Explore the vast range of titles available.

In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

Objective: To determine the frequency of hematuria and lower urinary tract problems in rheumatoid arthritis cohort. Method: This cross sectional prospective study was conducted at department of Rheumatology Indus Medical College Tando Mohammad Khan from August 1, 2022 to March 3, 2023. Total 229 patients were selected after written and informed consent; demographic details were taken. Bristol Female Lower Urinary Tract Questionaries’ (BFLUTS) was filled and all male participants were asked for ultrasound scan of prostate and freshly voided midstream urine was collected for microscopic hematuria. Results: In this study the prevalence of Lower Urinary Tract Symptoms (LUTS) was (77.3%) and microscopic hematuria (34.5%). Major symptoms of LUTS were: nocturia (69%), bladder pain (35.4%), leaking before going to toilet (40.6%), frequency of incontinence (24.5%), nocturnal incontinence (27.9%), sex life spoiled due to urinary symptoms (22.7%), avoid situation where no toilet (30.1%) and overall interference of life (32.8%) cases. A significant association of DAS-28 with LUTS and microscopic hematuria was seen (p<0.01). Conclusion: Lower urinary tract problems and microscopic hematuria are common in both genders, and severity of RA, increases LUTS and affects quality of life. doi: https://doi.org/10.12669/pjms.41.3.8316 How to cite this: Wagan AA, Paras. Lower urinary tract symptoms and hematuria in Rheumatoid arthritis (LUTH-RA) study. Pak J Med Sci. 2025;41(3):876-879. doi: https://doi.org/10.12669/pjms.41.3.8316 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The most prominent and rapidly increasing source of electrical power generation, wind energy conversion systems (WECS), can significantly improve the situation with regard to remote communities’ power supply. The main constituting elements of a WECS are a wind turbine, a mechanical transmission system, a doubly-fed induction generator (DFIG), a rotor side converter (RSC), a common DC-link capacitor, and a grid-side converter. Vector control is center for RSC and GSC control techniques. Because of direct and quadrature components, the active and reactive power can also be controller precisely. This study tracks the maximum power point (MPP) using a maximum power point tracking (MPPT) controller strategy. The MPPT technique provides a voltage reference to control the maximum power conversion at the turbine end. The performance and efficiency of the suggested control strategy are validated by WECS simulation under fluctuating wind speed. The MATLAB/Simulink environment using simpower system toolbox is used to simulate the proposed control strategy. The results reveal the effectiveness of the proposed control strategy under fluctuating wind speed and provides good dynamic performance. The total harmonic distortions are also within the IEEE 519 standard’s permissible limits which is also an advantage of the proposed control approach.

IntroductionIn order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis.MethodsOur proposed methodology trains four deep learning algorithms on an input dataset consisting of 920 patient respiratory audio files. These audio files were recorded using digital stethoscopes and comprise the Respiratory Sound Database. The four deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), and CNN ensembled with bidirectional LSTM (CNN-BLSTM).ResultsThe aforementioned models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The best performing algorithm, LSTM, has an overall accuracy of 98.82% and F1-score of 0.97.DiscussionThe LSTM algorithm's extremely high predictive accuracy can be attributed to its penchant for capturing sequential patterns in time series based audio data. In summary, this algorithm is able to ingest patient audio recordings and make precise lung disease predictions in real-time.

