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Introduction India has unique tobacco-free film and TV rules designed to prevent tobacco uptake. In this study, we examined the potential influence of exposure to smoking imagery in regionally famous films, on susceptibility to smoke in teenagers enrolled in schools in a district in Southern India. Methods A longitudinal survey of students, in grades 6 to 8 at baseline in 2017 and grades 7 to 9 one year later in 2018, ascertained prospective incident susceptibility to smoking during the study period in relation to baseline exposure to 27 locally popular films with actual or implied smoking imagery. Results We analyzed linked data from 33676 participants, and 3973 (11.8%) of the adolescents reported incident susceptibility. There was a significant increase in susceptibility to smoking with increasing exposure at baseline to smoking imagery in films on univariable analysis, highest tertile of exposure relative to no exposure (OR=1.4; 95% CI: 1.0 –2.1, ptrend

Abstract The regulated release of chemical messengers is crucial for cell-to-cell communication; abnormalities in which impact coordinated human body function. During vesicular secretion, multiple SNARE complexes assemble at the release site, leading to fusion pore opening. How membrane fusion regulators act on heterogeneous SNARE populations to assemble fusion pores in a timely and synchronized manner, is unknown. Here, we demonstrate the role of SNARE chaperones Munc13-1 and Munc18-1 in rescuing individual nascent fusion pores from their diacylglycerol lipid-mediated inhibitory states. At the onset of membrane fusion, Munc13-1 clusters multiple SNARE complexes at the release site and synchronizes release events, while Munc18-1 stoichiometrically interacts with trans-SNARE complexes to enhance N- to C-terminal zippering. When both Munc proteins are present simultaneously, they differentially access dynamic trans-SNARE complexes to regulate pore properties. Overall, Munc proteins’ direct action on fusion pore assembly indicates their role in controlling quantal size during vesicular secretion.

Objective: Despite advances in artificial intelligence (AI) in glaucoma prediction, most works lack multicenter focus and do not consider fairness concerning sex, race, or ethnicity. This study aims to examine the impact of these sensitive attributes on developing fair AI models that predict glaucoma progression to necessitating incisional glaucoma surgery. Design: Database study. Participants: Thirty-nine thousand ninety patients with glaucoma, as identified by International Classification of Disease codes from 7 academic eye centers participating in the Sight OUtcomes Research Collaborative. Methods: We developed XGBoost models using 3 approaches: (1) excluding sensitive attributes as input features, (2) including them explicitly as input features, and (3) training separate models for each group. Model input features included demographic details, diagnosis codes, medications, and clinical information (intraocular pressure, visual acuity, etc.), from electronic health records. The models were trained on patients from 5 sites (N = 27 999) and evaluated on a held-out internal test set (N = 3499) and 2 external test sets consisting of N = 1550 and N = 2542 patients. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUROC) and equalized odds on the test set and external sites. Results: Six thousand six hundred eighty-two (17.1%) of 39 090 patients underwent glaucoma surgery with a mean age of 70.1 (standard deviation 14.6) years, 54.5% female, 62.3% White, 22.1% Black, and 4.7% Latinx/Hispanic. We found that not including the sensitive attributes led to better classification performance (AUROC: 0.77–0.82) but worsened fairness when evaluated on the internal test set. However, on external test sites, the opposite was true: including sensitive attributes resulted in better classification performance (AUROC: external #1 - [0.73–0.81], external #2 - [0.67–0.70]), but varying degrees of fairness for sex and race as measured by equalized odds. Conclusions: Artificial intelligence models predicting whether patients with glaucoma progress to surgery demonstrated bias with respect to sex, race, and ethnicity. The effect of sensitive attribute inclusion and exclusion on fairness and performance varied based on internal versus external test sets. Prior to deployment, AI models should be evaluated for fairness on the target population. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

