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An exhaustive examination of automatic speech recognition
Speech is nature gift to the human being to differentiate with other creatures on the earth. Speech research stands out as one of the most daunting fields amidst numerous challenging research domains. This current analysis of existing literature aims to provide insights for future endeavors within the global speech research community. The study delves into various challenges associated with speech corpora, front-end algorithms aimed at efficient speech representation, and back- end engines tasked with the recognition process. Thirteen speech corpora undergo scrutiny concerning factors such as language diversity, duration, developmental progress, and accessibility. Furthermore, this research review illuminates potent methodologies that foster the extraction of rich features and bolster robust speech recognition capabilities. This gives an idea on how various methods are available to recognize speech in an effective way. 2026 Author(s). -
An Examination of the Challenges Associated with Applying Artificial Intelligence Techniques to Specific Management Problems
Artificial intelligence (AI) holds immense promise in revolutionizing management practices across various sectors, offering solutions to complex problems and optimizing decision-making processes. However, the application of AI techniques to management problems is not without its challenges. This examination delves into the multifaceted hurdles encountered when integrating AI into management frameworks, highlighting key obstacles and potential avenues for overcoming them.AI algorithms heavily rely on large volumes of high-quality data for effective training and decision-making. Yet, many management domains grapple with disparate data sources, inconsistencies, and incomplete datasets, hindering the performance and reliability of AI systems. Furthermore, the dynamic nature of management problems poses a significant challenge to AI implementation. Management environments are characterized by evolving trends, uncertainties, and unforeseen disruptions, rendering static AI models inadequate in adapting to changing conditions. Hence, the development of agile AI systems capable of continuous learning and adaptation becomes essential for addressing the dynamic nature of management challenges. 2024, Collegium Basilea. All rights reserved. -
An Examination of Methodological Approaches for Segmentating Fetal Brain MRI Images - Analysis
In today's world and in the country like India, Women's health needs more care. Especially the women's health during the pregnancy period plays a vital role in both the mother as well as the baby's care. As per a survey, among thousands three of them found to have fetal brain abnormalities. If these abnormalities are predicted at the early stage, then it will be an added advantage in saving both the life of mother and baby. During the pregnancy number of tests have to be performed to monitor fetal development. Tests like fetal ultrasound, Chorionic Villus Sampling, Amniocentesis, Fetal Echocardiogram, Fetal MRI imaging SCAN etc. The fetal brain abnormality can be predicted as well as treated at the early stage by analyzing the fetal brain MRI during the gestational period. Identifying abnormalities in fetal brain MRI images involves several essential steps, including image segmentation, analyzing images involves extracting distinctive features, refining their quality, identifying relevant patterns, and categorizing them based on specific criteria. The process of classification determines whether an abnormality is present or not. Analyzing images presents a complex undertaking owing to the diversity in shapes, spatial arrangements, and intensity levels within the images. This paper focuses on reviewing and comparing various segmentation techniques, highlighting their respective strengths and weaknesses. 2024 IEEE. -
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An evaluation of workplace HIV and AIDS programme development in zimbabwe stock exchange listed companies based in harare zimbabwe
The study explores the state of workplace HIV and AIDS interventions in Zimbabwe with specific reference to programming interventions in Zimbabwe Stock Exchange newlinelisted Companies in the Harare Metropolitan Province. The scientific knowledge domain-location of the study involves Strategic Human Resource management, Human Resource Management, Business (Corporate) planning and Labour administration (integrated occupation health and safety, employee welfare and newlinewellness) and Policy development and implementation. The key consideration is that newlinethe company has an enlightened self-interest to facilitate workplace HIV and AIDS because it leads to employee welfare, wellness and longevity which translate to increased productivity in the context of in Zimbabwe Stock Exchange-listed Companies. In this background, Zimbabwe Workplace HIV and AIDS programmes are generally considered to be fragile, fragmented, under-developed, weak and unsustainable in both design and management by a wide range of stakeholders despite close to two decades of implementation since promulgation and establishment. The study tries to identify key determinants of workplace HIV and AIDS program development in Harare, assesses the current state of workplace HIV and AIDS newlineprogramme development in Harare, tries to establish the significance of senior newlinemanagement commitment on workplace HIV programme development, ascertains the newlinerelationship between having a Comprehensive Staff Welfare Program (CSWP) and workplace HIV and AIDS program development and evaluates the extent to which companies comply with workplace HIV program development standards. The study was conducted using mixed methods and data was collected using a survey questionnaire and focus group discussions. Based on the study it was found that the general state of the Workplace HIV and AIDS Programme Development is in a state newlineof serious underdevelopment. -