Objective: To perform ultrasound examination in un-resolving frozen shoulder disorder, in Pakistani cohort visiting rheumatology clinic. Methods: This cross sectional study was carried out at Department of Rheumatology, Indus Medical College Tando Mohhamad Khan, from 16th March 2022 to 30th October 2022. Patients diagnosed as unilateral frozen shoulder on clinical grounds and received intra-articular injection (s) in last six months, never been investigated, still persisting with pain and restricted range of shoulder motion were enrolled. After the demographic details and shoulder examination, ultrasound examination of both shoulder joints was performed by senior musculoskeletal radiologist, to know the exact diagnosis. Results: In 138 cases on ultrasound examination following injuries were noted: rotator cuff tendinopathy (RCT) (61%), adhesive capsulitis (21%), mixed lesion (rotator cuff tendinopathy and adhesive capsulitis) (14%). In age group < 50 years rotator cuff tendinopathy was the major lesion, while in cases >50 years age group: adhesive capsulitis (AC) was predominant lesion (p-0.05).Rotator cuff tendinopathy had significant association with supraspinatus tears (p<0.5). Conclusion: In Un-resolving frozen shoulder pain, ultrasound examination of involved joint helps in reaching the exact cause which may differ from the existing diagnosis and guides to further management. doi: https://doi.org/10.12669/pjms.40.1.7440 How to cite this: Wagan AA, Surahyo P. Un-resolving frozen shoulder: Are we really treating it? Pak J Med Sci. 2024;40(1):---.  doi: https://doi.org/10.12669/pjms.40.1.7440 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective: To determine the frequency of modifiable cardiovascular risk factors in the Pakistani cohort with Ankylosing Spondylitis (AS). Method: After IRB approval, a cross-sectional study was conducted among patients of AS, at the Department of Rheumatology Indus Medical College, Tando Mohammad Khan, from 15th March to 15th September, 2022. After obtaining demographic data, other parameters such as blood pressure (BP) and body mass index were recorded. In addition, a 5 ml blood sample was collected to assess their serum lipid profile, and fasting blood sugar levels. Using the laboratory data, the Framingham cardiovascular risk score was calculated for each patient and they were categorized into low, intermediate, or high-risk categories. Results: Total 131 cases of ankylosing spondylitis: frequency of modifiable risk factors were: obesity (75.6%), high TG level (62.6%), high risk FRS score (40.5%), high LDL level (38.1%), low HDL (34.4%), hypertension (30.5%), diabetes mellitus (26.7%), high cholesterol level (17.6%), smoking (16%). In univariate analysis AS cases shows that increasing disease duration was associated with more risk of modifiable risk factors (p<0.05), on multivariate analysis, a positive association of age, diastolic blood pressure, smoking, diabetes mellitus, DMARDS, herbal medication-but not statistically significant (p>0.05). Conclusion: In chronic AS there’s higher prevalence of modifiable cardiovascular risk factors, earlier recognition and effective management helps in prevention of future cardiovascular events. doi: https://doi.org/10.12669/pjms.40.3.7265 How to cite this: Wagan AA, Surahyo P. Pakistani Ankylosing Spondylitis Cohort with modifiable cardiovascular risk factors (PAS-CVD) study. Pak J Med Sci. 2024;40(3):---. doi: https://doi.org/10.12669/pjms.40.3.7265 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Federated learning (FL) enables data owners to collaboratively train a machine learning model without revealing their private data and sharing the global models. Reliable and continuous client participation is essential in FL for building a high-quality global model via the aggregation of local updates from clients over many rounds. Incentive mechanisms are needed to encourage client participation, but malicious clients might provide ineffectual updates to receive rewards. Therefore, a fair and reliable incentive mechanism is needed in FL to promote the continuous participation of clients while selecting clients with high-quality data that will benefit the whole system. In this paper, we propose an FL incentive scheme based on the reverse auction and trust reputation to select reliable clients and fairly reward clients that have a limited budget. Reverse auctions provide candidate clients to bid for the task while reputations reflect their trustworthiness and reliability. Our simulation results show that the proposed scheme can accurately select users with positive contributions to the system based on reputation and data quality. Therefore, compared to the existing schemes, the proposed scheme achieves higher economic benefit encouraging higher participation, satisfies reward fairness and accuracy to promote stable FL development.

Root-knot nematodes (Meloidogyne spp.) are sedentary endoparasites that cause severe economic losses to agricultural crops globally. Due to the regulations of the European Union on the application of nematicides, it is crucial now to discover eco-friendly control strategies for nematode management. Biocontrol is one such safe and reliable method for managing these polyphagous nematodes. Biocontrol agents not only control these parasitic nematodes but also improve plant growth and induce systemic resistance in plants against a variety of biotic stresses. A wide range of organisms such as bacteria, fungi, viruses, and protozoans live in their natural mode as nematode antagonists. Various review articles have discussed the role of biocontrol in nematode management in general, but a specific review on biocontrol of root-knot nematodes is not available in detail. This review, therefore, focuses on the biocontrol of root-knot nematodes by discussing their important known antagonists, modes of action, and interactions.

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.

Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices.