The European Union’s new Entry/Exit System (EES), launched with a phased rollout in October 2025, will fundamentally transform how non-EU travellers move across borders in the 29-country Schengen Area. One key requirement is the capture of biometric data – facial images and fingerprints – along with passport details and travel history. While all Schengen border points must have EES fully deployed by April 2026, the phased rollout has highlighted both the promise and operational challenges of large-scale biometric implementation. Yes, EU residents and visitors can say goodbye to smudgy passport stamps and hello to a fully digital travel experience, but this also underscores the critical element of accuracy, resilience and scalability in these systems. This wake-up call has upended staid passenger processing systems that include manual identity checks and document-based verification. The implementation of these new systems provides real-world data on their effectiveness, and the early signs are beyond encouraging. Biometric verification can reduce airport processing times by 30% to over 80% compared to manual checks, depending on implementation. U.S. Customs and Border Protection reports that biometric Global Entry kiosks cut processing time from 45 seconds to under six seconds per traveller. EES is more than a compliance mandate – it’s a strategic shift toward smarter, more secure borders. For airports, it’s an opportunity to modernise operations, improve passenger flow and align with the EU’s broader digital transformation goals. By 2026, more than half of airports expect to use biometrics at check‑in and bag drop, and around 70% of airlines plan to have biometric identity management systems in place, reflecting both the appeal of dependable AI‑enabled processes and the growing risks of clinging to legacy workflows. Passengers have also expressed an overwhelming preference for biometric experiences. Roughly three-quarters of travellers worldwide say they would share their biometric information if it
Apr 23, 2026 · via regionalgateway.net
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Apr 23, 2026 · via openpr.com
Face-selective neurons in the macaque visual cortex dynamically change their tuning properties, according to a study published last month in Nature. The findings suggest that artificial neural networks—which are stably tuned—may not fully model visual systems in primates. Neurons in the inferotemporal cortex initially respond to both faces and inanimate objects, the study found. But then those cells switch to respond to specific facial features, including inter-eye distance and hair color. That switch likely corresponds to a shift from the brain recognizing the presence of a face to gauging what that face looks like, according to the study investigators. “[This shift] is a phenomenon that has never been characterized before,” says Laura Gwilliams, assistant professor of psychology at Stanford University, who was not involved in the work. “We need to rethink how neurons code information.” Conflicting evidence has fueled a debate in face perception research: On the one hand, the visual cortex contains clusters of highly selective neurons—known as face patches—indicating that the region uses specialized mechanisms for face processing. And stimulating face-selective brain areas in primates, including humans, distorts the perception of faces but not of non-face objects. Yet, other studies suggest that the neurons employ a general code to respond to a range of visual features. The new paper “offers a potential solution to solve this controversy,” says Shahab Bakhtiari, assistant professor of psychology at the Université de Montréal, who did not take part in the study. “The two theories were looking at the same thing but at different [timepoints].” Neurons in the inferotemporal cortex initially use a general code, then switch to a face-specific code in a matter of milliseconds, the new study found. By contrast, convolutional neural networks—image-processing algorithms that attempt to model visual pathways—use a nonspecific approach for facial recognition. “I think it’s very interesting
Apr 23, 2026 · via thetransmitter.org
To evaluate SSD performance in real-world AI workloads, we tested the load times of various deep learning models. These include both image classifiers (such as ResNet, VGG19, and EfficientNet) and a large language model (LLaMA 2 7B). Since model sizes and complexities vary widely, we calculated the geometric mean across all tests to provide a clear and balanced comparison. The chart below gives an at-a-glance view of which drives handle AI-related loading tasks most efficiently. The chart above presents the average of all our comparisons, including tests with LLMs (Large Language Models) such as LLaMA 2 7B, as well as various image classification models that will be detailed below. Individual Test Results Large Language Models To evaluate real-world AI performance, we use a benchmark that measures the time it takes to load the LLaMA 2 7B large language model from SSD storage into system and GPU memory. The procedure replicates a typical machine learning workflow, where models need to be initialized quickly for inference or fine-tuning. Using the Hugging Face Transformers library in offline mode ensures the model is loaded entirely from local storage, without network interference. The tokenizer and full model are preloaded using PyTorch in float16 precision to simulate a realistic deployment scenario, and the total loading time—from disk to memory—is recorded. By running the benchmark test across multiple SSDs, we can identify which ones deliver the fastest model initialization times, a critical factor for AI tasks that demand low startup latency. Image Classification Models Benchmark Our benchmark measures the loading time of large image classification models by analyzing two stages: transfer from SSD to system memory, and then to the GPU. Each model is loaded 20 times to calculate an average and standard deviation for reliable results. The goal is to compare loading efficiency across architectures—particularly important
Apr 23, 2026 · via techpowerup.com
It’s a phrase we use a lot in our community, “Drink the Kool-Aid”, meaning becoming unreasonably infatuated with a dubious idea, technology, or company. It has its origins in 1960s psychedelia, but given that it’s popularly associated with the mass suicide of the followers of Jim Jones in Guyana, perhaps we should find something else. In the sense we use it though, it has been flowing liberally of late with respect to AI, and the hype surrounding it. This series has attempted to peer behind that hype, first by examining the motives behind all that metaphorical Kool-Aid drinking, and then by demonstrating a simple example where the technology does something useful that’s hard to do another way. In that last piece we touched upon perhaps the thing that Hackaday readers should find most interesting, we saw the LLM’s possibility as a universal API for useful functions. It’s Not What An LLM Can Make, It’s What It Can Do When we program, we use functions all the time. In most programming languages they are built into the language or they can be user-defined. They encapsulate a piece of code that does something, so it can be repeatedly called. Life without them on an 8-bit microcomputer was painful, with many GOTO statements required to make something similar happen. It’s no accident then that when looking at an LLM as a sentiment analysis tool in the previous article I used a function GetSentimentAnalysis(subject,text) to describe what I wanted to do. The LLM’s processing capacity was a good fit to my task in hand, so I used it as the engine behind my function, taking a piece of text and a subject, and returning an integer representing sentiment. The word “do” encapsulates the point of this article, that maybe the hype has got it
Apr 22, 2026 · via hackaday.com
Face ID Vs Fingerprint: Which Is Better For Your Phone Security? Phone security has evolved well past requiring PIN numbers and passcodes to unlock them. These days, phones have biometrics, a form of identification that uses a person's unique traits to grant them access to the device. Two common forms of biometrics used in smartphones are Face ID (also known as facial recognition) and fingerprint scanning. No two faces or fingerprints are exactly alike, making them highly secure ways of preventing unwanted access to your phone. When it comes to Face ID vs. fingerprint for phone security, it can be hard to pick between the two. Since both have unique strengths and weaknesses, the better choice depends on several factors. But ultimately Face ID is more secure. However, it's best to ensure the phone uses 3D Face ID technology like you'll find on the value-packed Apple iPhone 17 or Huawei Mate 80 Pro. Android phones, like the Samsung Galaxy S26 Ultra, with all its cool features, use 2D facial recognition. This is less secure because it relies on flat image patterns instead of depth, which can be tricked by a photo. Still, fingerprints may be preferred if reliability and privacy are your biggest security concerns. Face ID has stronger security than fingerprints Security is the biggest reason to choose facial recognition over fingerprints for unlocking your phone. Basically, a device with this biometric feature maps your face by projecting thousands of invisible dots onto it, and then analyzing them to create a detailed 3D map. It's extremely hard to fool with a flat visual of yourself (photo or video) or a realistic mask. For instance, the TrueDepth camera on the iPhone will reject these tricks when it fails to identify the contours of a human face. Fingerprint scanners, on the
Apr 22, 2026 · via bgr.com
The American Civil Liberties Union has filed complaints against police departments in Montgomery, Prince George’s and Anne Arundel counties, claiming a false identification resulting from a facial recognition technology search led an innocent woman to be jailed for six months. Kimberlee Williams, an Oklahoma resident, said she had never been to Maryland before she was arrested on an outstanding warrant from Montgomery County while trying to drop off a food delivery order at a military base. “I was flown there in handcuffs, for a crime I had nothing to do with,” Williams said in an ACLU news release. “My family and I can’t get that time back, but I hope my experience will be a warning to police in Maryland and across the country that this technology can ruin lives. No family deserves to go through that.” The ACLU said Williams is the 14th person it knows of who was wrongfully arrested because of faulty facial