BackgroundThe short-term risk of suicide after medical hospital discharge is four times higher among men compared with women. As previous work has identified female-specific antecedents of suicide-related behavior after medical hospitalization of women with serious mental illness, we examined predictors among a similar population of men with multimorbidity.MethodsClassification and regression tree (CART) models were developed and validated using electronic health records (EHRs) from 1,423,161 medical (non-psychiatric) hospitalizations of men ≥ 18-years-old with an existing diagnosis of a depressive disorder, bipolar disorder, or chronic psychosis. Hospitalizations occurred between 2009 and 2017. Risk groups were evaluated using an independent testing set. The primary outcome was readmission within one year associated with ICD-9 or -10 code for self-harm or attempt.ResultsThe 1-year readmission rate for intentional self-harm and suicide attempt was 3.9% (55,337/1,423,161 hospitalizations). The classification model discriminated risk with area under the curve (AUC) 0.73 (Confidence Interval [95%CI] 0.68-0.74), accuracy 0.82 (95%CI 0.71-0.83), sensitivity 82.6% (95%CI 81.2-84), and specificity 83.1% (95%CI 81.7-84.5). Strongest predictors were medical comorbidity, prior self-harm, age, and prior hospitalization. Men with greater medical comorbidity burden and prior self-harm were at highest risk (Odds Ratio [OR] 3.10, 95%CI 3.02-3.18), as were men < 62-years-old with few medical comorbidities (OR 1.11 95%CI 1.08-1.13).LimitationsThe study focused on medical hospitalizations for suicide attempt and thus captured only severe attempts resulting in hospitalization.ConclusionsAfter medical hospitalization, men with serious mental illness experienced a high risk of self-harm (1:25 hospitalizations). Risk was particularly elevated among younger patients without prior medical conditions and older patients with medical comorbidity and prior self-harm.

In cell models, changes in the 'accessible' pool of plasma membrane (PM) cholesterol are linked with the regulation of endoplasmic reticulum sterol synthesis and metabolism by the Aster family of nonvesicular transporters; however, the relevance of such nonvesicular transport mechanisms for lipid homeostasis in vivo has not been defined. Here we reveal two physiological contexts that generate accessible PM cholesterol and engage the Aster pathway in the liver: fasting and reverse cholesterol transport. During fasting, adipose-tissue-derived fatty acids activate hepatocyte sphingomyelinase to liberate sequestered PM cholesterol. Aster-dependent cholesterol transport during fasting facilitates cholesteryl ester formation, cholesterol movement into bile and very low-density lipoprotein production. During reverse cholesterol transport, high-density lipoprotein delivers excess cholesterol to the hepatocyte PM through scavenger receptor class B member 1. Loss of hepatic Asters impairs cholesterol movement into feces, raises plasma cholesterol levels and causes cholesterol accumulation in peripheral tissues. These results reveal fundamental mechanisms by which Aster cholesterol flux contributes to hepatic and systemic lipid homeostasis.

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

Background: Though agricultural farming is considered a peaceful and healthy industry it has a high rate of mortality especially suicide. In India, 11.2% of suicides are reported in the farming sector. Aim: To identify the role of psychosocial factors leading to farmer’s suicide in a farmer’s dominant district of Kerala. Material and Method: One hundred and sixty-six successive suicides in Wayanad district and age, sex, marital status, and financial status matched normal controls residing in the same locality were evaluated and compared on psycho-social demographic variables, life events, and details of suicide. Results: Significantly a greater number of victims were living separately, living in rented homes, had no own land for cultivation, migrated, had marital discord, and strained relationships with relatives. They had loans from private financial enterprises and single money persons, had past attempts, a family history of alcoholism, and highly stressful life events. The most frequent psychiatric diagnosis was alcohol dependence/abuse followed by depression. The majority committed suicide by hanging followed by insecticide poisoning. Intention to commit suicide was expressed by 38.5% and 30.2% were intoxicated at the time of attempt. The venue for the attempt was the own house in 75.7% of cases. Conclusion: This study suggests the following remedial measures to prevent farmers’ suicides. Expert advice to manage financial constraints like availing loans, crop insurance, etc. Guidance from the agricultural department for cost-effective cultivation. Early identification and treatment of psychiatric disorders.

