Highlights: AI-powered liquid biopsy detects ovarian cancer early Combines DNA fragment analysis and biomarkers

A breakthrough AI-powered liquid biopsy combining cell-free DNA and protein biomarkers improves early detection of ovarian cancer, aiding in accurate screening.

Hu, K; Issler, D R; Chen, Z. Geological Survey of Canada, Open File 6692, 2021, 37 pages, https://doi.org/10.4095/327948
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/gid_327948.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/gid_327948.jpg" title="Geological Survey of Canada, Open File 6692, 2021, 37 pages, https://doi.org/10.4095/327948" height="150" border="1" /></a>

Hu, K; Issler, D R; Chen, Z; Dietrich, J R; Dixon, J. GeoConvention 2022, abstracts; 2022, 1 sheet
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20220002.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20220002.jpg" title="GeoConvention 2022, abstracts; 2022, 1 sheet" height="150" border="1" /></a>

Facebook has acquired emotion detection start-up FacioMetrics to push its artificial intelligence (AI) research into building facial gesture controls.

Scientists have developed a new smartphone app that tracks eye movement to determine, in less than a minute, if a child is showing signs of autism spectrum disorder.

PRZOOM - Newswire (press release) - Mon, 11 Nov 2024 00:00:00 -0500, San Antonio TX United States - PathogenDx’s Microarray Detection Platform is the fastest and most cost-effective test for UTI detection with higher throughput for efficient and accurate diagnosis - PathogenDx.com

About osteoporosis and the risk factors factors, bone scans for detection and treatments for osteoporosis.

Farmscape for November 6, 2024

Research conducted by Iowa State University shows tongue tip fluids collected from stillborn and dead piglets can be used to accurately detect the presence of the virus responsible for PRRS.
With the goal of improving the diagnostic value of tongue tips for PRRS surveillance, an Iowa State University study funded through the Swine Health Information Center, evaluated four different sample collection protocols across 597 tongue tips from stillborn and dead piglets.
SHIC Associate Director Dr. Lisa Becton says this is a relatively easy to use type of sample that's being assessed to determine its accuracy for detecting disease in swine.

Quote-Dr. Lisa Becton-Swine Health Information Center:
The key findings really showed that virus isolation of PRRS can be done from tongue tip fluids.
This is important because, up until this time, it had not been proven that this could happen so verifying that the virus isolation can be done is important because that helps to specifically determine if live virus is present in samples that are collected, not just the presence of the RNA.
It's also important because it does provide a way to evaluate different sample protocols and then determine which of those really are the best suited for the best diagnostic outcomes.
Those things are very important, especially when we're looking at newer sample types to provide veterinarians and producers a way to have confidence in utilization of these alternative sample types.
This information will be utilized and shared both with producers and veterinarians because a lot of times people are looking at what are different ways that are relatively labour friendly to be able to collect samples to assess disease status on their farms and tongue tip fluids were one of those samples that was identified as needing investigation so it was important to have research on this to be able to come up with production protocols that can be used for this sample type.

Dr. Becton acknowledges diagnostic tests can be costly so you want to optimize the diagnostic results from the samples submitted to the lab by understanding the protocols to follow when collecting those samples.
Full results of the study can be found at swinehealth.org.
For more visit Farmscape.Ca.
Bruce Cochrane.

       *Farmscape is produced on behalf of North America’s pork producers

Farmscape for November 7, 2024

Animal health officials are responding to the first case of a pig in the United States testing positive for high path H5N1 avian influenza.
On October 30th USDA confirmed that the first detection in a pig of highly pathogenic H5N1 avian influenza had occurred on a small backyard mixed farm in Oregon that housed poultry, swine, sheep and goats.
Swine Health Information Center Executive Director Dr. Megan Niederwerder notes the investigation was triggered when birds on the farm started showing clinical signs of infection.

Quote-Dr. Megan Niederwerder-Swine Health Information Center:
We know that H5N1 is a specific highly pathogenic avian influenza strain that has really increased with regards to circulation over the last two years.
This is primarily maintained in migratory waterfowl but the virus has spilled over into other mammalian species such as seals and sea lions as well as domestic livestock including dairy cattle, first detected in March of 2024 and now in the first pig in October of 2024.
These pigs that were housed on this farm, there were five pigs, none of these animals were intended for the commercial food supply.
Pork continues to be safe for consumption.
There is no concern about the safety of the nation's pork supply as a result of this finding.
The other aspect of this detection is that none of the pigs that were housed on the operation, including the one that was found to be infected, had any clinical signs.
They were completely healthy so this could indicate a low pathogenicity in pigs.
We're still learning about that.
Only a single pig has been shown to be infected so there's a lot to learn about the potential risk to the swine industry.

