are selected (based on correlation analysis) as predictors for material hardness and strength in tension, compression, and flexion. In particular, the model built on the extra-tree regressor algorithm demonstrated a remarkably robust fit, with R-squared evaluation scores of 0.9993 and 0.9996 for CF-PA and CF-ABS, respectively. This work develops a ML model that relates porosity to the other mechanical properties of AM composites and the prediction models’ exceptional accuracy, along with their precise alignment with experimental data, provide invaluable insights for the autonomous control and data-driven optimization of the structures.

Integrated study on the deterioration investigation and intervention treatment of rural vacant traditional dwellings under population contraction

ABSTRACT The rapid advancement of urbanization in China has exacerbated the contraction of many rural populations and as a result, the abandonment of many traditional dwellings. Therefore, the aim of this study was to properly analyze the deterioration of vacant traditional dwellings and propose ways to intervene and gradually solve the problem of vacant housing in rural areas. In this study, Wushao Village in Xuzhou City was selected for a pilot study of a typical village with a shrinking population. The research methods involved abundant field investigations to clarify the construction techniques and gather information on the deterioration of vacant traditional dwellings. Additionally, a comprehensive comparative analysis was performed to determine the appropriate treatments for deterioration problems. The results indicated that the vacant traditional dwellings in Wushao Village had various types of decay. Corresponding intervention measures and steps were proposed based on the specific causes of deterioration. This study demonstrated that comprehensive deterioration investigations and intervention treatments were able to effectively rehabilitate the integral status and preserve the authenticity of rural traditional dwellings. This will be conducive to the continuation and revitalization of the traditional features of villages.

The Three Rabbits with Common Ears: The Trajectory of Dunhuang Murals.

Over the course of thousands of years, the weathered murals have once again become apparent. Among them, the “Three Rabbits Sharing Ears” caisson pattern is a unique decorative element in Dunhuang murals, depicting the unique scene of “Three Rabbits Sharing Ears”. Therefore, the symbolism behind it has attracted academic attention. This article introduces the development history and cultural symbols of “caisson”, the symbolism of the “Three Rabbits Sharing Ears” pattern, and its manifestation as a “rail object paradigm”. Through comprehensive literature research and comparative analysis of other themes, this article aims to reveal that the “Three Rabbits Sharing Ears” caisson represents family unity, prosperity, and fertility prayers, providing a new perspective for further understanding the cultural connotation and artistic expression of Dunhuang murals.

Comparative Analysis of the Mitochondrial Genomes of Chloropidae and Their Implications for the Phylogeny of the Family.

Chloropidae, commonly known as grass flies, represent the most taxonomically diverse family of Diptera Carnoidea, comprising over 3000 described species worldwide. Previous phylogenetic studies of this family have predominantly relied on morphological characters, with mitochondrial genomes being reported in a few species. This study presents 11 newly sequenced mitochondrial genomes (10 Chloropidae and 1 Milichiidae) and provides the first comprehensive comparative analysis of mitochondrial genomes for Chloropidae. Apart from 37 standard genes and the control region, three conserved intergenic sequences across Diptera Cyclorrhapha were identified in all available chloropid mitochondrial genomes. Evolutionary rates within Chloropidae exhibit significant variation across subfamilies, with Chloropinae displaying higher rates than the other three subfamilies. Phylogenetic relationships based on mitochondrial genomes were inferred using maximum likelihood and Bayesian methods. The monophyly of Chloropidae and all four subfamilies is consistently strongly supported, while subfamily relationships within Chloropidae remain poorly resolved, possibly due to rapid evolution.

A systematic review on deep learning-based automated cancer diagnosis models.

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.

Robot Indoor Navigation: Comparative Analysis of LiDAR 2D and Visual SLAM

&lt;span lang="EN-US"&gt;Robot indoor navigation has become a significant area of research and development for applications such as autonomous robots, smart homes, and industrial automation. This article presents an in-depth comparative analysis of LiDAR 2D and visual sensor simultaneous localization and mapping (SLAM) approaches for robot indoor navigation. The increasing demand for autonomous robots in indoor environments has led to the development of various SLAM techniques for mapping and localization. LiDAR 2D and visual sensor-based SLAM methods are widely used due to their low cost and ease of implementation. The article provides an overview of LiDAR 2D and visual sensor-based SLAM techniques, including their working principles, advantages, and limitations. A comprehensive comparative analysis is conducted, assesing their capabilities in terms of robustness, accuracy, and computational requirements. The article also discusses the impact of environmental factors, such as lighting conditions and obstacles, on the performance of both approaches. The analysis’s findings highlight each approach’s strengths and weaknesses, providing valuable insights for researchers and practitioners in selecting the appropriate SLAM method for robot indoor navigation based on specific requirements and constraints&lt;/span&gt;