Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images.

A recent global health crisis, COVID-19 is a significant global health crisis that has profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies using medical images is a challenging task. Thus, the requirement of an end-to-end automated system is vastly necessary in clinical treatments.

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification.

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis.

An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging.

Background: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has shown promise in improving accuracy. However, the lack of transparency in AI decision-making processes presents a challenge for clinical adoption.

Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques.

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework.

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies.

Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics.

Purpose: To detect the Marchiafava Bignami Disease (MBD) using a distinct deep learning technique.

Background: Advanced deep learning methods are becoming more crucial in contemporary medical diagnostics, particularly for detecting intricate and uncommon neurological illnesses such as MBD. This rare neurodegenerative disorder, sometimes associated with persistent alcoholism, is characterized by the loss of myelin or tissue death in the corpus callosum.

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer.

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer.

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed.

Acetylcholinesterase biosensors for electrochemical detection of neurotoxic pesticides and acetylcholine neurotransmitter: A literature review.

Neurotoxic pesticides are a group of chemicals that pose a severe threat to both human health and the environment. These molecules are also known to accumulate in the food chain and persist in the environment, which can lead to long-term exposure and adverse effects on non-target organisms. The detrimental effects of these pesticides on neurotransmitter levels and function can lead to a range of neurological and behavioral symptoms, which are closely associated with neurodegenerative diseases.

UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement.

In today's society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user's behavior data to suggest relevant items.

Efficient decolorization and detoxification of triarylmethane and azo dyes by porous-cross-linked enzyme aggregates of Pleurotus ostreatus laccase.

In this preset study, porous-cross-linked enzyme aggregates (CLEAs) of Pleurotus ostreatus laccase were utilized for the spontaneous decolorization and detoxification of triarylmethane and azo dyes, reactive blue 2 (RB) and malachite green (MG). The specific surface area and pore radius of the porous-CLEAs are 136.3 m/g and 19.

Blended Ensemble Learning Prediction Model for Strengthening Diagnosis and Treatment of Chronic Diabetes Disease.

Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications.

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease.

As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial.

DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images.

In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays.

Amino-functionalised mesoporous silica microspheres for immobilisation of lipase B - application towards greener production of 2,5-furandicarboxylic acid.

In the present study, amino-functionalised mesoporous silica microspheres were utilised as support for the covalent immobilisation of lipase B (CaLB) for the subsequent production of 2,5-furandicarboxylic acid (FDCA) from 2,5-diformylfuran (DFF). Under the optimised operating conditions of pH 6.5, particle/enzyme ratio of 1.

Green synthesis of silver nanoparticles using Nardostachys jatamansi and evaluation of its anti-biofilm effect against classical colonizers.

In this communication, we present the green synthesis of silver nanoparticles (AgNPs) using medicinally important Nardostachys jatamansi rhizome extract in the presence of sunlight. UV-vis spectroscopy, Fourier Transform Infrared spectroscopy (FTIR), Transmission electron microscope (TEM) and Energy dispersive X-ray analysis (EDX) were employed to characterize the synthesized AgNPs. UV-visible spectroscopic studies confirmed the presence of biosynthesized AgNPs.

Recent developments in the use of tyrosinase and laccase in environmental applications.

Our current global environmental challenges include the reduction of harmful chemicals and their derivatives. Bioremediation has been a key strategy to control the massive presence of chemicals in the environment. Enzymes including the phenoloxidases, laccases and tyrosinases, are increasingly being investigated as "green products" in the removal of many chemical contaminants in waters and soils.

A stereoselective synthesis of the C9-C19 subunit of (+)-peloruside A.

The stereoselective synthesis of a C9-C19 fragment of the potent antitumor agent peloruside A is disclosed. The C11 stereogenic centre was created by a vinylogous Mukaiyama aldol reaction following Carreira's protocol, with excellent stereocontrol. The C13 stereogenic centre was introduced by a substrate controlled reduction.

Preparation and characterization of porous cross linked laccase aggregates for the decolorization of triphenyl methane and reactive dyes.

The production of porous cross-linked enzyme aggregates (p-CLEAs) is a simple and effective methodology for laccase immobilization. A three-phase partitioning technique was applied to co-precipitate laccase and starch, followed by cross-linking with glutaraldehyde and removal of starch by α-amylase to create pores in the CLEAs. Scanning electron microscopy revealed a very smooth spherical structure with numerous large pores.

Carnosic acid: a potent chemopreventive agent against oral carcinogenesis.

The present study was aimed to investigate the chemopreventive potential of carnosic acid in 7,12-dimethylbenz(a)anthracene (DMBA)-induced hamster buccal pouch carcinogenesis. The chemopreventive potential was assessed by analyzing the tumor incidence, tumor volume and burden as well as by measuring the status of lipid peroxidation, non-enzymatic and enzymatic antioxidants and phase I and phase II detoxification enzymes. Oral squamous cell carcinoma was developed in the buccal pouch of golden Syrian hamsters by painting with 0.