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Comparative Study of AI Models for Automated Tuberculosis Detection Using Image Processing Techniques
Tuberculosis is a critical global health issue, particularly in resource-limited regions where early and accurate diagnosis is important and is in need so that the treatment is effective and the control transmission is controlled. The known diagnostic methods, such as sputum smear microscopy and nucleic acid amplification tests are costly, time-consuming, and require trained professionals. Due this in some cases it is inaccessible in many regions. Deep learning-based automated TB detection offers a promising alternative by enhancing diagnostic efficiency through medical imaging analysis. This study presents a comparative evaluation of five deep learning models, InceptionResNetV2, DenseNet, VGG16, ANN, and a custom CNN, trained on a dataset of 3,008 chest radiograph images, evenly distributed between TB-positive and normal cases. The dataset underwent advanced preprocessing techniques, pixel normalization, and data augmentation. The hyperparameter tuning process was applied, which optimized the learning rates, dropout rates, convolutional filter sizes, and batch sizes to enhance model performance. The models were assessed using accuracy, precision, recall, F1-score, sensitivity, specificity.. Experimental results indicated that the custom CNN achieved the highest classification accuracy (99.51). The superior performance of the custom CNN over other models is attributed to optimized feature extraction, effective preprocessing, and structured hyperparameter tuning. A comparative analysis with previous studies highlights how this approach mitigates dataset limitations and improves model interpretability, and the potential of AI-driven TB detection, enhancing future diagnostic efficiency by improving model generalizability and deployment in real-world healthcare settings. 2025 IEEE. -
The Role of Semiotics in Digital Marketing: Study on Consumer Interpretation and Brand Communication
This paper aims to examine the concept of semiotics with an emphasis on the manner in which consumers analyse brand messages through signifying practices. Being the science of meaning, semiotics in central to the formation of perception towards brand image and communication. Given the proliferation of digital communications in many different channels, it is vital for modern marketing to engage with semiotic features. Hence, the present research uses semi structured interviews as its data collection method to explore how consumers assign meaning to visuals and the symbols used to depict them as a means of gaining their attention and securing their attention and loyalty. Research results indicates semiotic assets including picture, iconography, and storyline boost brand familiarity/identity and consumer bond. Culture- sensitive signs enhances perception and ensure that messages passed do not create confusion. These observations present marketers with valuable information useful when developing cultural and interpretational strategies in the market. 2026, IGI Global Scientific Publishing. -
Inpatient complaining behaviour: A study on the overt and covert behaviour of inpatients in Indian hospitals
Consumer dissatisfaction and complaining behaviour have always been a topic of discussion in educational institutes and industries alike. Whereas dissatisfaction with product purchases and subsequent returns or associated consumer responses is very common, the same in the service sector has been quite different. In India, it is not only the patient who decides, which healthcare service to opt for, because Indians are culturally embedded in a system of collective consumption where other family members or relatives or friends also influence their decision-making. This paper is an exploratory study done to comprehend the chosen behavioural responses of dissatisfied inpatients in India through a questionnaire survey. The survey followed a retrospective recall technique in which the recall window was fixed at six months. The sampling technique followed was probability sampling. The data collection tool was structured and self-administered questionnaire administered in the sampled nine districts of Kerala. A good number of respondents attributed their overt complaining behaviour to lack of cordiality of doctors, nurses or the attending staff and lack of proper care and concern from doctors or nurses. Post complaining, service recovery was found to be satisfactory for most of the complainers. 2020, Kamala-Raj Enterprises. All rights reserved. -
Narrating Trauma as Victims of Human Trafficking in China: A Study on Select North Korean Memoirs
The memoirs titled In Order to Live; A North Korean Girl's Journey, to Freedom and; A Thousand Miles to Freedom: My Escape from North Korea are written by Yeonmi Park and Eunsun Kim two women who managed to escape from North Korea. They went through an experience of being forced into labour in China as victims of trafficking. In their memoirs these authors vividly depict the pain that comes with being exploited. The main aim of this study is to analyse how memoirs can effectively address the issue of trafficking. These remarkable women skilfully use the memoir genre to make a personal plea for action. They strategically make choices appeal to readers emotions openly share their distressing experiences and support their stories with research and evidence that connect their experiences with the broader problem of human trafficking in China. This study clearly shows that both these memoirs emphasize the importance of the memoir genre in advocating for rights. It also highlights how survivor memoirs have the potential to inspire advocacy and involvement, in combating trafficking. 2025 Sciedu Press. All rights reserved. -
