Search

A recent report from researchers at Birmingham Women's and Children's NHS Foundation Trust discusses the diagnosis and treatment of progressive familial intrahepatic cholestasis (PFIC), a rare liver disorder that primarily affects children. The researchers aimed to provide recommendations for the management of PFIC in clinical practice. They developed an algorithm for the diagnosis and treatment of children with suspected PFIC, which includes the use of licensed inhibitors of ileal bile acid transporters as the first-line treatment. The authors hope that these recommendations will help standardize the management of PFIC and raise awareness of current developments in the field. [Extracted from the article]

A study conducted by researchers at Sichuan University of Science and Engineering in China explores the design of smart senior care (SSC) product app interfaces for the elderly. The researchers use deep learning (DL) technology to optimize the app interface design, taking into consideration the cognitive characteristics and habits of the elderly. The study finds that the proposed model based on the deep-Q-network (DQN) algorithm significantly improves the user experience and satisfaction of the elderly, enhancing their ability to utilize smart products and improve their quality of life. The research provides valuable insights for further research and practice in related fields. [Extracted from the article]

A report from the University of Oslo discusses the use of machine learning algorithms and heuristic stopping criteria in systematic reviews in education and educational psychology. The study found that using machine learning algorithms for abstract screening reduced the workload of irrelevant records by an average of 58% and saved an estimated 1.66 days of time. The random forests algorithm with sentence bidirectional encoder representations from transformers performed the best. The study also found that heuristic stopping rules could retrieve 95% of relevant abstracts, with the best results achieved by stopping the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers. The research provides empirical evidence on the performance of machine learning screening algorithms in this field. [Extracted from the article]

A recent study conducted by researchers at Hamad Bin Khalifa University in Doha, Qatar, focuses on the use of machine learning and digital signal processing to improve the analysis of electroencephalogram (EEG) signals for medical diagnosis. EEG is a safe and common test that records electrical signals from the brain and has various applications in diagnosing conditions such as epilepsy, Alzheimer's, brain tumors, sleep disorders, and stroke. The study provides a comprehensive review of recent approaches for EEG preprocessing, feature extraction, and diagnosis of brain disorders, aiming to identify reliable algorithms for improving the accuracy of automatic diagnostic systems. The research concludes that the paper can be a valuable resource for researchers and practitioners in the field, offering insights into recent developments and future research directions. [Extracted from the article]

A research paper from the Dr. Sivanthi Aditanar College of Engineering in Tiruchendur, India, explores the analysis of Bipolar Disorder (BD) using structural magnetic resonance imaging (MRI) and a supervised learning framework. The study aims to identify specific abnormalities in brain cells that indicate BD. The researchers employed a 3D texture analysis approach, extracting texture features from the MRI data and using a supervised learning algorithm called Support Vector Machine (SVM) to classify samples as healthy or abnormal. The results showed promising outcomes, with the SVM model achieving an accuracy of 90.42%. This research provides valuable insights into the application of supervised learning and MRI data analysis for understanding BD. [Extracted from the article]

A study conducted by researchers at Henan Normal University in China has proposed a new method for monitoring the training load of athletes using sensor technology and motion image analysis. The researchers combined the advantages of vision-based motion monitoring and wearable sensor-based motion monitoring to create a method that can be used for motion monitoring anytime and anywhere. They also developed a two-stage neural network motion monitoring algorithm to identify different types of motions separately, which improves the accuracy of recognition. Additionally, the study introduced a data enhancement algorithm based on acceleration data to increase the number of samples without the need for re-collecting data. The results of simulation experiments showed that this method can be applied to real-life situations. [Extracted from the article]

A recent study conducted by researchers at the ECE Department in Vijayawada, India, examined the effectiveness of machine learning techniques for the early detection of type 2 diabetes. The researchers analyzed various papers and methodologies, assessing the accuracy of these methods. They proposed a comprehensive framework that utilizes current machine learning algorithms and incorporates novel technologies to enhance the accuracy and reliability of diabetes prediction. The study emphasizes the socio-economic impact of early diabetes detection and highlights the importance of timely interventions in reducing healthcare expenses and improving patient outcomes. The research contributes to global efforts to combat diabetes and improve public health. [Extracted from the article]

A study conducted by researchers at the University of Salerno in Italy has developed a user-friendly algorithm to assist surgeons in pedicle arthrodesis operations for spine surgery. The algorithm uses 3D scans of a patient's vertebra to identify the optimal direction for screw insertion and select the best screws to maximize bone occupation without intersecting each other or going through the walls of the pedicle and vertebral body. The algorithm has been tested on six vertebrae and allows surgeons to freely choose the entry point for the screw. This research represents a novel approach in the field and provides surgeons with a tool for planning their surgeries and creating customized surgical templates. [Extracted from the article]

A research paper from the Kalasalingam Academy of Research and Education in Tamil Nadu, India, introduces a model called Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The model incorporates the Taylor series with the Tunicate Swarm Algorithm and uses the Yeo-Johnson transformation for data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder, and the fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The research concludes that the developed model is effective and efficient, with high accuracy, a low false positive rate, and a high true positive rate. [Extracted from the article]