An evaluation of SH and anti-plane SH wave signals for nanosensor applications using two distinct models of piezoelectric materials lead zirconate titanate (PZT-2) and PZT-5H
Investigating how wave propagation affects the functionality of surface acoustics wave (SAW) macro- and nanosensors is the main objective of the current investigation. Consequently, the surface piezoelectricity theory is used to investigate shear horizontal waves (SH) in an orthotropic PQC layer that is layered on top of an elastic framework (Model I), a piezoelectric substrate, and an orthotropic PQC substrate (Model II). Approach: A variable-separable approach is used in the study. Based on the differential equations and matrix formulation, theoretical forms are created and utilized to display the wavenumber of surface waves in any direction of the piezoelectric medium. Two configurations are examined: an orthotropic piezoelectric material layer over an elastic framework and a piezoelectric material half-space with a nanosubstrate. Analytical expressions for frequency equations are derived for both symmetric and anti-symmetric waves. Study investigates the effects of surface elastic constants, surface density, anisotropic piezoelectric constant, and symmetric and anti-ssymmetric modes on phase velocity. The study is confined to only linear wave propagation. Additionally, the analysis is based on idealized material properties and surface properties of the material. Surface effect study is the novelty which is conducted in the piezoelectric model and their applications in sensors. The findings of this research may be useful in designing surface acoustic wave sensors (SAW) devices. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025. -
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An ettective dynamic scheduler tor reconfigurable high speed computing system
High Speed Computing is a promising technology that meets ever increasing real-time computational demands through leveraging of flexibility and parallelism. This paper introduces a reconfigurable fabric named Reconfigurable High Speed Computing System (RHSCS) and offers high degree of flexibility and parallelism. RHSCS contains Field Programmable Gate Array (FPGA) as a Processing Element (PE). Thus, RHSCS made to share the FPGA resources among the tasks within single application. In this paper an efficient dynamic scheduler is proposed to get full advantage of hardware utilization and also to speed up the application execution. The addressed scheduler distributes the tasks of an application to the resources of RHSCS platform based on the cost function called Minimum Laxity First (MLF). Finally, comparative study has been made for designed scheduling technique with the existing techniques. The proposed platform RHSCS and scheduler with Minimum Laxity First (MLF) as cost function, enhances the speed of an application up to 80.30%. 2014 IEEE. -
An ethnopharmacological investigation of antidiabetic plants used in Gudibande Taluk, Chikkaballapur District, Karnataka, India
This research offers an ethnopharmacological investigation of the application of medicinal plants for treating diabetes. An ethnobotanical survey was conducted in Gudibande Taluk, Chikkaballapur District, Karnataka, India. Traditional healers were interviewed about 28 plant species belonging to 22 families being used in treating diabetes. Fabaceae was recorded as the most prevalent family with maximum number of plant species. Leaves of 41.9% plant species were noticed as the most frequently plant parts used followed by fruits (12.9%), seeds (12.9%), and root (6.5%) for the treatment of diabetes. The study also comprized molecular docking and molecular dynamics simulations to assess the pharmacological potential of bioactive compounds, focused on interactions with human pancreatic alpha-amylase. Two ligands, metformin and compound 197678, were examined with GROMACS for 200 ns. The findings showed that all protein-ligand complexes maintained structural stability, with RMSD, RMSF, Rg, SASA, and hydrogen bonding metrics indicating the stability and possible effectiveness of these compounds. Conservation issues were also recognized, such as habitat loss and ignorance of younger generations about exposure of traditional knowledge. The results of the study underscored the healing potential of neglected medicinal flora and promote community-driven conservation of plants important for diabetes treatment. 2025, Indian journals. All rights reserved. -
An ethnographic expose of Mithun-human interrelationship among the Kuki community of Northeast India
Unrestrained consumption and a lack of a proper breeding ecosystem have depleted the variety and species count of mithun (Bos frontalis). Indigenous Kuki tribes have a unique relationship with mithun, reared in the semi-domestic countryside. For the Kuki community, a mithun is used during community festivals, as a bride price in marriages, to settle disputes, in land-deed covenants, and at death ceremonies. Mithun-human interrelationship lessens poverty, empowers community survival, guarantees the completion of critical cultural obligations, and maintains marital bonds in the Kuki community. The head of a mithun signifies solemnity and celebration in many cultural underpinnings. A white cock, a dog, a goat, a pig, and a mithun were sacrificial elements to appease the unseen spirits for good health and prosperity. While some Indigenous practices have faded with the arrival of Christianity, the cultural involvement of mithun persists to this date. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
An ESIPT/AIE active Schiff Base for the selective detection of Picric acid, Ammonia, and its potential applications in anticounterfeiting and latent fingerprinting
A novel ESIPT/AIE-active Schiff base fluorophore, N?1,N?6-bis((Z)-2,4-dihydroxybenzylidene)adipohydrazide (ADHB), has been designed and synthesized. ADHB exhibits remarkable selectivity and sensitivity towards picric acid in aqueous phase, as well as ammonia in both aqueous and solid phases, with LOD values of 55.5 nM and 88.7 nM respectively, facilitating its efficacy in real sample analysis. While exhibiting notable luminescence in polar solvents (? = 0.15 %), ADHB displays pronounced fluorescence enhancement in the solid state (??? = 320 nm) due to aggregation-induced emission (AIE). The molecular skeleton of ADHB incorporates two potential excited-state intramolecular proton transfer (ESIPT) active sites that exhibit distinctive, reversible halochromic properties in the solid state. The adaptability of this Schiff base as a multi-responsive fluorescent material was explored by the fabrication of a blue-emitting polyvinyl alcohol (PVA) composite film and paper-based test strips. The detection limits agree with the amount of contaminants that the U.S. Environmental Protection Agency (EPA) allows in drinking water. The sensing mechanism was elucidated through comprehensive DFT studies, NMR titration studies and Job's plot analysis. The tunable photophysical properties of this AIE-active probe facilitates practical applications in anti-counterfeiting and latent fingerprint visualization, highlighting its significance in forensic science and security authentication. These findings establish ADHB as a fluorescent platform for the sensitive detection and continuous monitoring of hazardous compounds in environmental systems. 2025 Elsevier B.V. -
An equal split triple-band wilkinson power divider employing extended cross shaped microstrip line /
Microwave and Optical Technology Letters, Vol.60, Issue 10, pp.2488-2492. -
An ensemble deep learning model for automatic classification of cotton leaves diseases
Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
An Ensemble Approach Using ResNet and DenseNet for Cataract Detection
Cataracts represent a widespread ocular condition that profoundly affects an individuals vision and overall quality of life. Timely detection proves crucial for effective treatment, yet existing methodologies often entail invasive and discomforting procedures. Hence, an innovative approach is proposed for cataract detection utilizing an ensemble framework, which presents numerous significant advantages. It uses an ensemble framework amalgamating ResNet and DenseNet pre-trained learning models for cataract detection. This strategy enhances the precision and dependability of diagnosing cataracts. On the other hand, it diminishes false positives and negatives, consequently ensuring more accurate and timely diagnoses. Beyond mere accuracy, our ensemble framework brings about additional benefits. It bolsters the resilience of cataract detection by mitigating the influence of individual model biases and variances. Furthermore, it enhances the systems adaptability, making it applicable to various patient demographics and ocular conditions. Such adaptability is significant in the global healthcare landscape, facilitating effective deployment across diverse regions and populations. Moreover, our approach alleviates the discomfort and invasiveness associated with conventional cataract detection methods, promoting early diagnosis and reducing patient apprehension. Streamlining the diagnostic process also eases the burden on healthcare providers and improves overall patient care. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An enhancing reversible data hiding for secured data using shuffle block key encryption and histogram bit shifting in cloud environment