Abstract Objective(s) A subset of laryngeal squamous cell carcinoma (LSCC) patients undergoing larynx preserving treatment ultimately require total laryngectomy (TL) for oncologic or functional reasons. This study aims to identify TL risk factors in these patients. Methods Retrospective cohort study using Veterans Affairs (VA) database. T1–T4 LSCC cases treated with primary radiotherapy (XRT) or chemoradiotherapy (CRT) were assessed for TL and recurrence. Binary logistic and Cox regression and Kaplan–Meier analyses were implemented. Results Of 5390 cases, 863 (16.0%) underwent TL. On multivariable analysis, age (adjusted odds ratio: 0.97 [0.96–0.98]; p T1 disease (T2, 1.76 [1.44–2.17]; p

Fires occurring within buildings present a grave concern, as they entail substantial risks to human lives, property, and the environment. Implementing appropriate fire safety measures becomes imperative to mitigate the occurrence of fires and ensure efficient response in the event of an incident. The primary aim of the study is to examine the performance of various coatings on the residual strengths of the reinforced normal strength self-compacting geopolymer concrete (NSGC) and high strength self-compacting geopolymer concrete (HSGC) columns subjected to standard temperature exposure in accordance with ISO 834 guidelines. The design compressive strength of the concretes was 36.12, 57.06 and 52.8 MPa. Two types of coating were employed in the present investigation namely ceramic wool wrapping and high alumina cement. A computerized electric furnace is used to heat the column specimens. Amongst the forty-two columns developed, six specimens tested without temperature exposure and protective coating as a reference specimen. Twelve specimens were heated as per ISO fire curve and tested to assess the residual axial strength performance and twenty-four specimens were coated with protective layers, then heated and tested to assess the post fire performance. It is exemplified that the composite protective coating offers effective resistance to concrete, gaining optimum axial strength performance. The findings show that these fire protecting composites have a high potential for utilizing as new strengthening techniques for reinforced concrete (RC) columns.

Much of the student-staff partnership literature calls for increased collaboration and power sharing among staff and students. Less common are accounts by student partners themselves that take up the challenge of what partnership and power feel like as universities embrace their neoliberal trajectory - and - purport to do so on behalf of students themselves. Especially acute is the conundrum of how partnership initiatives can, and do, reproduce the very power dynamics they set out to transform. We are a group of students and staff working in curriculum partnership together at Western Sydney University. The context of our work together is the 21C project, a university-wide strategy to transform curriculum, teaching, and learning, drawing on 'partnership pedagogy'. In this paper, we engage in a process of reflexive inquiry to interrogate a new elective unit that many of us are involved in as advocates, co-creators, as students and staff learning together, and as evaluators, called "We are the university: Students co-creating change" (WATU). To highlight partnership's intricate power plays, we offer a fictionalised account to reflect our multi-voiced experiences of being involved in WATU. We have come to understand power's simultaneity in partnership as forms of power "over," as "permission-giving," as "sharing (or partnership)," and as the power "to act (agency)." The account is our story of partnership's inevitable contradictions - a collaboration that teaches us about the challenges of working together while being cautious of partnership's transformatory claims. [The page range (258-274) cited in the .pdf is incorrect. The correct page range is p258-272.]

L-glutaminase is a unique enzyme with catalytic activity and the ability to modulate glutamine levels, making it a valuable enzyme with numerous potential applications. L-glutaminase triggers a distinctive reaction by converting L-glutamine into glutamic acid while releasing ammonia concurrently. This enzymatic process holds potential applications across diverse industries, notably in food and pharmaceuticals. The primary objective of the present research was to identify and isolate a fungal strain proficient in L-glutaminase production from soil found in maritime environments. The physical and nutritional conditions were optimized for maximum synthesis of L-glutaminase under solid state fermentation (SSF) and submerged fermentation conditions (SmF). The isolated organism was identified as Fusarium solani-melongenae strain CRI 24 by morphological and 18S rRNA analysis. The optimum carbon source under SmF and SSF was found to be starch (0.2% w/v). Wheat bran as solid substrate among others showed optimum enzyme activity. On the seventh day of incubation, at pH 8.0 and 0.7% L-glutamine concentration under SSF and SmF, the highest enzyme activity was detected. The greatest enzyme activity in SSF was seen at a moisture content of 10%. Fusarium solani-melongenae species produced the enzyme under optimal conditions and 4.20 and 4.73-fold increase (from 0.8 U/mL to 3.61 U/mL and from 0.781 U/mL to 3.69 U/mL) was achieved after optimization in submerged and in solid state fermentation, respectively. The selective isolation and optimization processes described in this work are a promising technique for the industrial production of L-glutaminase and can be applied in the pharmaceutical and food industries.

