Hybrid Convolutional Neural Network and Generative Adversarial Network Framework for Robust Deepfake Detection: Advancing Accuracy, Scalability, and Multimodal Integration.

Deepfakes pose critical threats to digital media integrity and societal trust. This paper presents a hybrid deepfake detection framework combining Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to address challenges in scalability, generalizability, and adversarial robustness. The framework integrates adversarial training, a temporal decay analysis model, and multimodal detection across audio, video, and text domains.

Dynamic Lissajous patterns for real time identification and localization of power quality disturbance.

This study proposes a novel and computationally efficient method for real-time identification and localization of power quality (PQ) disturbances in microgrids using dynamic Lissajous patterns formed by voltage and current waveforms. Each power disturbance-such as sag, swell, harmonic distortion, and transients-induces a unique geometric deformation in the Lissajous figure, which serves as a visual signature of the event. Key geometric and statistical features, including area, skewness, kurtosis, and centroid deviation, are extracted from these dynamic patterns to construct robust indices for classification.

Prevalence of polycystic ovarian syndrome in South Asian countries and 21 global regions with Sociodemographic index: A systematic analysis of the Global Burden of Disease (1990-2021).

Background: This analysis examines both the prevalence and the disability-adjusted life years (DALYs) linked to polycystic ovary syndrome (PCOS) across South Asia and worldwide, drawing on data from the Global Burden of Disease (GBD) 1990-2021 study.

Methods: Data from GBD 2021 were analyzed using Joinpoint Regression to assess age-standardized prevalence rates (ASPR) and DALYs (ASDR). Sociodemographic index (SDI) was incorporated to evaluate the relationship between socioeconomic factors and PCOS burden.

Neurological Manifestations in Oropouche Virus Infection: A Systematic Review and Meta-Analysis.

Oropouche virus (OROV), an emerging arbovirus, poses a significant public health concern in tropical and subtropical regions of Latin America, as well as in other parts of the world, with imported cases reported in North America and Europe. While OROV is primarily associated with acute febrile illness, especially emerging evidence suggests it may cause neurological complications, though these remain understudied. This systematic review and meta-analysis aim to estimate the prevalence of neurological manifestations in OROV infections.

A hybrid M-DbneAlexnet for brain tumour detection using MRI images.

Introduction: Brain Tumour (BT) is characterised by the uncontrolled proliferation of the cells within the brain which can result in cancer. Detecting BT at the early stage significantly increases the patient's survival chances. The existing BT detection methods often struggle with high computational complexity, limited feature discrimination, and poor generalisation.

Prevalence of recurrent nerve injury among esophageal cancer patients undergoing esophagectomy: A systematic review and meta-analysis.

Esophageal cancer remains a major cause of cancer-related mortality worldwide, and esophagectomy is a primary curative treatment for localized disease. However, recurrent laryngeal nerve (RLN) injury is a common and impactful complication that can impair vocal cord function, increase aspiration risk, and hinder postoperative recovery. To quantify its prevalence and explore contributing factors, we conducted a systematic review and meta-analysis including 24 studies and 6015 patients.

Effect of SGLT-2 inhibitors on COPD exacerbations in individuals with type 2 diabetes: A meta-analysis and Bayesian sensitivity analysis.

Background: Chronic obstructive pulmonary disease (COPD) and Type 2 diabetes mellitus (T2DM) frequently coexist, amplifying morbidity, mortality, and healthcare costs. COPD exacerbations are more frequent and severe in T2DM patients, necessitating therapies addressing both conditions. This systematic review and meta-analysis evaluates the impact of sodium-glucose cotransporter-2 inhibitors (SGLT-2i) on COPD exacerbations in T2DM patients.

Exposure to violence and risk of hypertensive disorders in pregnancy: Systematic review and meta-analysis.

Background: Hypertensive disorders during pregnancy (HDP) are a significant global health concern, affecting 5-10 % of pregnancies and contributing to maternal and newborn morbidity and mortality. While various factors, including genetics and physiological changes, influence the development of HDP, emerging evidence suggests a strong association with psychosocial stressors, particularly exposure to violence. This meta-analysis aimed to assess the association between exposure to violence and risk of hypertensive disorders in pregnancy.

Proctitis and Other Gastrointestinal Manifestations in Mpox Disease: A Systematic Review and Meta-Analysis.

Background: Mpox, caused by the monkeypox virus (MPXV), is primarily recognized for its dermatologic and systemic symptoms. However, emerging evidence suggests a significant prevalence of gastrointestinal (GI) manifestations, particularly proctitis, diarrhea, nausea, vomiting, and abdominal pain. Despite the growing clinical recognition of these symptoms, their epidemiology and impact remain poorly understood.

Bio inspired feature selection and graph learning for sepsis risk stratification.

Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machine learning models for sepsis prediction often suffer from poor interpretability, limited generalizability across diverse patient populations, and challenges in handling class imbalance and high-dimensional clinical data. To address these gaps, this study proposes a novel framework that integrates bio-inspired feature selection and graph-based deep learning for enhanced sepsis risk prediction.

Enhancing sparse data recommendations with self-inspected adaptive SMOTE and hybrid neural networks.