Dr. Niederwerder encourages pork producers to review their biosecurity procedures focussing on areas where workers or equipment may be exposed to both dairy farms and pig farms or poultry farms and pig farms.
To keep up to date on the situation visit swinehealth.org.
For more visit Farmscape.Ca.
Bruce Cochrane.

       *Farmscape is produced on behalf of North America’s pork producers

When we last heard from Lumos, the bike helmet company had announced a model which was simply equipped with a "smart" tail light. The firm is getting fancy again, however, with its turn-indicating, 360-degree-illuminating, crash-detecting Nyxel.

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Category: Bicycles, Transport

Tags: bike lights, Cycling, Helmet, Road Safety, Kickstarter

Researchers at the University of Mississippi have come up with a faster, more efficient method for detecting landmines – millions of which pose a lethal threat to people in war-ravaged countries all over the world. This breakthrough, which uses lasers and acoustic vibration, has the potential to save thousands of lives a year.

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Category: Technology

Tags: Landmine Detection, Land Mines, Laser, University of Mississippi

(Telecompaper) Veracity Trust Network has been awarded the Cybersecurity Co-Innovation and Development Fund (CCDF) CyberCall grant of SGD 1 million by the Cyber Security Agency Singapore (CSA)...

In this paper a line detection and segmentation technique is presented. The proposed technique is an improved version of an older one. The experiments have been performed on the training dataset of the ICDAR 2009 handwriting segmentation contest in order to be able to compare, objectively, the performance of the two techniques. The improvement between the older and newer version is more than 24% while the average extra CPU time cost is less than 200 ms per page.

E-mail spam is no more garbage but risk since it recently includes virus attachments and spyware agents which make the recipients' system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning techniques have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should counteract with it. To cope with this, parameters optimization and feature selection have been used to reduce processing overheads while guaranteeing high detection rates. However, previous approaches have not taken into account feature variable importance and optimal number of features. Moreover, to the best of our knowledge, there is no approach which uses both parameters optimization and feature selection together for spam detection. In this paper, we propose a spam detection model enabling both parameters optimization and optimal feature selection; we optimize two parameters of detection models using Random Forests (RF) so as to maximize the detection rates. We provide the variable importance of each feature so that it is easy to eliminate the irrelevant features. Furthermore, we decide an optimal number of selected features using two methods; (i) only one parameters optimization during overall feature selection and (ii) parameters optimization in every feature elimination phase. Finally, we evaluate our spam detection model with cost-sensitive measures to avoid misclassification of legitimate messages, since the cost of classifying a legitimate message as a spam far outweighs the cost of classifying a spam as a legitimate message. We perform experiments on Spambase dataset and show the feasibility of our approaches.

Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.

Aim/Purpose: The aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting. Background: Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of policy driven and plagiarism detection approaches. These mechanisms are either proactive or reactive and focus on identifying, catching, and punishing those found to have cheated or plagiarized. To be more effective against plagiarism, mechanisms that detect cheating or colluding in real-time are desirable. Methodology: In the development of a tool for real-time plagiarism prevention, literature review and prototyping was used. The prototype was implemented in Delphi programming language using Indy components. Contribution: A real-time plagiarism detection tool suitable for use in a computer-based assessment setting is developed. This tool can be used to complement other existing mechanisms. Findings: The developed tool was tested in an environment with 55 personal computers and found to be effective in detecting unauthorized access to internet, intranet, and USB ports on the personal computers. Recommendations for Practitioners: The developed tool is suitable for use in any environment where computer-based evaluation may be conducted. Recommendation for Researchers: This work provides a set of criteria for developing a real-time plagiarism prevention tool for use in a computer-based assessment. Impact on Society: The developed tool prevents academic dishonesty during an assessment process, consequently, inculcating confidence in the assessment processes and respectability of the education system in the society. Future Research: As future work, we propose a comparison between our tool and other such tools for its performance and its features. In addition, we want to extend our work to include testing for scalability of the tool to larger settings.

In today's world, malware has become a part and threat to our computer systems. All electronic devices are very susceptible/vulnerable to various threats like different types of malware. There is one subset of malware called ransomware, which is majorly used to have large financial gains. The attacker asks for a ransom amount to regain access to the system/data. When dynamic technique using machine learning is used, it is very important to select the correct set of features for the detection of a ransomware attack. In this paper, we present two novel algorithms for the detection of ransomware attacks. The first algorithm is used to assign the time stamp to the features (API calls) for the ordering and second is used for the ordering and ranking of the features for the early detection of a ransomware attack.

Smart home devices are vulnerable to various attacks, including distributed-denial-of-service (DDoS) attacks. Current detection techniques face challenges due to nonlinear thought, unusual system traffic, and the fluctuating data flow caused by human activities and device interactions. Identifying the baseline for 'normal' traffic and suspicious activities like DDoS attacks from encrypted data is also challenging due to the encrypted protective layer. This work introduces a concept called synoptic crow search with recurrent transformer network-based DDoS attack detection, which uses the synoptic weighted crow search algorithm to capture varying traffic patterns and prioritise critical information handling. An adaptive recurrent transformer neural network is introduced to effectively regulate DDoS attacks within encrypted data, counting the historical context of the data flow. The proposed model shows effective performance in terms of low false alarm rate, higher detection rate, and accuracy.

Aim/Purpose: The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background: A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomography (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology: An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution: The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings: The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations - 92%. Impact on Society: Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research: The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.

Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.

Aim/Purpose: 5G and IoT are two path-breaking technologies, and they are like wall and climbers, where IoT as a climber is growing tremendously, taking the support of 5G as a wall. The main challenge that emerges here is to secure the ecosystem created by the collaboration of 5G and IoT, which consists of a network, users, endpoints, devices, and data. Other than underlying and hereditary security issues, they bring many Zero-day vulnerabilities, which always pose a risk. This paper proposes a security solution using network slicing, where each slice serves customers with different problems. Background: 5G and IoT are a combination of technology that will enhance the user experience and add many security issues to existing ones like DDoS, DoS. This paper aims to solve some of these problems by using network slicing and implementing an Intrusion Detection System to identify and isolate the compromised resources. Methodology: This paper proposes a 5G-IoT architecture using network slicing. Research here is an advancement to our previous implementation, a Python-based software divided into five different modules. This paper’s amplification includes induction of security using pattern matching intrusion detection methods and conducting tests in five different scenarios, with 1000 up to 5000 devices in different security modes. This enhancement in security helps differentiate and isolate attacks on IoT endpoints, base stations, and slices. Contribution: Network slicing is a known security technique; we have used it as a platform and developed a solution to host IoT devices with peculiar requirements and enhance their security by identifying intruders. This paper gives a different solution for implementing security while using slicing technology. Findings: The study entails and simulates how the IoT ecosystem can be variedly deployed on 5G networks using network slicing for different types of IoT devices and users. Simulation done in this research proves that the suggested architecture can be successfully implemented on IoT users with peculiar requirements in a network slicing environment. Recommendations for Practitioners: Practitioners can implement this solution in any live or production IoT environment to enhance security. This solution helps them get a cost-effective method for deploying IoT devices on a 5G network, which would otherwise have been an expensive technology to implement. Recommendation for Researchers: Researchers can enhance the simulations by amplifying the different types of IoT devices on varied hardware. They can even perform the simulation on a real network to unearth the actual impact. Impact on Society: This research provides an affordable and modest solution for securing the IoT ecosystem on a 5G network using network slicing technology, which will eventually benefit society as an end-user. This research can be of great assistance to all those working towards implementing security in IoT ecosystems. Future Research: All the configuration and slicing resources allocation done in this research was performed manually; it can be automated to improve accuracy and results. Our future direction will include machine learning techniques to make this application and intrusion detection more intelligent and advanced. This simulation can be combined and performed with smart network devices to obtain more varied results. A proof-of-concept system can be implemented on a real 5G network to amplify the concept further.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.

Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.

This work demonstrates a hybrid deep learning (DL) model for lung cancer (LC) detection using CT images. Firstly, the input image is passed to the pre-processing stage, where the input image is filtered using a BF and the obtained filtered image is subjected to lung lobe segmentation, where segmentation is done using squeeze U-SegNet. Feature extraction is performed, where features including entropy with fuzzy local binary patterns (EFLBP), local optimal oriented pattern (LOOP), and grey level co-occurrence matrix (GLCM) features are mined. After completing the extracting of features, LC is detected utilising the hybrid efficient-ShuffleNet (HES-Net) method, wherein the HES-Net is established by the incorporation of EfficientNet and ShuffleNet. The presented HES-Net for LC detection is investigated for its performance concerning TNR, and TPR, and accuracy is established to have acquired values of 92.1%, 93.1%, and 91.3%.

Shuoyan LIU,Chao LI,Yuxin LIU,Yanqiu WANG, Vol.E107-D, No.11, pp.1468-1471
Escalators are an indispensable facility in public places. While they can provide convenience to people, abnormal accidents can lead to serious consequences. Yolo is a function that detects human behavior in real time. However, the model exhibits low accuracy and a high miss rate for small targets. To this end, this paper proposes the Small Target High Performance YOLO (SH-YOLO) model to detect abnormal behavior in escalators. The SH-YOLO model first enhances the backbone network through attention mechanisms. Subsequently, a small target detection layer is incorporated in order to enhance detection of key points for small objects. Finally, the conv and the SPPF are replaced with a Region Dynamic Perception Depth Separable Conv (DR-DP-Conv) and Atrous Spatial Pyramid Pooling (ASPP), respectively. The experimental results demonstrate that the proposed model is capable of accurately and robustly detecting anomalies in the real-world escalator scene.
Publication Date: 2024/11/01

CATS are secretive by nature and they have evolved to hide signs of illness and pain. Oftentimes owners only discover the cat’s sickness at the critical stage, which might be too late or harder for the cat to receive treatment. Therefore, early detection and diagnosis of hematuria are important for the cat’s wellbeing.

Hematuria or blood in the urine is one of the clinical signs of feline lower urinary tract diseases (FLUTD) and it may indicate a serious underlying condition.

In effort to continue making the world better for pets, Royal Canin is introducing Hematuria Detection in Malaysia for the very first time. Hematuria Detection are litter granules that enable early detection of microscopic blood traces in cat’s urine in the comfort of cat owner’s home with only three steps.

Royal Canin, at the same time also introduced the FHN Sterilised Wet Pouch, the gravy variant in addition to the earlier launched kibbles, specially tailored for neutered cats.

The OELD App allows workers to use a smart device to configure and maintain the OELD smart junction box, the Searchpoint Optima Plus point infrared gas detector and the Searchline Excel connected to it.

The AimSafety PM100 Single-Gas Monitor and the Four-Gas AimSafety PM400 Multi-Gas Monitor combine innovation, ease of use, fast response time and data aggregation to help workers and organizations provide better safety and workplace toxic threat protection.

Engineered for truck or bus garages, repair and diesel/LNG conversion shops, and test bays and park ramps, this methane detection system can spot leaks as small as 100 parts per million.

The GasLab industrial gas detection safety series is designed to ensure workers stay safe when near toxic and hazardous gases, as well as in explosive environments such as Zones 1 and 2.

Arlington, VA – The Mine Safety and Health Administration has extended the comment period for a proposed rule that would require underground coal mine operators to equip coal-hauling machines and scoops with proximity detection systems.

National Safe Skies Alliance performs its evaluations under an ISO 9001:2015-approved Quality Management System.

SAGE Integration is collaborating with Ambient.ai to enhance enterprise security through real-time AI threat detection and rapid investigations.

When it comes to gunshot detection, no two technologies are alike. What most have in common, however, is their effortless fit within a security integrator’s life-safety product portfolio and business model.

The Foundation Series is a complete, off-the-shelf fire detection solution featuring addressable and non-addressable models, and a full range of Autocall devices.

SDM spoke with industry experts about gunshot and gun detection solutions and the opportunities those solutions offer to security dealers and integrators.

Dave Engebretson shares a new innovation in the intrusion detection and alarm monitoring space. 

 Presence detection is the ability to identify or locate the existence of people in a specific area or physical space.

The “Editor’s Choice” and more new products and solutions as described by manufacturers.

What is the best technology for detecting combustible gases in confined spaces?

OSHA requires a direct-reading portable gas detector to test the atmosphere in a confined space before my workers enter. Why is a direct-reading monitor needed instead of an alarm-only monitor?