Dysfluency refers to discontinuity in speech due to noise or speech disorder, this dysfluency has unique features in terms of pitch and time based on these characteristics the dysfluent speech is categorized into repetition, prolongation, or blocking of words or phrases, and because of this uneven structure in speech, it named unstructured speech. The recognition and classification of such unstructured speech require an effective algorithm to boost the process of Automatic Speech Recognition in real-time and it is a challenging task because the system will take more time to extract features like pitch, frequency or time, and energy. From the past few decades, many researchers have proposed algorithms to address the mentioned, but still, there is a gap in recognizing the endpoint of each frame based on the mentioned which helps to robust the recognition accuracy of dysfluent speech. So, in this proposed work to extract the desired and unique features of dysfluent speech the samples are decomposed into different sub-signals by using two advanced methods of Empirical mode decomposition (EMD), they are Variational Mode Decomposition (VMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD). These two techniques overcome the one major disadvantage of EMD that is the EMD techniques use different decomposition levels to produce the oscillation of the same time series signal. Hence combination of VMD and CEEMD decomposes the input speech sample into 5 sub-signals to classify the different classes of dysfluency using a New Gate Control Unit (NGCU) based on Recurrent Neural Networks (RNN).

The smart city traffic management domain is perpetually a crucial sector that requires innovative strategies due to expanding urbanization and vehicle use. In this study, we have introduced a traffic prediction and handling system that utilizes Temporal Convolutional Networks (TCNs) combined with Federated Learning (FL) to deal with urban traffic effectively. This approach leverages the sophisticated functionalities of TCNs to evaluate and estimate traffic trends, such as congested phases, traffic flow, and ideal mobility routes. The system guarantees data privacy and utilizes decentralized information analysis using Federated Learning. In this approach, every point in the intelligent city network, including traffic sensors and cameras, serves to collectively comprehend traffic patterns without disclosing raw data. Using this cooperative method not only improves the model’s ability to forecast outcomes accurately but also enables efficient real-time traffic management, with the ability to adapt to changing conditions. The method has shown significant efficacy in enhancing traffic flow and mitigating congestion. The critical criteria are a 20% drop in average commuting times, a 25% drop in traffic congestion, and a 15% enhancement in emergency response times. These statistics highlight the system’s efficiency in improving urban traffic control by integrating modern technologies.

Background: Urinary incontinence (UI) is an unintentional or involuntary loss of urine. Stress and urge incontinence are the most common types. Many women feel embarrassed to seek medical care, and it affects their quality of life to a greater extent and is usually a neglected problem among rural communities. Objective: The objective of the study was to estimate the prevalence and to determine the factors associated with UI among peri- and postmenopausal women. Methodology: A community-based cross-sectional study was done among 200 peri- and postmenopausal women of age ≥40 years. The sample size was calculated using a prevalence of 25% from previous studies with a 95% confidence interval and absolute precision of 6%. A simple random sampling technique was employed. A semi-structured questionnaire consisting of the Questionnaire for Urinary Incontinence Diagnosis scale was used for data collection. Data analysis was performed in SPSS version 20.0. P ≤ 0.05 was considered statistically significant. Results: The prevalence of UI was 24% (48 out of 200 women), of which stress incontinence was seen in 15% and urge incontinence in 17% of women. Mixed incontinence was seen in 8% of women. Factors such as increasing age, attainment of menopause, chronic constipation, diabetes, and recurrent UTIs were significantly associated with UI. Conclusion: Two out of every ten peri- and postmenopausal women were found to have UI. Identification of women at risk, educating them regarding preventive measures, following a healthy diet, and encouraging them to seek medical care can greatly reduce their chance of developing UI and aid in improving their quality of life.

The cardiovascular system is responsible for carrying the blood along with nutrition and oxygen throughout the body. This system consists of heart, blood, and blood vessels. The experts, or doctors called as cardiologists, analyze the sounds of heart’s (lub-dub) beat and flow of blood to diagnose Cardio Vascular Disease (CVD) using a traditional stethoscope and phonological cardiogram technique. Through the stethoscope, the cardiologist will listen to vibration produced by heart beat and heart beat sound and murmur sound are popularly known as phonocardiogram (PCG) signals, which are being recorded for medical diagnosis purposes using a stethoscope. The development of a technique for the automatic recognition of HVD’s assists the experts in recognizing the CVD effectively in the initial stage itself from PCG signals. There are many tools available to help doctors in a clinical setting for the accurate diagnose the CVD in a less time. The main aim of this proposed work is to provide an Artificial Intelligence (AI) based PCG signal analysis for the automatic and early detection of various cardiac conditions using supervised and unsupervised Recurrent Neural Network (RNN) based Bidirectional Long Short-Term Memory (Bi-LSTM) Machine Learning (ML) algorithm. Along with this algorithm, Generative Adversarial Networks (GAN’s) is considered because they can create fresh, high-quality, pseudo-real data that resembles their training set which has been demonstrated by using their two unique networks: Discriminator Network (DN) and the Generator Network (GN). The proposed method is tested using heart sound signals from the well-known, freely accessible PhysioNet and Kaggle datasets. The Experimental results are validated based accuracy, precision, F1-score, sensitivity, and specificity.

Objective: The purpose of this systematic review and meta-analysis was to determine whether there is a connection between polycystic ovarian syndrome (PCOS)-affected women's levels of the anti-Mullerian hormone (AMH) and certain cardiometabolic indicators. Materials and Methods: To find pertinent recent research published between 2017 and 2023, a thorough search was done in PubMed. Studies were included if they looked into the relationship between PCOS-related women's AMH levels and cardiometabolic markers. To determine pooled effect estimates, data from the included studies were examined using random-effects models. Results: Five papers were included in the meta-analysis since they satisfied the inclusion requirements. The meta-analysis found substantial positive relationships between AMH levels and markers of insulin resistance, fasting blood sugar levels, and dyslipidemia measures such as total cholesterol (SMD: 0.68, 95% confidence interval: 0.34–1.00, P < 0.001). Conclusion: This systematic review and meta-analysis show that AMH levels in PCOS-affected women significantly positively correlate with markers of insulin resistance, fasting glucose levels, and dyslipidemia parameters. These findings imply that the pathogenesis of the cardiometabolic abnormalities seen in PCOS may include AMH. AMH may be used as a biomarker to estimate the cardiometabolic risk in PCOS-affected women, but more studies are required to determine its clinical applicability.

Objective: This study sought to determine the relationship between right ventricular (RV) function and clinical variables and prognosis in individuals with acute myocardial infarction (AMI) utilizing strain imaging. Materials and Methods: A prospective observational research involving 150 patients who had been admitted with AMI was carried out. Utilizing two-dimensional speckle-tracking strain imaging, RV function was assessed. Age, sex, risk factors, and comorbidities were recorded as clinical parameters. A 12-month follow-up was conducted to assess major adverse cardiovascular events (MACE). Results: 65% of the study's participants were men, with a mean age of 58.2 years. When compared to a healthy control group, individuals with AMI had significantly lower RV longitudinal strain (RVLS) (P 0.001). RVLS and left ventricular ejection fraction had a statistically significant connection (r = 0.642, P 0.001). Patients with compromised RVLS had a greater rate of MACE over the follow-up period compared to those with maintained RV function (P = 0.014). Conclusion: In conclusion, strain imaging offers useful information for evaluating RV function in patients with AMI. Reduced left ventricular performance and a higher likelihood of unfavorable clinical outcomes are linked to impaired RVLS. Utilizing strain imaging to detect RV dysfunction early can help direct treatment plans and enhance patient outcomes.