Narrating Trauma as Victims of Human Trafficking in China: A Study on Select North Korean Memoirs
The memoirs titled In Order to Live; A North Korean Girl's Journey, to Freedom and; A Thousand Miles to Freedom: My Escape from North Korea are written by Yeonmi Park and Eunsun Kim two women who managed to escape from North Korea. They went through an experience of being forced into labour in China as victims of trafficking. In their memoirs these authors vividly depict the pain that comes with being exploited. The main aim of this study is to analyse how memoirs can effectively address the issue of trafficking. These remarkable women skilfully use the memoir genre to make a personal plea for action. They strategically make choices appeal to readers emotions openly share their distressing experiences and support their stories with research and evidence that connect their experiences with the broader problem of human trafficking in China. This study clearly shows that both these memoirs emphasize the importance of the memoir genre in advocating for rights. It also highlights how survivor memoirs have the potential to inspire advocacy and involvement, in combating trafficking. 2025 Sciedu Press. All rights reserved. -
Efficacy of a peer-delivered group psychological intervention to reduce psychological distress among university students in India: a randomised controlled trial using an active control condition; [Eficacia de una intervenci psicolica grupal realizada por pares para la reducci del malestar psicologico en estudiantes universitarios en India: ensayo clico aleatorizado empleando una condici de control activo]
Background: Brief psychological interventions in low-and-middle-income-countries (LMICs) have been typically tested against usual or enhanced usual care (EUC). This design precludes understanding of the role of non-specific factors in influencing outcomes. Objective: This study evaluated an adapted version of WHOs Problem Management Plus (gPM+), titled Coping with COVID, against an active control condition to reduce anxiety and depression during the COVID-19 pandemic. Methods: In this two-arm, single-blind, randomised controlled trial, young adults aged 1824 years who screened positive for COVID-19 related psychological distress in Bengaluru (India) were randomly allocated to either Coping with COVID (n = 91) or non-directive Supportive Counselling (SC; n = 92) groups. Coping with COVID was a 6-sesion, group-based programme that taught coping strategies for stress. SC was a 6-sesion, group-based programme that offered non-directive support. The primary outcomes were anxiety and depression as measured by the Hospital Anxiety and Depression Scales (HADS) assessed at baseline, post-intervention, 2-months (primary outcome timepoint), and 6-months after treatment. Secondary outcomes included generalised worry, positive wellbeing, pandemic-related stress, and suicidal ideation. Results: Between October 2021 and December 2022, 183 participants were enrolled into the trial. Relative to SC, Coping with COVID did not lead to significant reductions in anxiety (mean difference 0.24 [95% CI, ?1.01,1.48], p>.05), or depression (mean difference.03 [95% CI, ?1.19, 1.26], p>.05). Similarly, there were no significant differences between conditions for all secondary outcomes. Conclusions: The findings suggest that the benefits of strategies that comprise transdiagnostic scalable psychological interventions may not surpass non-specific factors in driving symptom reduction. Clinical implications: There is a need to further evaluate the role of non-specific factors in scalable psychological programmes because focusing on these may have implications for ease of training and implementation. Trial registration:Australian New Zealand Clinical Trials Registry identifier: ACTRN12621001064897. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Videoconferencing-delivered psychological intervention for the treatment of COVID-19 related psychological distress in University students: study protocol for a randomised controlled trial in India
Background: The mental health impacts of the COVID-19 pandemic have been profound. This paper outlines the study protocol for a trial that tests the efficacy of a brief group-based psychological intervention (Coping with COVID; CWC), relative to Supportive Counselling, to reduce distress associated with COVID-19 in a young adult population in Bangalore, India. Methods: A single-blind, parallel, randomized controlled trial will be carried out via video conferencing in a small group format. Following informed consent, adults that screen positive for levels of psychological distress (Kessler 10 (K-10 score ? 20) and have access to a videoconferencing platform will be randomised to an adapted version of CWC (n = 90) or Supportive Counselling (SC) (n = 90). The primary outcome will be reduction in psychological distress including anxiety and depression at 2-months post treatment. Secondary outcomes include worry, positive wellbeing, and stress in relation to COVID-19. Discussion: This treatment trial will assess whether CWC will result in reduced distress relative to Supportive Counselling in a young adult population in Bangalore, India. This study will yield important insights into the role of nonspecific factors versus the interventions components in impacting COVID-19 related distress. Trial registration: This trial was prospectively registered on the Australian New Zealand Clinical Trials Registry (ACTRN12621001064897). Ethics and dissemination: Ethics approval has been obtained from the participating institution, CHRIST University in Bangalore. Results of the trial will be submittedfor publication in peer reviewed journals and findings presented at scientific conferences and to key service providers and policy makers. 2022, The Author(s). -