recognition technology. Williams’ ordeal began when an investigator working for a bank uploaded an image of a suspect who had withdrawn thousands of dollars by impersonating account holders in Maryland branches. The investigator uploaded the image to a national LISTSERV of police and private investigators called CrimeDex. Someone on the LISTSERV ran the image through facial recognition and sent back Williams’ name and photo as a match. Read More The bank investigator notified a Montgomery County Police Department detective of the match in a memo, citing “facial recognition software,” and the department obtained an arrest warrant “without any independent investigation,” according to the ACLU. But that detective in Montgomery County never disclosed how he had come across Williams’ name, according to the ACLU. Instead, he “misleadingly claimed that Ms. Williams had been ‘identified’ as the suspect and that the detective had confirmed the identification
Apr 22, 2026 · via thebanner.com
Air Force Gen. Steven Nordhaus, chief, National Guard Bureau, presents the 2026 Senator Ted Stevens Leadership Award during the Tragedy Assistance Program for Survivors Gala in Washington, D.C., Mar. 17, 2026. The award was presented to Sara Wilson in recognition of her advocacy and support for Gold Star Families. (U.S. Army National Guard photo by Staff Sgt. Kelly Boyer)
| Date Taken: | 03.17.2026 |
| Date Posted: | 04.22.2026 15:25 |
| Photo ID: | 9633491 |
| VIRIN: | 260317-A-KB362-1002 |
| Resolution: | 5875x3909 |
| Size: | 2.23 MB |
| Location: | DISTRICT OF COLUMBIA, US |
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This work, Nordhaus Presents TAPS Award [Image 3 of 3], by SSG Kelly Boyer, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Apr 22, 2026 · via dvidshub.net
A transgender fan of the New York Knicks basketball team was tracked over a two-year period using sophisticated face recognition technology at Madison Square Garden (MSG), according to a lawsuit by a fired former security official who worked there. The woman was later banned from the venue. The suit alleges that Nina Richards (not her real name) became a fixation for the head of security working for James Dolan, the owner of MSG, Radio City Music Hall, and the Sphere, among other venues that use the same surveillance technology. Dolan, scion of former cable TV titan Charles Dolan, also owns the Knicks and the New York Rangers hockey team. Related The lawsuit’s allegation follows news in 2024 that Dolan added the faces of up to 1,500 lawyers involved in litigation against his companies to his facial recognition system at MSG, banning them from the property. Security head John Eversole became aware of Richards in 2021, soon after games resumed at the Garden following the COVID shutdowns, Wired reports. Never Miss a Beat Subscribe to our newsletter to stay ahead of the latest LGBTQ+ political news and insights. Described by another security staffer as “a very large transgender woman, being a fan,” Eversole determined that Richards was a threat to players and the team’s image. He ordered her to be added MSG’s facial-recognition database and instructed staffers to perform an open source “work-up” on her, based on the images captured by their cameras and other intelligence gathered by security. Eversole allegedly wanted to keep Richards “away from the players.” Richards was targeted “because of her gender identity,” according to the suit filed by former MSG security staffer Donnie Ingrasselino. Employees who were forced to conduct the surveillance were often uncomfortable, according to another former staffer, since they believed it to be
Apr 22, 2026 · via lgbtqnation.com
Getty Images/iStockphoto/Urupong Future mission success requires overcoming today’s data sharing challenges Commentary Read more
Apr 22, 2026 · via federalnewsnetwork.com
Immigration and Customs Enforcement bureaucrats are reportedly planning to use specialty facial recognition glasses to collect data on Americans in real time, independent journalist Ken Klippenstein revealed. Financial statements viewed by Klippenstein point to the development of a facial recognition platform modeled after commercially available AI smart glasses, like Meta’s widely-panned “pervert glasses.” ICE’s in-house model, it seems, will allow agents to monitor video and reference vast federal databases of biometric information on subjects regardless of if they’ve been arrested, or even charged with a crime. “The project will deliver innovative hardware, such as operational prototypes of smart glasses, to equip agents with real-time access to information and biometric identification capabilities in the field,” read an ICE budget document leaked to Klippenstein. Perhaps most alarmingly, Department of Homeland Security insiders told the investigative journalist that the technology involved isn’t limited to immigration enforcement. “It might be portrayed as seeking to identify illegal aliens on the streets,” one anonymous DHS attorney told Klippenstein, “but the reality is that a push in this direction affects all Americans, particularly protestors.” That reveal comes just a few months after an incident in Maine in which an ICE agent admitted to scanning protestors’ faces with his phone. “We have a nice little database, and now you’re considered domestic terrorists,” the agent tells a couple who were out documenting the immigration agents in their community. In October, 404 Media reported that ICE agents were scanning peoples’ faces in order to check whether they were citizens. These targets for surveillance are often chosen at random — we now know that many of ICE’s arrests over the past year have been circumstantial, a far cry from the targeted enforcement of known criminals the Trump administration promised. Taken together, what began as surveillance infrastructure marketed for catching illegal immigrants
Apr 22, 2026 · via futurism.com
Police slammed for 'Orwellian overreach' as facial recognition cameras wrongfully identifies 59-year-old man The man has been scared to leave his home following the incident Don't Miss Most Read The police have been called upon to reveal how facial recognition cameras are being used after a 59-year-old was wrongfully arrested because of the technology. Colin McMahon was accused of stealing £300 worth of furniture from Ikea after he was flagged by the scanner in public last year. He was handcuffed on Harlesden High Street by the Metropolitan Police following cameras linking him with an offence that took place in February 2025. Mr McMahon was eventually acquitted by magistrates earlier this year after it was revealed he had actually been running an Alcoholics Anonymous meeting 10 miles away at the time of the theft. TRENDING Stories Videos Your Say Following the revelations, Big Brother Watch has urged police to be transparent about the use of facial recognition software. Jack Coulson told GB News: “It used to be 'computer says no', now it's 'computer says arrest'. “The police are making up their own rules and experimenting on the public. “This is not about keeping the public safe from the most dangerous criminals. A man spent being accused of a crime he did not commit due to being wrongfully identified by a facial recognition camera |GETTY “An innocent man has been dragged to the dock for decor. “We are calling on the police to come clean about how many innocent people they have put onto watchlists and how many they have incorrectly arrested. “A moratorium is needed until the government steps-in with legislation to protect the British public from this Orwellian overreach.” Mr McMahon had been in a meeting regarding a homeless charity project last October when he was arrested moments after leaving.
Apr 22, 2026 · via gbnews.com
Early learning in the age of AI: why human skills matter more than ever Artificial intelligence is reshaping industries, workplaces and daily life at remarkable speed. Amid growing excitement about innovation, and concern about automation, one reality is becoming clearer: the skills likely to matter most in an AI-driven world are deeply human. That was a key theme in a recent Khaleej Times opinion piece exploring what early learning should look like in the age of AI. For Australia’s early childhood education and care (ECEC) sector, the message is particularly relevant. As AI becomes more capable, early learning may need to become more intentional. Not more digital. Not more screen-based. But more human-centred. AI is not the threat, poorly designed learning is Artificial intelligence is already highly effective at: - pattern recognition - data processing - automation - prediction. What it cannot replicate in the same way are the capabilities that sit at the heart of human development, including: - empathy - creativity - ethical judgement - curiosity - imagination - social connection. These are also the foundations built in the early years. The challenge for educators and policymakers is not whether children should compete with machines. It is whether learning environments are designed to nurture the qualities machines cannot replace. This aligns strongly with Australia’s Early Years Learning Framework, which emphasises belonging, being and becoming through relationships, play and responsive teaching. The core skills children need in an AI world Creativity and imagination AI can generate text, music and images based on prompts. It cannot independently create meaning, purpose or vision in the human sense. Young children develop creativity through: - open-ended play - storytelling - music and movement - art experiences - designing, building and inventing. These experiences support flexible thinking and innovation later in life. Critical thinking
Apr 22, 2026 · via thesector.com.au
Sign up for The Agenda, Them’s news and politics newsletter, delivered Thursdays. The security staff for Jim Dolan, the owner of the Knicks, as well as the CEO of Madison Square Garden, allegedly used surveillance technology to track a transgender woman’s near-every movement during her frequent visits to the New York sporting venue, per a bombshell new report in Wired. The report alleges that Dolan has been using face-recognition technology in excessive and intrusive ways since at least 2018, watching a plethora of people, including his personal critics, at both Madison Square Garden, as well as his other properties, including Radio City Music Hall and the Sphere in Las Vegas. The report describes MSG as his own “panopticon.” One of the most obsessively tracked people under Dolan’s alleged paranoid watch has been Nina Richards, a trans woman who, according to Wired, was monitored during a two-year period when she frequented the venue. (Richards is a pseudonym for the woman, who requested the outlet not name her due to privacy concerns.) The report claims that, shortly after the Garden’s reopening in 2021, Richards “became a fixation” for Dolan’s chief security officer John Eversole. Eversole allegedly compiled a dossier on Richards solely for the reason that she was a trans woman and “wanted to keep her away from the players,” per Wired. This reportedly included entering her face into the venue’s facial recognition system. A lawsuit filed by former MSG security vice president Donald Ingrasselino claims that Eversole would show Richards’ picture during weekly meetings, misgender her, and tell his employees to look out for “him or it or whatever it is.” The suit also claimed Richards was targeted “because of her gender identity.” An employee likewise told Wired that “she posed no threat” but was forced to monitor her by Eversole."She
Apr 22, 2026 · via advocate.com
Tinder responds to viral video about tricking facial scan Earlier this month, journalist Christophe Haubursin published a YouTube video called "Something very weird is happening on Tinder." In the video, which has over 1.5 million views as of this publication, Haubursin described a way to workaround to Tinder's Face Check feature — the facial recognition that is now required for all U.S. users as of Oct. 2025. What Haubursin and his interviewees discovered is a bunch of profiles that appeared normal, but the last photo on each profile was…off. It was usually a digitally-altered image of a different person in a weird scenario, like on a billboard or in a Victorian painting. And if someone matched with this person and asked about the image, they dodged the question. Instead, they asked to move the conversation to WhatsApp, where it became clear they were romance scammers. But how did they evade Face Check? Haubursin found that Tinder and Hinge, both owned by Match Group, only need one photo for the facial recognition software. So these people may be the actual person in that odd image, and able to pass the face scan. Then, they could grift images of other people from the internet to use for the bulk of their profile. You May Also Like AdultFriendFinder — readers’ pick for casual connections Tinder — top pick for finding hookups Hinge — popular choice for regular meetups Tinder didn't respond to Haubursin's request for comment, but it did respond to Mashable's. "We're aware of the concerns raised about our Photo Verification and Face Check features. In recent weeks, we've taken action to strengthen our Photo Verification badging logic, including requiring greater consistency across profile photos and additional reviews to achieve higher confidence in cases that warrant extra scrutiny," a Tinder spokesperson told
Apr 22, 2026 · via mashable.com
UK court determines police use of live facial recognition legal Live facial recognition can be deployed nationwide, a U.K. court ruled Tuesday, dismissing arguments from plaintiffs who said the technology is racially biased and is used arbitrarily. The cameras are typically placed in high-traffic areas and are used to match pictures of passersby to a watchlist of criminal suspects. London’s Metropolitan Police celebrated the decision, saying the technology has helped them secure 2,100 arrests — including of 100 sex offenders — since 2024. One of the plaintiffs, Shaun Thompson, said he was wrongly identified by the cameras and improperly threatened with arrest. The other, Silkie Carlo, is a privacy advocate with Big Brother Watch, an organization that fights against surveillance technologies. Thompson was threatened with arrest in February 2024 near London Bridge after he was mistakenly identified as his brother, who is on a police watchlist, according to a media summary provided by the court. He refused to provide police with fingerprints to prove his identity. The episode left him “distressed, angry, and fearful of being wrongly identified again,” according to the media summary. Thompson and Carlo did not argue that live facial recognition is illegal in principle, but instead contended that the Metropolitan Police’s policy leaves “too much discretion to police officers as to where, why and against whom LFR may be used” and thereby violates European human rights law. The decision comes at a time when the U.K. has been roiled by debate about law enforcement’s use of facial recognition technology. In December, the British Home Office said it wanted law enforcement to use facial recognition at “significantly greater scale.” It launched a public consultation to determine how to implement a more robust legal framework for its deployment. Hours after the announcement, the Home Office released a report
Apr 22, 2026 · via therecord.media
TOKYO -- Japan Airlines plans to give passengers the option of using facial recognition instead of tickets to board international flights by 2035, hoping to make the process simpler and more secure.
Japanese airline aims to implement system by 2035 that can replace ID, tickets
A passenger passes through a gate using Japan Airlines' facial recognition technology. (JAL)
TOKYO -- Japan Airlines plans to give passengers the option of using facial recognition instead of tickets to board international flights by 2035, hoping to make the process simpler and more secure.