Personalized recommendation systems are vital for enhancing user satisfaction and reducing information overload, especially in data-sparse environments like e-commerce platforms. This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split-Convolution (SC) neural network (LSTM-SC) and an advanced sampling technique-Self-Inspected Adaptive SMOTE (SASMOTE). Unlike traditional SMOTE, SASMOTE adaptively selects "visible" nearest neighbors and incorporates a self-inspection strategy to filter out uncertain synthetic samples, ensuring high-quality data generation.

Bionic Artificial Leaves Based on AIE-Active Supramolecular Hydrogel for Efficient Photocatalysis.

A novel hydrogel-based biomimetic artificial leaf is fabricated by integrating host-guest interactions with covalent bonding. Specifically, a water-soluble tetraphenylethylene-embedded pillar[5]arene (m-TPEWP5), which exhibits aggregation-induced emission (AIE) property, is synthesized as the host molecule. An amphiphilic guest G is introduced to form a stable complex (HGSM) via non-covalent interactions.

Association of estimated glomerular filtration rate with proliferative diabetic retinopathy: a systematic review and meta-analysis.

Background: Proliferative diabetic retinopathy (PDR) is a serious vision-threatening complication of diabetes. Chronic kidney disease (CKD), measured by estimated glomerular filtration rate (eGFR), shares similar pathophysiological mechanisms with diabetic retinopathy, including inflammation, oxidative stress, and vascular dysfunction. However, the strength of the association between eGFR and PDR remains unclear.

Impact of SGLT-2i on COPD exacerbations in patients with type 2 diabetes mellitus: A systematic review and meta-analysis.

Background: Chronic Obstructive Pulmonary Disease (COPD) and Type 2 Diabetes Mellitus (T2DM) often coexist, leading to compounded morbidity, mortality, and healthcare burden. COPD exacerbations significantly impact patients with T2DM, with increased frequency and severity. Sodium-glucose cotransporter-2 inhibitors (SGLT-2i) have demonstrated promising benefits in managing both glycemic control and respiratory health.

Hybrid optimization enabled DenseNet for autism spectrum disorders using MRI image.

Autism spectrum disorder (ASD) is the neuro-developmental disorder caused by various changes in the brain. It affects the life conditions with social interaction and communication. Most of the previous researches used the various techniques for the early detection to reduce the ASD, but it had been occurred several complications such as, time expenses, and low accessibility for diagnosis.

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

Background: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions.

Enhanced deep learning models for automatic fish species identification in underwater imagery.

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images.

Implementation of distributed arithmetic-based symmetrical 2-D block finite impulse response filter architectures.

This paper presents an efficient two-dimensional (2-D) finite impulse response (FIR) filter using block processing for two different symmetries. Architectures for a general filter (without symmetry) and two symmetrical filters (diagonal and quadrantal symmetry) are implemented. The proposed architectures need fewer multipliers because of the symmetry of the filter coefficients.

Granite classification using machine learning and edge computing.

Background: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and countershave a major influence on the interior d ´ecor which is essential to set the moodand ambience of a house.

Experimental study and machine learning model to predict formability of magnesium alloy sheet.

Magnesium alloy is not only light in weight but also possesses moderate strength. Magnesium AZ31-H24 alloy sheet has many applications in the automotive and aerospace industries. Experimental stretch forming tests are performed on this sheet to measure the material's formability by constructing forming limit diagrams.

ExpHBA Deep-IoT: Exponential Honey Badger Optimized Deep Learning For Breast Cancer Detection in IoT Healthcare System.

Breast cancer (BC) is the most widely found disease among women in the world. The early detection of BC can frequently lessen the mortality rate as well as progress the probability of providing proper treatment. Hence, this paper focuses on devising the Exponential Honey Badger Optimization-based Deep Covolutional Neural Network (EHBO-based DCNN) for early identification of BC in the Internet of Things (IoT).

Optimization and Microstructural Studies on the Machining of Inconel 600 in WEDM Using Untreated and Cryogenically Treated Zinc Electrodes.

Any industry that manufactures dies, punches, molds, and machine components from difficult-to-cut materials, such as Inconel, titanium, and other super alloys, largely relies on wire electrical discharge machining (WEDM). In the current study, the effect of the WEDM process parameters on Inconel 600 alloy with untreated zinc and cryogenically treated zinc electrodes was investigated. The controllable parameters included the current (IP), pulse-on time (Ton), and pulse-off time (Toff), whereas the wire diameter, workpiece diameter, dielectric fluid flow rate, wire feed rate, and cable tension were held constant throughout the experiments.

Biophysical insights on the interaction of anticoagulant drug dicoumarol with calf thymus-DNA: deciphering the binding mode and binding force with thermodynamics.

The biological activity of drugs is exhibited due to their interactions with bio-receptors. Dicoumarol (DIC) is a natural hydroxycoumarin and a well-known anticoagulant. DNA is the genetic material and one of the targets of numerous drugs.

Sentiment Analysis based Emotion Extraction for COVID-19 Using Crawled Tweets and Global Statistics for Mental Health.

The unpredictable and crucial challenges that occurred because of the COVID-19 pandemic disease have taken a gradual upsurge impacting over 213 countries across the globe. Different countries have taken several measures to get control over it like Lockdown, Curfews, Travel ban, etc. but still the cases were increasing and the situation was getting worse globally during some period of time.