A Comparative Performance Analysis of Convolution W/O OpenCL on a Standalone System
Initial approach of this paper is to provide a deep understanding of OpenCL architecture. Secondly, it proposes an implementation of a matrix and image convolution implemented in C (Serial Programming) and OpenCL (Parallel Programming), to describe detailed OpenCL programming flow and to provide a comparative performance analysis. The implementation is being carried on AMD A10 APU and various algebraic scenarios are created, to observe the performance improvement achieved on a single system when using Parallel Programming. In the related works authors have worked on AMDAPPSDK samples such as N-body & SimpleGL to understand the concept of vector data types in OpenCL and OpenCL-GL interoperability, have also implemented 3-D particle bouncing concept in OpenCL & 3D-Mesh rendering using OpenCL. Lastly, authors have also illuminated about their future work, where they intend to implement a novel algorithm for mesh segmentation using OpenCL, for which they have tried to form a strong knowledge base through this work. 2015 IEEE. -
Bioinformatics Research Challenges and Opportunities in Machine Learning
This research work has studied about the utilization of machine learning algorithms in bioinformatics. The primary purpose of studying this is to understand bioinformatics and different machine algorithms which are used to analyze the biological data present with us. This research study discusses about different machine learning approaches like supervised, unsupervised, and reinforcement which play an essential role in understanding and analyzing biological data. Machine learning is helping us to solve a wide range of bioinformatics problems by describing a wide range of genomics sequences and analyzing vast amounts of genomic data. One of the biggest real-world problems is that machine learning is helping us to identify cancer with a given gene expression, which is done using a support vector machine. In addition, this study discusses about the classification of molecular data, which will help find out minor diseases. With the advancement of machine learning in healthcare and other related applications, data collection becomes a tedious process. This article also focuses on some of the research problems in machine learning domain. The uses of machine learning algorithms in bioinformatics have been extensively studied. These objectives will help to understand bioinformatics and different machine algorithms that are used to analyze the biological data. This research study presents different machine learning approaches like supervised, unsupervised, and reinforcement, which play an important role in understanding and analyzing biological data. Machine learning helps to solve a wide range of bioinformatics related challenges by describing a wide range of genomics sequences and analyzing huge amounts of genomic data. One of the biggest real-time challenges is that the machine learning is helping to identify cancer with a given gene expression, and this is done by using a support vector machine. Finally, this research study has discussed about the classification of molecular data, which will be helpful in finding out minor diseases. 2022 IEEE. -
Flexural performance of FDM-fabricated PLA composites reinforced with short carbon fiber
Adding short carbon fiber reinforcement to thermoplastic matrices can improve the mechanical performance of additively manufactured polymer-based composites. This work examines the flexural behavior of Fused Deposition Modeling (FDM)-produced polylactic acid (PLA)-based composites enhanced by short carbon fibers at 3 wt% and 6 wt%. For comparison, unreinforced PLA specimens were also fabricated under identical processing conditions. All the samples were tested for flexural strength using a three-point bend test, following the ASTM standards for polymer composites. The results showed a clear improvement in strength as the reinforcement content increased. The PLA composite with the 3% short carbon fiber reinforcement showed a noticeable increase in load-bearing ability, while the 6% reinforced composite had an impressive 88% higher flexural strength than the plain PLA. To examine how the materials failed, SEM has been utilized to assess the samples' fractured surfaces. 2026, Gruppo Italiano Frattura. All rights reserved. -
Experimental Investigation and Predictive Modelling of Tribological Behaviour in Fly Ash Reinforced Polymer Composites Fabricated via Stereolithography