Nowadays there are numerous intruders trying to get the privacy information from cloud resources and consequently need a high security to secure our data. Moreover, research concerns have various security standards to secure the data using data hiding. In order to maintain the privacy and security in the cloud and big data processing, the recent crypto policy domain combines key policy encryption with reversible data hiding (RDH) techniques. However in this approach, the data is directly embedded resulting in errors during data extraction and image recovery due to reserve leakage of data. Hence, a novel shuffle block key encryption with RDH technique is proposed to hide the data competently. RDH is applied to encrypted images by which the data and the protection image can be appropriately recovered with histogram bit shifting algorithm. The hidden data can be embedded with shuffle key in the form of text with the image. The proposed method generates the room space to hide data with random shuffle after encrypting image using the definite encryption key. The data hider reversibly hides the data, whether text or image using data hiding key with histogram shifted values. If the requestor has both the embedding and encryption keys, can excerpt the secret data and effortlessly extract the original image using the spread source decoding. The proposed technique overcomes the data loss errors competently with two seed keys and also the projected shuffle state RDH procedure used in histogram shifting enhances security hidden policy. The results show that the proposed method outperforms the existing approaches by effectively recovering the hidden data and cover image without any errors, also scales well for large amount of data. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
An enhancement of machine learning model performance in disease prediction with synthetic data generation
The challenges of handling imbalanced datasets in machine learning significantly affect the model performance and predictive accuracy. Classifiers tend to favor the majority class, leading to biased training and poor generalization of minority classes. Initially, the model incorrectly treats the target variable as an independent feature during data generation, resulting in suboptimal outcomes. To address this limitation, the model was adjusted to more effectively manage target variable generation and mitigate the issue. This study employed advanced techniques for synthetic data generation, such as synthetic minority oversampling (SMOTE) and Adaptive Synthetic Sampling (ADASYN), to enhance the representation of minority classes by generating synthetic samples. In addition, data augmentation strategies using Deep Conditional Tabular Generative Adversarial Networks (Deep-CTGANs) integrated with ResNet have been utilized to improve model robustness and overall generalizability. For classification, TabNet, a model tailored specifically for tabular data, proved highly effective with its sequential attention mechanism that dynamically processes features, making it well suited for handling complex and imbalanced datasets. Model performance was evaluated using a novel approach of training synthetic data and testing on real data (TSTR). The framework was validated on the COVID-19, Kidney, and Dengue datasets, achieving impressive testing accuracies of 99.2%, 99.4%, and 99.5%, respectively. Furthermore, similarity scores of 84.25%, 87.35%, and 86.73% between the real and synthetic data for the COVID-19, Kidney, and Dengue datasets, respectively, confirmed the reliability of the synthetic data. TabNet consistently showed substantial improvements in F1-scores compared to other models, such as Random Forest, XGBoost, and KNN, emphasizing the importance of selecting the right synthetic data augmentation techniques and classifiers. Additionally, SHapley Additive exPlanations (SHAP)-based explainable AI tools were used to interpret model performance, providing insights into feature importance and its impact on predictions. These findings confirm that the proposed approach enhances the accuracy, robustness, and interpretability, offering a valuable solution for addressing data imbalance in classification tasks. The Author(s) 2025. -
An Enhanced Whale Optimization Algorithm for Task Scheduling in Cloud Computing
Task Scheduling is the significant challenge in the environment of Cloud Computing (CC) and has attention in numerous researchers in recent years with respect to attain cost effective computation and improve resource utilization. The existing algorithms has limitations of role and selection criteria of inertia weight was not considered. In this research, Enhanced Whale Optimization Algorithm (EWOA) is proposed for maximize effectiveness of task scheduling in CC. An inertia weight is implemented in WOA algorithm that enhances the convergence and accuracy of algorithm that helps in task scheduling effectiveness. The performance of proposed technique is estimated with performance measure of Makespan (ms), execution time (s) and resource utilization (%). The proposed method attained less execution time of 2304, 2537, 2765, 2983 and 3016s for 200, 400, 600, 800 and 1000 number of tasks. The proposed method attained the superior results when compared with other existing algorithms like Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer
Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect
Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R0 is 1.3. Vaccination of infants and kids will be considered as future work. 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).