Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.

In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.

An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia.

Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learning, is gaining a preferred trend of research in medical image processing because of their admirable performance, even with small datasets. It inspires us to develop a novel deep learning-based leukemia detection system in which an efficient and lightweight MobileNetV2 is used in conjunction with ShuffleNet to boost discrimination ability and enhance the receptive field via convolution layer succession.

Pupilmate: A do-it-yourself frugal pupilometer.

The pupillary direct and consensual reflex is an important non-invasive quick assessment of the neurological state of the eye. Currently, there is no cheap and affordable recording tool for screening and documentation of a relative afferent pupillary defect. We describe how to construct a frugal, do-it-yourself handheld scotopic binocular pupillometer device called Pupilmate.

Capillary-Enhanced Biomimetic Adhesion on Icy Surfaces for High-Performance Antislip Shoe-Soles.

The World Health Organization (WHO) reports 684,000 deaths/year due to slips and falls (SFs), with ∼38 million people requiring medical attention per annum. In particular, SFs on ice surfaces account for 45% of all SF incidents, costing over $100 billion globally in healthcare, intensive care, and insurance expenses. Current antislip solutions focus on hydrophobicity to repel interfacial fluids, aiming to maintain solid-to-solid contact.

Eco-sustainable point-of-care devices: Progress in paper and fabric based electrochemical and colorimetric biosensors.

Monitoring real-time health conditions is a rinsing demand in a pandemic prone era. Wearable Point-of-Care (POC) devices with paper and fabric-based sensors are emerging as simple, low-cost, portable, and disposable analytical tools for development of green POC devices (GPOCDs). Capabilities of passive fluid transportation, compatibility with biochemical analytes, disposability and high degree of tunability using vivid device fabrication strategies enables development of highly sensitive and economically feasible POC sensors in particularly post COVID-19 pandemic outbreak.

An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder.

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively.

Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier.

The fast improvement of cyberattacks in the area of the Internet of Things (IoT) presents novel safety challenges to zero-day attacks. Intrusion detection systems (IDS) are generally focused on exact attacks to defend the use of IoT. However, the attacks were unidentified, for IDS still signifies tasks and concerns about consumers' data privacy and safety.

Harnessing the UiO-67 metal-organic framework for advanced detection of cadmium ions in water bodies.

Heavy metal ions are hazardous pollutants that pose serious threats to ecosystems and human health, making it imperative to detect and monitor their presence in water for environmental protection. This paper highlights the synthesis of the UiO-67 Metal-Organic Framework (MOF) without any dopants, offering a novel approach specifically for the detection of cadmium ions (Cd) in aqueous environments. Following solvothermal synthesis, Powder X-ray Diffraction (PXRD), BET nitrogen adsorption-desorption analysis, X-ray Photoelectron Spectroscopy (XPS), and Scanning Electron Microscopy (SEM) were used to characterize the structural and morphological features of UiO-67.

Segmentation and classification of brain tumor using Taylor fire hawk optimization enabled deep learning approach.

The brain is a crucial organ that controls the body's neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignancies.

A privacy-preserving expert system for collaborative medical diagnosis across multiple institutions using federated learning.

Expert system recommendation assists the healthcare system to develop in real-time monitoring and diagnosis of patient conditions over several healthcare institutions. Privacy concerns, however, present significant problems since patient data leaks can lead to big effects including financial losses for hospitals and invasions of personal privacy for people. To address these issues, the research introduces a privacy-preserving collaborative medical diagnosis (CMD) method on a federated learning (FL).

A review on recent advances in g-CN-MXene nanocomposites for photocatalytic applications.

The solutions for environmental remediation and renewable energy generation have intensified the exploration of efficient photocatalytic materials. Recently, the composites of g-CNand MXene have gained considerable interest for their potential applications in photocatalysis. In the g-CN-MXene composite, the g-CNpossesses unique physical, chemical, and optical properties to increase visible light absorption.

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.

Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location.

Energy-efficient quad tree-based clustering using edge-assisted UAV-relay to enhance network lifetime in WSN.

Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering.

Compositional effects of hybrid MoS-GO active layer on the performance of unipolar, low-power and multistate RRAM device.

Currently, 2D nanomaterials-based resistive random access memory (RRAMs) are explored on account of their tunable material properties enabling fabrication of low power and flexible RRAM devices. In this work, hybrid MoS-GO based active layer RRAM devices are investigated. A facile hydrothermal co-synthesis approach is used to obtain the hybrid materials and a cost-effective spin coating method adopted for the fabrication of Ag/MoS-GO/ITO RRAM devices.