Tribological evaluation of fly ash-reinforced UV-curable resin composites produced through stereolithography additive manufacturing formed the central focus of this study. Controlled fly ash additions extending up to 2% by weight were examined, and all tests ran under dry sliding conditions. Neat resin was designated as the experimental reference throughout. Even with the modest addition of 0.5% fly ash, wear came down by roughly 9.8%, which was small but directionally significant. Moving to 1% fly ash pushed wear reduction to 16.4%. At 1.5%, the reduction advanced further to 21.3% with all evaluations performed under identical testing conditions. The 2% fly ash specimen produced the highest wear reduction of 25.8% among all compositions examined. Frictional behavior followed a comparable declining trend across filler levels. At 0.5% fly ash COF fell 7.6% below the neat resin value, and at 1% that reduction grew to 12.9%. The 1.5% addition brought friction reduction to 17.4%. Total COF reduction at 2% fly ash reached 20.3%, which confirmed a steady and consistent frictional improvement with progressive filler incorporation. Linear regression and artificial neural networks and XGBoost-based gradient boosting were the three predictive frameworks developed alongside experimental work. All three tracked closely with measured tribological outcomes. Prediction accuracies reached up to 95.4% with lower error values documented across every model. The 2% fly ash formulation stood out as the most effective composition tested. It delivered the strongest combined improvements in wear resistance and friction stability while remaining fully compatible with the operational demands of additive manufacturing. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches
This study evaluates advanced machine learning (ML) and deep learning (DL) models for predicting the tensile shear and bending strength of friction stir spot welding joints involving Al 5754 and Al 6111 alloys. ML techniques include Linear Regression, Decision Tree, Random Forest (RF), K-Nearest Neighbors, Support Vector Regression, and XGBoost, while DL models comprise Recurrent Neural Network (RNN) and Backpropagation Neural Network (BPNN). The models were assessed for discrepancies between experimental and predicted results, with the best-performing model identified using R-squared (R2), Root-Mean-Square Error, Mean Square Error, and Mean Absolute Error. The data preprocessing phase included feature scaling and an 85:15 train-test split. Key input process parameters included spindle speed, dwell time, plunge depth, and tool pin profile. The results demonstrate that XGBoost yielded the highest predictive accuracy, achieving an R2 score of 99.99% for both tensile shear and bending strength, while RF offered a strong balance between accuracy and robustness. Other ML models struggled with the datasets complexity, resulting in lower performance. Among DL approaches, the BPNN outperformed the RNN, achieving approximately 99.8% accuracy by effectively capturing complex data patterns. ASM International 2025. -
Influence of shot peening on microstructure and electrochemical behavior of al-Cu alloy; [Influence du grenaillage sur la microstructure et le comportement.]
The current study explores the effects of shot peening on the microstructural evolution and corrosion behavior of precipitation-hardened Al-Cu alloy. Microstructural analysis revealed significant grain size reduction from 100 m to 6.5 m, and this process improves grain boundary density, reduces precipitate size from 2 m to 0.5 m, and ensures a uniform elemental distribution, effectively mitigating galvanic corrosion risks. Energy-dispersive spectroscopy (EDS) confirms the homogenisation of alloying elements. At the same time, X-ray diffraction (XRD) analysis highlights crystallographic modifications, including refined crystallite size, redistributed secondary phases, and compressive residual stresses, contributing to enhanced strength and fatigue resistance. Corrosion behavior studies reveal a dramatic reduction in weight loss from 0.09 mg to 0.03 mg and a corrosion rate decrease from 3.5 10?3 mpy to 1.5 10?3 mpy, along with a noble shift in corrosion potential from ?1150 mV to ?775 mV. As observed in optical microstructures, grain refinement, residual stress introduction, and uniform surface characteristics lead to a significant shift from severe to mild corrosion attacks. Impedance analysis demonstrated improved corrosion resistance in the shot-peened alloy, evidenced by higher charge transfer resistance (Rct) and lower double-layer capacitance (Cdl). 2025 Canadian Institute of Mining, Metallurgy and Petroleum. -
Microstructural evolution and damping response in ARB-processed ZK60 alloy
This study investigates the effect of microstructural evolution on the damping behaviour of ZK60 magnesium alloy processed via Accumulative Roll Bonding (ARB). ARB was employed at 300C for up to four cycles, significantly refining the grain structure and altering dislocation and precipitation behaviour. Comprehensive microstructural analysis revealed the formation of fine equiaxed grains (?6.7 m), dissolution of coarse precipitates, and increased dislocation density. TEM and Selected Area Diffraction (SAD) patterns confirmed dynamic recrystallisation and uniform grain orientation, while XRD patterns exhibited peak broadening and intensity changes, indicating crystallite refinement and texture evolution. Damping results showed substantial improvements in the ARB-processed alloy, particularly at low-to-mid frequencies, with up to 21% higher damping capacity than the base alloy. This enhancement is attributed to increased grain boundary sliding, dislocation interactions, and refined precipitatematrix interfaces. Both alloys exhibited similar damping responses at higher frequencies, suggesting saturation of energy dissipation mechanisms. 2025 Canadian Institute of Mining, Metallurgy and Petroleum. -
Friction and wear behaviour of copper reinforced acrylonitrile butadiene styrene based polymer composite developed by fused deposition modelling process