Researchers from the University of Electronic Science and Technology of China have developed an intelligent inspection guidance system for urethral endoscopy. The system combines image processing, simultaneous localization and mapping (SLAM), and intelligent navigation technologies to assist doctors in conducting a quick and comprehensive examination. The system uses a generative adversarial network-based deblurring algorithm and a vascular attention-based feature extraction algorithm to improve accuracy in identifying urethral vascular features. The research concludes that the proposed method is more accurate and comprehensive, resulting in a 4.34% accuracy improvement. [Extracted from the article]

A recent study conducted by researchers from the Icahn School of Medicine at Mount Sinai in New York City explores the use of advanced deep learning algorithms for magnetic resonance imaging (MRI) segmentation. Accurate MRI segmentation is crucial for diagnosing abnormalities and planning treatments, but traditional supervised learning techniques require a large amount of annotated data. The study reviews state-of-the-art algorithms that utilize a limited number of annotated samples, including self-supervised learning, generative models, few-shot learning, and semi-supervised learning. The researchers also discuss future directions in the field, such as contrastive language-image pretraining, to further improve image segmentation with limited labels. [Extracted from the article]

A report from the China University of Petroleum (East China) discusses the need for modern technology, specifically artificial intelligence, to support the diagnosis of breast cancer. The existing computer-aided diagnostic systems for breast cancer are costly and limited in their capabilities. The report proposes the use of improved algorithms and a simple integrated system for pre-diagnosis of breast cancer based on CNN and Pyqt5. The system utilizes tensorflow and pytorch frameworks and includes a human-computer interaction GUI system interface. The research has been peer-reviewed and more information can be obtained from the China University of Petroleum (East China). [Extracted from the article]

Researchers from Brigham and Women's Hospital in Boston, Massachusetts have published a report on algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation (TMS). TMS is a device-based neuromodulation technique used to treat brain diseases. The report discusses modern algorithms for real-time TMS electric field modeling, including basis representation techniques and deep neural-network-based methods. It also reviews software tools that integrate electric field modeling with navigated TMS. The research was supported by the National Institutes of Health (NIH) in the United States. [Extracted from the article]

This article discusses the use of machine learning algorithms, specifically the K-Nearest Neighbors (KNN) algorithm, in predicting vaccine targets. Traditional methods for identifying suitable antigen targets for vaccines can be time-consuming and lack scalability. The authors present an implementation of the KNN algorithm for vaccine target prediction and demonstrate its effectiveness in accurately identifying vaccine targets. The study highlights the potential of the KNN algorithm in this field and provides insights for future research. Please note that this article has not been peer-reviewed. [Extracted from the article]

A study conducted at Velagapudi Ramakrishna Siddhartha Engineering College explores the use of a genetic algorithm-based decision tree to detect dementia through MRI analysis. The researchers emphasize the importance of early and accurate detection of dementia for optimal therapy and management. By analyzing a large collection of MRI images and associated clinical data, the decision tree algorithm achieved an impressive accuracy rate of 96.67%. This research contributes to the development of a non-invasive diagnostic tool for dementia detection. [Extracted from the article]

A new study conducted by researchers at Quantum University focuses on hydrocephalus, a central nervous system disorder that primarily affects infants and toddlers. The study aims to improve the accuracy of hydrocephalus diagnosis by developing an algorithm called H-detect. This algorithm utilizes image processing techniques and the CatBoost algorithm to predict hydrocephalus more effectively than traditional methods. The research concludes that the H-detect strategy outperforms conventional approaches. For more information, readers can refer to the journal article "H-Detect: an Algorithm for Early Detection of Hydrocephalus" published in Informatika i avtomatizacia. [Extracted from the article]

A study conducted by researchers at Prince Sattam bin Abdulaziz University in Saudi Arabia explores the issue of balancing generation and demand in interconnected microgrids (MGs) to address security, power quality, and reliability concerns. The study proposes an optimized fractional order load frequency control (LFC) scheme with cascaded outer and inner control loops. The proposed controller, based on a modified version of the liver cancer optimization algorithm (MLCA), achieves better disturbance rejection compared to traditional LFC methods. The study also considers the contribution of electric vehicles and the high penetration of renewables to test the stability of the proposed LFC technique. The results confirm the superior response and improved performance of the proposed controller. [Extracted from the article]

A study conducted by researchers at Guangxi University for Nationalities in Nanning, China, proposes a new method for diagnosing Parkinson's disease based on speech signals. The researchers developed a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm (BFAHA) to selectively extract disease-specific information from speech signals, improving accuracy and reducing the number of features used. The algorithm was tested on high-dimensional datasets and showed competitive performance compared to other algorithms. This research has been peer-reviewed and published in the Journal of Bionic Engineering. [Extracted from the article]

A recent study conducted at Shiraz University aimed to improve the accuracy of breast cancer diagnosis by using a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods. The researchers tested their algorithm on three common breast cancer datasets and found that it significantly improved accuracy while reducing the number of features needed for diagnosis. The study suggests that the proposed algorithm has the potential to enhance breast cancer diagnosis and reduce cost and time. Further information on the study can be found in the International Journal of Computational Intelligence Systems. [Extracted from the article]