Integrating Constant Power Loads (CPL) in a DC microgrid generates a virtual negative impedance that may lead to system instability. CPL deteriorates the system damping, which is more severe for the DC power system with large line impedances. This paper proposes a novel and simple virtual negative resistance-based control loop for the DC-DC converters connected at both source and load ends to enhance the CPL stability margin in a DC microgrid. The proposed control scheme includes a simple pole-zero criterion to evaluate the virtual negative resistance. Further, small-signal and extended large-signal models of the bidirectional DC-DC converters are developed to calculate stabilizer parameters and assess system stability with CPL. These models effectively calculate optimal stabilizer parameter values using simple root locus analysis. A comparative stability analysis is conducted to illustrate the system’s resilience in the face of changes in parameter values and considerable enhancement of the stability margin. The eigenvalue analysis and time domain simulations are performed with the proposed control strategy at the source and load side converters without affecting the load side voltage profile to demonstrate its effectiveness.

Online trading has become an important component of the portfolio of investors as it provides an attractive and profitable investment option in spite of the risk involved. Many individuals embraced diversified financial portfolios and started investing in Stocks, Cryptocurrencies, Bonds, Metals, and Commodities through online portals. It needs constant attention to the price of the financial commodities to obtain profits. However, the majority of the investors are landing in losses because of the lack of continual monitoring of the prices to ascertain the changes in the prices of financial commodities all the time. In this work, to assist users in order to avoid this tiresome task of continual monitoring, a prototype of a low-cost portable device for change indication in the price of multiple financial commodities of various trading paradigms based on NodeMCU is proposed. The device includes a Data retrieval and processing (DRP) unit in the form of nodeMCU, control unit, and notification unit. The DRP unit initially obtains the list of the financial commodities and control parameters including sampling rate, threshold, and time delay from the Google Firebase real-time database which are collected from user through a mobile application. Further, it fetches the real-time price data of the financial commodities to compute the percentage change in the given time duration and obtain the prioritized list i.e., the list of financial commodities as per the descending order of absolute percentage change and instructs the notification unit to generate alarm through an audio buzzer in case at least if one of the financial commodity exhibits an absolute percentage change greater than a predefined threshold. The notification unit also contains Red and Green LEDs corresponding to each of the financial commodities to indicate negative and positive changes that exceed the threshold. Further, an LCD displays the details of the first financial quantity in the prioritized list. The control unit comprises two designated switches to change the financial quantity whose details are to be displayed on the LCD and a silent mode switch to disable the audio notifications for the sake of convenience of the user. A prototype of the proposed device is realized with 16 cryptocurrencies, a sampling rate of one sample per minute, and a time delay of one hour for the purpose of testing the device. A maximum of 0.20 % of the mean error rate is observed in the price of Dogecoin, and 0% of the mean error rate is observed in the percentage change of price corresponding to all cryptocurrencies. Further, the number of notifications using RED and GREEN LEDs are computed for all cryptocurrencies which are found to be exactly in match with the original data with various threshold values of 1%,2%,5%, and 10%. The total number of notifications including both Red and Green LEDs is found to be higher in HBAR. The proposed device assists investors in making timely decisions for profitable trading and provides valuable insights about the momentum of various markets to readjust the investments and balance the portfolios accordingly.

The use of machine learning algorithms for the assessment of speech fluency is increasingly becoming recognized globally due to their ability to quickly identify speech impairments. This approach is preferred over manual diagnosis, as it reduces the likelihood of human error and minimizes the delay in commencing the therapy. A pipelined deep learner-dual classifier (PDL-DC) is proposed for the automated detection of speech impairment. The assessment of individuals’ speech fluency consisted of two distinct phases: the classification of speech disfluencies and the categorization of fluency disorders. Speech disfluencies, including revisions, prolongations, whole-word repetitions, word-medial repetitions, and filled pauses, were categorized into distinct groupings. The second aspect of classification pertains to the assessment of fluency levels, wherein speakers are classified into three categories: healthy individuals, individuals with stuttering, and individuals with Specific Language Impairment (SLI). The proposed model’s implementation of a pipelined design enables the dual validation of a subject’s fluency. The proposed model demonstrates an average classification accuracy, precision, and recall of 97%.