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A tumor develops when brain cells exhibit abnormal growth patterns within various body locations, characterized by irregular boundaries and shapes. Typically, these tumors exhibit rapid proliferation, increasing at a rate of approximately 1.6% per day. This abnormal cell growth can lead to invisible illnesses and alterations in psychological and behavioral functions, contributing to a rising trend in adult mortality rates worldwide. Therefore, Brain tumors must be detected early. Failure to do so may cause a deadly, incurable condition. Effective brain tumor therapy improves survival if detected early. Magnetic Resonance Imaging (MRI) is essential for finding and classifying brain tumors. The manual nature of brain tumor diagnosis and classification makes it prone to errors, necessitating the development of automated processes for improved accuracy. In light of these considerations, we have devised with a fully automated way to use MR images to find and classify brain tumors. Our approach encompasses three key phases: pre-processing, segmentation, and classification. To detect tumors in the brain, we utilized MRI, employing the deep transfer with the transformed VGG19 model. Notably, our research demonstrates superior growth rates when using other pre-trained Convolutional Neural Network (CNN) models such as AlexNet and VGG-16. The deep transfer learning with the transformed VGG19 model yielded accuracy achieving 98.65% (dataset 1) and 99.18% (dataset 2) for different datasets.

In this work, we report a systematic study on charge transport and thermo-electric properties of poly (3,4-ethylenedioxythiophene):poly(styrene sulfonate):poly(ethylene glycol) (PEDOT–PSS:PEG) organic thin films doped with silver nanoparticles (AgNPs). Transparent and flexible hybrid nanocomposite films were prepared by a simple strategy via bar coating technique. The effect of PEG treatment and AgNPs nanoparticles distribution in PEDOT–PSS films was examined through various characterization techniques such as scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform infra-red spectroscopy (FTIR), and thermo gravimetric analysis (TGA). The content of AgNPs in PEDOT–PSS:PEG was varied and optimized for 10 wt% as a percolation threshold. The addition of AgNPs and subsequent PEG treatment enhances the conductivity of PEDOT–PSS films from 2 to 420.33 S/cm due to the removal of non-complexed PSS and synergetic interaction between PEDOT–PSS and AgNPs segments via PEG. These highly conductive nanocomposite films were employed in an organic thermo-electric (TE) device to investigate the TE properties. These PEG treated PEDOT-PSS: AgNPs nanocomposite organic films exhibit a enhanced power factor from 6 μW/mK2 to 85 μW/mK2 which is nearly 15 times higher than that of pure PEDOT-PSS thin films. Due to ease of processing, flexibility, excellent charge transport, and thermo-electric properties, these PEG-treated PEDOT–PSS:AgNPs nanocomposite films can be potential thermo-electric materials for organic electronic devices operated at room temperature.

Skin cancer is the most dominant and critical method of cancer, which arises all over the world. Its damaging effects can range from disfigurement to major medical expenditures and even death if not analyzed and preserved timely. Conventional models of skin cancer recognition require a complete physical examination by a specialist, which is time-wasting in a few cases. Computer-aided medicinal analytical methods have gained massive popularity due to their efficiency and effectiveness. This model can assist dermatologists in the initial recognition of skin cancer, which is significant for early diagnosis. An automatic classification model utilizing deep learning (DL) can help doctors perceive the kind of skin lesion and improve the patient’s health. The classification of skin cancer is one of the hot topics in the research field, along with the development of DL structure. This manuscript designs and develops a Detection of Skin Cancer Using an Ensemble Deep Learning Model and Gray Wolf Optimization (DSC-EDLMGWO) method. The proposed DSC-EDLMGWO model relies on the recognition and classification of skin cancer in biomedical imaging. The presented DSC-EDLMGWO model initially involves the image preprocessing stage at two levels: contract enhancement using the CLAHE method and noise removal using the wiener filter (WF) model. Furthermore, the proposed DSC-EDLMGWO model utilizes the SE-DenseNet method, which is the fusion of the squeeze-and-excitation (SE) module and DenseNet to extract features. For the classification process, the ensemble of DL models, namely the long short-term memory (LSTM) technique, extreme learning machine (ELM) model, and stacked sparse denoising autoencoder (SSDA) method, is employed. Finally, the gray wolf optimization (GWO) method optimally adjusts the ensemble DL models’ hyperparameter values, resulting in more excellent classification performance. The effectiveness of the DSC-EDLMGWO approach is evaluated using a benchmark image database, with outcomes measured across various performance metrics. The experimental validation of the DSC-EDLMGWO approach portrayed a superior accuracy value of 98.38% and 98.17% under HAM10000 and ISIC datasets across other techniques.

The study of research proposes a systematic pattern for optimization and fabrication of a sustainable-cost effective electrochemical sensor made by Bi-CdFe 2 O 4 (BCDF) nanoparticle and graphite powder. The structural examinations of synthesized BCDF materials were analyzed by specific spectral techniques viz.; P-XRD, SEM-EDX, TEM, XPS, FT-IR and DRS techniques. The modified sensor electrode offer a significant electrochemical properties that can improve the material selectivity and sensitivity actions measured by Cyclic Voltammetric (CV) and Electrochemical Impedance Spectral (EIS) plots. We demonstrated a developed highly-purity BCDF-graphite paste electrode for sensing actions on Paracetamol and Lead (Pb 2+ ) ions under 0.1 M KCl. The excellent sensing activity towards Lead ions and Paracetamol confirmed by its redox potential peaks at scan rate of 1–5 V/s with maximum sensitivity of -0.61 V and 0.69 V respectively. The excellent photo-dye-degradation action of BCDF (98.2%) than those of host CDF (81.6%) on Rose Bengal (RB) dye was demonstrated. Its kinetic study reveals that this process follows first order kinetics and rate constants of the host (18.1 × 10 −3 min −1 ) and BCDF (39.2 × 10 −3 min −1 ) were measured. Thus, the synthesized BCDF electrode provides a new perception for developing specific nano-sensor towards detection of toxic metals.

Computer vision heavily relies on features, especially in image classification tasks using feature-based architectures. Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. Bidirectional LSTM (BiLSTM) networks store data in both forward and backward directions, while traditional Long Short-Term Memory (LSTM) networks handle data in a specific order. This study proposes a computer vision system that integrates BiLSTM with CNN features for image categorization tasks. The system effectively reduces feature dimensionality using learned features, addressing the high dimensionality problem in leaf image data and enabling early, accurate disease identification. Utilizing CNNs for feature extraction and BiLSTM networks for temporal dependency capture, the method incorporates label information as constraints, leading to more discriminative features for disease classification. Tested on datasets of pepper and maize leaf images, the method achieved a 99.37% classification accuracy, outperforming existing dimensionality reduction techniques. This cost-effective approach can be integrated into precision agriculture systems, facilitating automated disease detection and monitoring, thereby enhancing crop yields and promoting sustainable farming practices. The proposed Efficient Labelled Feature Dimensionality Reduction utilizing CNN-BiLSTM (ELFDR-LDC-CNN-BiLSTM) model is compared to current models to show its effectiveness in reducing extracted features for leaf detection and classification tasks.

A green combustion route was developed using Aloe vera extract for the synthesis of highly crystalline Calcium Titanate (CaTiO3) (CTO) nanoparticles (~ 30 nm). The optimized CaTiO3 NPs exhibits a direct optical band gap of 3.12 eV and abundant surface oxygen vacancies, enhancing charge separation and light absorption. As a photocatalyst, the material achieved a remarkable degradation efficiency of 90.44% for Malachite green (MG) dye under UV irradiation and 57.88% under sunlight within 120 min, following pseudo-first-order kinetics. Scavenger studies confirmed that photogenerated holes (h+) and hydroxyl radicals (·OH) play dominant roles in the degradation process. Furthermore, the Aloe vera–derived CaTiO3 demonstrated superior electrochemical activity toward Glyphosate detection, showing rapid amperometric response, low charge-transfer resistance, and excellent linearity (R2 > 0.96) across 1–6 µM concentration with LOD 0.38 µM and sensitivity ~ 0.25 µA µM–1. These results demonstrate the potential of Aloe vera–assisted CaTiO3 as a sustainable multifunctional nanomaterial that integrates environmental remediation and agrochemical sensing, offering a scalable and eco-friendly platform for next-generation photocatalysts and electrochemical sensors.

This study presents comprehensive theoretical, spectroscopic, and biological investigations of the compound 5-Chloro-6-fluoro-2-(2-pyrazinyl)-1H-benzimidazole (5CF2PB). Density Functional Theory (DFT) calculations were performed at the B3LYP/6–311 +  + G(d,p) level, and a Potential Energy Scan (PES) was carried out to identify the most stable conformer and its optimized geometry. Theoretical vibrational frequencies and Potential Energy Distribution (PED) analysis were correlated with experimental FT-IR and FT-Raman spectra, showing excellent agreement. Experimental UV–Vis and 1 H– 13 C NMR spectra were recorded and compared with theoretical predictions using the IEF-PCM solvation model in DMSO, chloroform, and water. Frontier Molecular Orbital (FMO) analysis revealed a HOMO–LUMO energy gap of 4.043 eV, consistent with moderate chemical reactivity and optical absorption. The compound’s chemical reactivity descriptors, Molecular Electrostatic Potential (MEP), and topological parameters were analyzed through QTAIM, ELF, LOL, IRI, and RDG methods, providing insights into electronic structure and non-covalent interaction regions. Hirshfeld surface and 2D fingerprint analyses confirmed the dominance of halogen–hydrogen and halogen–nitrogen interactions in crystal packing. In-vitro antimicrobial screening demonstrated that 5CF2PB exhibits potent antibacterial and antifungal activities, particularly against Pseudomonas aeruginosa and Aspergillusniger , showing better efficacy than the standard drug ciprofloxacin. The PASS prediction suggested significant anti-mycobacterial potential, which was validated by molecular docking studies against the protein targets 6TE7 and 5O4L, yielding strong binding affinities and inhibition constants. Molecular dynamics simulations further confirmed the stability of the ligand–protein complexes. Overall, the integrated computational, spectroscopic, and biological analyses establish 5CF2PB as a promising multifunctional compound with potential pharmacological applications.

Bio-derived NiO-Carbon nanocomposites were prepared using Neem ( Azadirachta indica ) leaf extract through a green combustion approach. Structural analysis confirmed the formation of face-centered cubic NiO with crystallite sizes in the 25–50 nm range. The optimized 1:1 NiO: C composite demonstrated a limit of detection calculated via the 3σ/slope method in the millimolar range, along with a linear sensing response between 1 and 6 mM for glyphosate. In addition, the material achieved 98.78% degradation of Congo Red (CR dye) under UV irradiation (365 nm, 15 mW/cm²) while maintaining strong reusability and stability. The improved performance is attributed to the presence of conductive carbon, which facilitates charge transport, reduces electron–hole recombination, and increases the availability of active surface sites. Electrochemical impedance analysis further confirmed reduced charge-transfer resistance in the optimized composite, supporting its enhanced interfacial kinetics. The scalable and cost-effective synthesis, combined with stability in acidic media, highlights the potential of this composite as a multifunctional platform for pesticide monitoring and wastewater remediation.

Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable electrocardiogram (ECG) monitors have been used for decades to monitor patients with arrhythmias. These monitors provide real-time data on cardiac activity to identify irregular heartbeats. However, rhythm monitoring and wave detection, especially in the 12-lead ECG, make it difficult to interpret the ECG analysis by correlating it with the condition of the patient. Moreover, even experienced practitioners find ECG analysis challenging. All of this is due to the noise in ECG readings and the frequencies at which the noise occurs. The primary objective of this research is to remove noise and extract features from ECG signals using the proposed infinite impulse response (IIR) filter to improve ECG quality, which can be better understood by non-experts. For this purpose, this study used ECG signal data from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database. This allows the acquired data to be easily evaluated using machine learning (ML) and deep learning (DL) models and classified as rhythms. To achieve accurate results, we applied hyperparameter (HP)-tuning for ML classifiers and fine-tuning (FT) for DL models. This study also examined the categorization of arrhythmias using different filters and the changes in accuracy. As a result, when all models were evaluated, DenseNet-121 without FT achieved 99% accuracy, while FT showed better results with 99.97% accuracy.

The sensitivity of developed electrode has gained significant attention for potential energy storage and electrochemical sensor activities. The modified nano-CdO/Ag-CdO-carbon paste electrodes were developed for electrochemical detection of mercury (Hg + ) heavy metal. The synthesized samples were well characterized through PXRD (Powder X-ray diffraction), SEM-EDAX (Scanning Electron Microscopy-Energy Dispersive X-ray Analysis), XPS (X-ray photo-electron spectroscopy), FT-IR (Fourier transform Infra-Red), and UV-Visible spectroscopy. The Ag-CdO modified electrode endowed with higher sensing current and Csp (188 F/g) than pure CdO NPs (94.6 F/g) measured by Linear Sweep (LS), Cyclic Voltammetric (CV) and Electrochemical Impedance Spectral (EIS) techniques. The excellent electrochemical sensing action of developed Ag-CdO electrode was examined on heavy metal Hg + ions at 1–5 mM scan rate in 0.1 M KCl. The linear relationship of sensing measurements with smaller concentration (1–5 mM) was observed with its increased current (+ 1.64 × 10 –4 A/cm 2 at 1 mM) at 30 mV/s. LOD of CdO and Ag-CdO electrodes (Hg +   Oxid ) were found at 1.91 mM & 2.41 mM (Hg +   Red ) respectively with maximum sensitivity at -0.006 V. LOQ of CdO and Ag-CdO electrodes (Hg +   Oxid ) were 5.78 mM & 6.98 mM respectively with maximum sensitivity at -0.006 V. The antibacterial measurements of prepared samples were examined for their susceptibility to inhibit the growth of gram-negative ( Escherichia coli) and gram-positive ( Staphylococcus aureus) bacteria. Thus, the synthesized Ag-CdO electrode provides a new insight for determining the concentrations of critical pollutants and processing the various nanoparticles for sensing of cyanogenic heavy metals.