This paper focuses on the development of copper filled Acrylonitrile Butadiene Styrene (ABS) composites by fused deposition modelling (FDM) and to characterize its friction and wear behaviour. Twin screw extrusion technique was employed to extract copper-ABS composite filament. Three different materials were tested, i.e. pure ABS, ABS+2.5wt% Cu and ABS+5wt% Cu. Friction and wear characteristics of pure ABS and copper filled ABS composites were tested under various loads and sliding velocities. Addition of Copper powder has significantly improved the friction and wear properties of the developed composites. Further, it is also observed that friction and wear behaviour increased with increase in copper content in ABS. Worn out surfaces were subjected to scanning electron microscopy studies to analyse and identify the possible wear mechanisms involved. Faculty of Mechanical Engineering, Belgrade. -
Experimental investigation of tribocorrosion
This chapter discusses various techniques available for evaluation of tribocorrosion behavior of industrial components, their applications, and limitations. Numerous influential factors of tribocorrosion, their mechanisms, and their characteristics have been discussed at length. Further, a case study of tribocorrosion behavior of aluminum-based in situ metal matrix composites have been deliberated comprehensively. 2021 Elsevier Inc. All rights reserved. -
Parametric effect on machining characteristics of laser machined Al7075TiB2 in-situ composite
The effect of laser parameters on the machining characteristics of an Al7075 based in-situ metal matrix composite reinforced with Titanium diboride(TiB2) is investigated. The cutting speed (at 10001200 m/hr), stand-off distance (SOD) (0.30.5 mm), and gas pressure (0.50.7 bar) were studied. Scanning electron microscopy (SEM) was used to validate the machining behaviour of in-situ composites. Surface roughness and dimensional error decrease as the SOD increases up to 0.4 mm, but both increases as the SOD increases to 0.5 mm. whereas the volumetric material removal rate (VMRR) increases up to 0.4 mm SOD and then decreases as SOD increases (0.5 mm). Surface roughness, VMRR, and dimensional error were all found to increase with laser speed. Surface roughness and dimensional error increase as gas pressure increase up to 0.5 bar, then decreases. The VMRR, on the other hand, increased continuously as the assist gas pressure increased. Copyright 2021 Inderscience Enterprises Ltd. -
QuintessenceChameleon transitions in anisotropic Kiselev model of neutron stars
We investigate a chameleon scalar field dynamically interacting with a Kiselev-type metric, where the static anisotropic fluid part of the metric is replaced by a density-dependent scalar field nonminimally coupled to curvature. This construction enables a transition from screened behavior in high-density regions where the scalar acquires an effective mass m???1?2 to unscreened quintessence dynamics at large scales, characterized by a critical screening radius rcrit?m??1. By solving the modified TolmanOppenheimerVolkoff equations under spherical symmetry, we show that radial scalar gradients (?r?) induce pressure anisotropies (?p?r?1) in neutron star envelopes, while deviations from general relativity (GR) are suppressed deep in the core (r -
Conformal Invariance and Phase Transitions: Implications for Stable Black Hole Horizons?
Abstract: The behavior of black hole horizons under extreme conditionssuch as near collapse or phase transitionsremains less understood, particularly in the context of soft hair and Aretakis instabilities. We show that the breakdown of conformal symmetry during the balding phase induces a topological reorganization of the horizon, leading to divergent entropy corrections and emergent pressure terms. These corrections exhibit universal scaling laws, analogous to quantum phase transitions in condensed matter systems, with extremal limits functioning as quantum critical points. Interestingly, by employing quasi-equilibrium boundary conditions, one could stabilize horizon dynamics without explicitly introducing ad hoc higher-order corrections, further limiting the universal applicability of conformal invariance in black hole physics. Pleiades Publishing, Ltd. 2025. -
Interacting Dark Energy and Its Implications for Unified Dark Sector
Alternative dark energy models were proposed to address the limitation of the standard concordance model. Though different phenomenological considerations of such models are widely studied, scenarios where they interact with each other remain unexplored. In this context, we study interacting dark energy scenarios (IDEs), incorporating alternative dark energy models. The three models that are considered in this study are time-varying ?, Generalized Chaplygin Gas (GCG), and K-essence. Each model includes an interaction rate ? to quantify energy density transfer between dark energy and matter. Among them, GCG coupled with an interaction term shows promising agreement with the observed TT power spectrum, particularly for ?<70, when ? falls within a specific range. The K-essence model (??0.1) is more sensitive to ? due to its non-canonical kinetic term, while GCG (??1.02) and the time-varying ? (??0.01) models are less sensitive, as they involve different parameterizations. We then derive a general condition when the non-canonical scalar field ? (with a kinetic term Xn) interacts with GCG. This has not been investigated in general form before. We find that current observational constraints on IDEs suggest a unified scalar field with a balanced regime, where it mimics quintessence behavior at n<1 and phantom behavior at n>1. We outline a strong need to consider alternative explanations and fewer parameter dependencies while addressing potential interactions in the dark sector. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.