Silicon nanowire piezoresistor and its applications: a review.

Monocrystalline bulk silicon with doped impurities has been the widely preferred piezoresistive material for the last few decades to realize micro-electromechanical system (MEMS) sensors. However, there has been a growing interest among researchers in the recent past to explore other piezoresistive materials with varied advantages in order to realize ultra-miniature high-sensitivity sensors for area-constrained applications. Of the various alternative piezoresistive materials, silicon nanowires (SiNWs) are an attractive choice due to their benefits of nanometre range dimensions, giant piezoresistive coefficients, and compatibility with the integrated circuit fabrication processes.

Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation.

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung.

MXene-Embedded Porous Carbon-Based CuO Nanocomposites for Non-Enzymatic Glucose Sensors.

This work explores the use of MXene-embedded porous carbon-based CuO nanocomposite (CuO/M/AC) as a sensing material for the electrochemical sensing of glucose. The composite was prepared using the coprecipitation method and further analyzed for its morphological and structural characteristics. The highly porous scaffold of activated (porous) carbon facilitated the incorporation of MXene and copper oxide inside the pores and also acted as a medium for charge transfer.

Design, Optimization and Performance Assessment of Single Port Film Bulk Acoustic Resonator through Finite Element Simulation.

In this paper, the study is supported by design, FEA simulation, and practical RF measurements on fabricated single-port-cavity-based acoustic resonator for gas sensing applications. In the FEA simulation, frequency domain analysis was performed to enhance the performance of the acoustic resonator. The structural and surface morphologies of the deposited ZnO as a piezoelectric layer have been studied using XRD and AFM.

Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms.

Objective: Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms.

Review on Uric Acid Recognition by MOFs with a Future in Machine Learning.

Uric acid (UA) is produced from purine metabolism and serves as a prevalent biomarker for multiple diseases including cancer. Hyperuricemia or hypouricemia can cause multiple dysfunctions throughout the biological processes. Consequently, there is a pressing need for monitoring UA concentration in body fluid.

An automated system of intrusion detection by IoT-aided MQTT using improved heuristic-aided autoencoder and LSTM-based Deep Belief Network.

The IoT offered an enormous number of services with the help of multiple applications so it faces various security-related problems and also heavy malicious attacks. Initially, the IoT data are gathered from the standard dataset as Message Queuing Telemetry Transport (MQTT) set. Further, the collected data are undergone the pre-processing stage, which is accomplished by using data cleaning and data transformation.

Implications of Toll-like receptors (TLRs) and their signaling mechanisms in human cancers.

Most essential pattern-recognition receptors regulating innate immune functions are toll-like receptors (TLRs). TLRs are characterized by lack of concurrent epithelial markers and are typically identified by their gene expressions. One major mechanism by which TLRs generate their effector functions is by triggering inflammatory responses.

TiS Nanoribbons: A Novel Material for Ultra-Sensitive Photodetection across Extreme Temperature Ranges.

Photodetectors that can operate over a wide range of temperatures, from cryogenic to elevated temperatures, are crucial for a variety of modern scientific fields, including aerospace, high-energy science, and astro-particle science. In this study, we investigate the temperature-dependent photodetection properties of titanium trisulfide (TiS)- in order to develop high-performance photodetectors that can operate across a wide range of temperatures (77 K-543 K). We fabricate a solid-state photodetector using the dielectrophoresis technique, which demonstrates a quick response (response/recovery time ~0.

Surface Plasmon Resonance-Based Gold-Coated Hollow-Core Negative Curvature Optical Fiber Sensor.

The hollow-core fiber-based sensor has garnered high interest due to its simple structure and low transmission loss. A new hollow-core negative-curvature fiber (HC-NCF) sensor based on the surface plasmon resonance (SPR) technique is proposed in this work. The cladding region is composed of six circular silica tubes and two elliptical silica tubes to reduce fabrication complexity.

A Novel Lightweight CNN Architecture for the Diagnosis of Brain Tumors Using MR Images.

Over the last few years, brain tumor-related clinical cases have increased substantially, particularly in adults, due to environmental and genetic factors. If they are unidentified in the early stages, there is a risk of severe medical complications, including death. So, early diagnosis of brain tumors plays a vital role in treatment planning and improving a patient's condition.