Search

OBJECTIVE: Mobile health (mHealth) technologies have emerged as promising tools in the field of neuro-physiotherapy, offering innovative solutions for enhancing clinical decision-making processes. This scoping review explores the existing literature on the use of mHealth applications in neuro-physiotherapy with a specific focus on their impact on clinical decision-making. MATERIALS AND METHODS: The PubMed, Google Scholar, Cochrane Library, ScienceDirect, and Scopus databases were comprehensively searched for both qualitative and quantitative peer-reviewed articles written in the English language and published till 2023 that focus on mHealth applications in neuro-physiotherapy and clinical decision-making. RESULTS: The key findings from the 14 included studies highlighted the diverse array of mHealth applications employed in neuro-physiotherapy, ranging from wearable sensors and mobile apps to virtual reality platforms. Synthesis of the evidence from these studies demonstrated the potential of these technologies in clinical decision-making and improving patient outcomes, patients' and therapists' perspectives of these applications, their clinical clues, and the challenges with their use. CONCLUSIONS: The findings from the review underscore the need for continued exploration of these technologies to optimize their effectiveness in rehabilitation settings and ultimately improve clinical decision-making and patient care in neuro-physiotherapy. [ABSTRACT FROM AUTHOR]

Thrombocytopenia is a common problem in NICU. there are so many causes of thrombocytopenias, in this study we are evaluating clinical and etilogies of thrombocytopenias. The incidence of thrombocytopenia in this study was 43%.In our study, out of the 28 cases of early onset thrombocytopenia, the leading cause was culture proven sepsis seen in 43.9 % of cases followed by 31.8 % cases having birth asphyxia, 15.15% of cases were born to mothers having preeclampsia and 9 % of cases were IUGR neonates. Out of the 15 cases of late onset thrombocytopenia, 62.2% of cases were having c sepsis and 26.6 %of cases having meconium aspiration syndrome, 8% cases having birth asphyxia and 0% cases having Rh incompatibility. Maternal risk factors like pregnancy induced hypertension and maternal. [ABSTRACT FROM AUTHOR]

Abstract With the swift advancement of technology and growing popularity of internet in business and communication, cybersecurity posed a global threat. This research focuses new Deep Learning (DL) model referred as FinSafeNet to secure loose cash transactions over the digital banking channels. FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. One such aspect is, relying these databases in most of the cases imposes a great technical challenge towards effective real time transaction security. FinSafeNet draws attention to the attack and reproductive phases of Hierarchical Particle Swarm Optimization (HPSO) feature selection technique simulating it in a battle for extreme time performance called the Improved Snow-Lion optimization Algorithm (I-SLOA). Upon that, the model then applies the Multi-Kernel Principal Component Analysis (MKPCA) accompanied by Nyström Approximation for handling the MKPCA features. MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. This causes FinSafeNet to work easily and still be able to make accurate forecasts. Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. When tested on commonly used database for testing banks performance, for instance the Paysim database, FinSafeNet significantly improves upon the previous and fundamental approaches, achieving accuracy of 97.8%.

Abstract Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models’ performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal’s integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

Introduction The primary goal of any diabetes treatment is better control of blood sugar levels. Self-care practices were found to be positively correlated with good glycaemic control, reduction of complications, and improvement in quality of life. The objectives of this study is to estimate the prevalence of self-care practices and to explore the facilitating factors and barriers in the self-management of diabetes. Methodology: The study was a community-based cross-sectional concurrent mixed method study done among the diabetic individuals residing in the urban field practice area of a Government Medical College (GMC) during October and November 2023 after obtaining approval from the Institutional Ethics Committee.The responses of 126 participants were obtained to assess the prevalence of self-care activities using a semi-structured questionnaire. 15 In-depth interviews (IDI) were conducted to know the practices and barriers to self-management of diabetes using an IDI guide. Each interview/discussion was recorded electronically after obtaining verbal consent.Quantitative data was analysed using a statistical package for social sciences (SPSS) software for descriptive statistics. The audio recordings and the field notes were transcribed and translated into English. The data were analysed thematically and were grouped into a few broad themes and sub-themes. Results: The prevalence of self-care practices was high for medication adherence and least for the foot care component. The facilitating factors for increased medication adherence, self-blood glucose monitoring, and changing dietary patterns were increased awareness, fear of complications, and following the doctor’s advice. The barriers to decreased foot care component and physical activity were health concerns, lack of awareness, busy work schedules, and cultural factors. Conclusion: The prevalence of medication adherence and self-blood glucose monitoring was the highest when compared to foot care practices. Our study results emphasized the need for awareness regarding self-care practices among diabetics.

The vehicular ad hoc networks VANET is an essential part of intelligent transportation systems (ITSs) since it may offer various multimedia services and safety services to pedestrians, passengers, and even drivers. A wireless communication protocol called dedicated short-range communication (DSRC) was created for toll collection systems. Nevertheless, DSRC standards are extremely constrained, necessitating the development of next-generation communication protocols appropriate for VANET. Here, intended to develop an Optimized Reinforcement Learning (ORL) for obtaining resource allocation in VANET. This proposed methodology is developed for achieving resource allocation with efficient data transmission. This proposed approach is utilized to adjust the control channel interval (CCI) and service channel interval (SCI) to empower network performance. Additionally, it is utilized to reduce data collisions and optimize the network’s backoff distribution. The proposed method is a combination of reinforcement learning (RL) and adaptive coati optimization (ACO). The coati optimization mimics the characteristics of coati in natures in which it depends upon the coati escape from predators and hunting and attacking behaviour at various climates.The RL is utilized to obtain an efficient channel access algorithm. In the RL, the Q value is optimally selected by using ACO. Based on this algorithm, the proposed method is utilized to enhance the performance of VANET data transmission by achieving optimal resource allocation. The proposed method is implemented in MATLAB, and performances are evaluated using performance measures. Additionally, to validate the performance of the proposed methodology, it is compared with conventional techniques.

Nowadays, the demand for pepper keeps on increasing with the increase in human population. Accurate diagnosis, flawless identification, and early detection of the lesions will improve the income of farmers. At present, deep learning (DL) based techniques assist farmers in identifying plant diseases with low cost and minimal time complexity. Hence, this study proposes a novel optimized DL model for classifying the presence and absence of pepper leaf disease using an effective feature learning process. The proposed study undergoes four major stages namely Pre-processing, Segmentation, Feature extraction, and Classification. In the pre-processing stage, initially, the input images are resized and the Improved Contrast Limited Adaptive Histogram Equalization (ICLAHE) technique is introduced to enhance the quality of the pepper leaf images. Then, the Kernelized Gravity-based Density Clustering (KGDC) technique is conquered to segment the diseased portions from the leaf images. Finally, the Gated Self-Attentive Convoluted MobileNetV3 (GSAtt-CMNetV3) technique is proposed to extract the features and classify the pepper leaf disease accurately. Moreover, a novel osprey optimization algorithm (Os-OA) is introduced to tune the parameters of the proposed DL model for enhancing the classification performance. The proposed study is implemented via the Python platform, and a publicly available Plant-Village dataset is utilized for the simulation process. Accuracy, precision and recall values achieved by the proposed pepper leaf disease classification for training percent 80 is 97.87%, 96.87% and 97.08% respectively.

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.