Apr 22, 2026 · via asia.nikkei.com
Abstract Targeted and immune-based therapies, such as PD-1/PD-L1 inhibitors, have become standard treatments for advanced non-small cell lung cancer (NSCLC). However, accurately identifying patients who benefit from these therapies remains challenging due to tumor heterogeneity and variability in PD-L1 staining. To address this issue, we propose a non-invasive, model-driven computer-aided diagnosis framework that predicts PD-L1 expression directly from CT images under limited labeled data conditions. This study included 188 NSCLC patients from two university hospitals, of whom 49 had PD-L1 expression ≥ 50% and 139 had < 50%. We introduce a Multi-task Masked Autoencoder (MTMAE) with three key components: (1) a self-supervised masked image modeling strategy to leverage unlabeled data and improve data efficiency, (2) an integrated segmentation task to enhance tumor-focused feature learning, and (3) a Gabor-based generative adversarial network for data augmentation to improve generalization. The proposed model achieved an AUC of 0.735 and an accuracy of 0.724, outperforming traditional supervised pretraining (AUC 0.695) and single-task MAE (AUC 0.712). These results demonstrate that combining self-supervised learning, multi-task learning, and GAN-based augmentation enables a reproducible and standardized model-based prediction of clinically reported PD-L1 status from CT images, providing a non-invasive complementary tool for treatment decision-making. Data availability The datasets used and/or analyzed during the current study are not publicly available due to patient privacy concerns and institutional review board (IRB) restrictions but are available from the corresponding authors upon reasonable request and subject to appropriate ethical approval and data-sharing agreements. References Siegel, R. L., Miller, K. D., Wagle, N. S. & Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 73(1), 17–48 (2023). Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer. J. Clin. 68(6), 394–424 (2018). Siegel, R. L., Miller, K. D., Fuchs, H.
Apr 22, 2026 · via nature.com
Pair lose High Court challenge against Met use of Live Facial Recognition - as Sir Mark Rowley defends tech's rapid rollout The High Court has thrown out a legal challenge brought by a pair over the Met's use of facial recognition technology in London Two people have lost their High Court challenge against the Metropolitan Police over its use of Live Facial Recognition (LFR) cameras - as Sir Mark Rowley denied the force had moved too quickly with its expansion. Listen to this article Youth worker Shaun Thompson, who was misidentified by the technology, and Silkie Carlo of campaign group Big Brother Watch brought the claim. They had concerns over the arbitrary or discriminatory use of facial recognition technology across London. Met commissioner Sir Mark Rowley warned "clumsy regulations" could "strangle" the progress of facial recognition at birth - as he denied the force had moved too quickly in its expansion of the technology. Sir Mark made the comments after a High Court challenge brought against the Metropolitan Police over its use of facial recognition technology in London was thrown out by judges. The pair's lawyers had argued in a hearing earlier this year that facial recognition is "similar to a DNA profileâ and that installing the technology permanently would make it "impossible" for Londoners to travel without their biometric data being taken and processed. Read more: Sainsburyâs apologises after kicking innocent man out of supermarket in facial recognition mix-up Read more: Home Secretary defends controversial police facial recognition as thousands already arrested Scotland Yard defended the legal challenge, telling the court its live facial recognition policy was lawful. Lord Justice Holgate and Mrs Justice Farbey said in a judgment on Tuesday: âIn the context of promoting law and order in a large metropolis, the policy provides the claimants with
Apr 22, 2026 · via lbc.co.uk
Figures Abstract The classification of brain tumors is an unsolved problem associated with heterogeneity of tumors and fluctuations in imaging conditions. In this work, the investigation introduces a powerful novel framework, named LHW-Net that combines handcrafted features called local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT). Within the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach to improve classification performance. The effectiveness and robustness of the proposed work are validated by the achieved experimental results on commonly accepted benchmark datasets. Citation: Suryadevara T, Mahamkali N, Rafi M (2026) LHW-Net: An ensemble-based machine learning framework for brain tumor classification. PLoS One 21(4): e0346821. https://doi.org/10.1371/journal.pone.0346821 Editor: Hikmat Ullah Khan, University of Sargodha, PAKISTAN Received: August 9, 2025; Accepted: March 24, 2026; Published: April 21, 2026 Copyright: © 2026 Suryadevara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All the data used in the publication are publicly available in repositories with following URL’s Brain Tumor MRI Dataset: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset. Brain Tumor Image Dataset https://www.kaggle.com/datasets/denizkavi1/brain-tumor/data. Br35H Brain Tumor Detection 2020 https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection. Funding: The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/588/46. Competing interests: The authors have declared that no competing interests exist. Introduction Brain is an important organ that is at the hub of the human nervous system and collaborates with the spinal cord in order to coordinate body
Apr 21, 2026 · via journals.plos.org