A study conducted by researchers at Zhengzhou University of Aeronautics explores the optimization of overseas surgical warehouse locations in cross-border medical supply chain networks using parallel big data mining. The researchers propose an algorithm that takes into account factors such as cross-border transportation cost, local transportation cost, warehouse operation cost, and market value to determine the best location for overseas warehouses. By introducing the concept of time window and customer satisfaction, a multi-objective location model is established. The study concludes that the proposed algorithm effectively improves user satisfaction, optimal probability, and reduces system costs. [Extracted from the article]

Researchers at Chitkara University in Punjab, India have developed an improved lung cancer segmentation technique using nature-inspired optimization approaches. The study focused on the precise identification of tumor nodules in lung cancer Computed Tomography (CT) images. The researchers integrated four nature-inspired swarm intelligent techniques with the K-means algorithm to address the visual challenges of segmentation. The results showed that the K-means algorithm with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation compared to other optimization approaches. This research provides a valuable tool for researchers to improve the segmentation of medical image datasets for identifying the targeted region of interest. [Extracted from the article]

A research team at the University of Chicago Medical Center has developed a new risk score, called the U.S. Candidate Risk Score (US-CRS), to predict the likelihood that a patient will die without a heart transplant. The US-CRS aims to address the limitations of the current therapy-based 6-status system by incorporating objective physiological measurements, such as levels of molecules in the blood associated with organ failure. The researchers analyzed data from over 16,900 adult heart transplant candidates to determine the relationship between the US-CRS and mortality within 6 weeks of being placed on the transplant waitlist. The US-CRS model was found to be much better at predicting mortality than the current U.S. heart allocation system. However, further validation and committee approval are needed before the US-CRS can be adopted. The researchers are also working on improving heart allocation overall and designing a fair and resilient continuous distribution system for deceased donor hearts. [Extracted from the article]

Researchers at Northern Border University have conducted a study comparing machine learning algorithms for investigating complications related to myocardial infarction (heart attack). The study aimed to classify these complications and predict fatal outcomes. The algorithms tested were Multilayer Perceptron (MLP), Naive Bayes (NB), and Decision Tree (DT). The results showed that the MLP classifier performed best with the full dataset, while the DT classifier was more favorable when the dataset size was reduced using an attribute selection algorithm. This research provides valuable insights into the use of machine learning in understanding and predicting myocardial infarction complications. [Extracted from the article]

Researchers from the University of Dhaka in Bangladesh have conducted a review of machine learning and deep learning algorithms for the detection of Parkinson's disease (PD) using handwriting and voice datasets. The study highlights the potential of these techniques in improving diagnostic accuracy and aiding clinical decision-making. The research serves as a roadmap for future studies in the development of ML and DL-based tools for PD detection, with the aim of advancing understanding and improving patient outcomes. This information is valuable for the scientific community and medical practitioners interested in personalized medicine and emerging technologies. [Extracted from the article]

A recent study conducted at Harbin Engineering University in Heilongjiang, China, has proposed an advanced expert collaborative filtering recommendation algorithm based on signal fluctuation. The algorithm uses signal processing technology to filter the level of experts and introduces a kurtosis factor to determine the credibility of expert users. The research conducted experiments on multiple datasets and concluded that their method is advanced and reliable. For more information, readers can refer to the article "Expert Mining Collaborative Filtering Recommendation Algorithm Based On Signal Fluctuation" published in Computing and Informatics. [Extracted from the article]

A recent report from Universiti Malaya discusses research on cancer treatment using optimal control supported by swarm intelligence. The study aims to develop hybrid techniques that combine optimal control theory with evolutionary algorithms and multi-objective swarm algorithms to reduce cancerous cells while minimizing the use of chemotherapy medications and toxicity. Two hybrid techniques were proposed and evaluated, with MOEA/D emerging as the most effective algorithm in reducing tumor size and cancer drug concentration. The study highlights the importance of integrating optimal control theory and evolutionary algorithms for optimizing cancer chemotherapy treatment. [Extracted from the article]

A study conducted at COMSATS University Islamabad in Pakistan explores the use of advanced image reconstruction algorithms to overcome the aliasing artifacts caused by undersampling in magnetic resonance imaging (MRI). The researchers propose a compressed Singular Value Decomposition (cSVD) model to reduce the reconstruction time of highly undersampled dynamic MRI data. They also present a parallel architecture using a customized GPU to further decrease the reconstruction time. Experimental results demonstrate a significant reduction in reconstruction time without compromising image quality, making this method suitable for real-time clinical applications. [Extracted from the article]

A recent study conducted by researchers at George Emil Palade University of Medicine in Targu Mures, Romania, has developed a second trimester prediction algorithm for early-onset hypertensive disorders of pregnancy (HDP). The algorithm combines biochemical markers, such as the sFlt-1/PlGF ratio, with uterine artery Doppler ultrasound parameters to accurately predict the occurrence and severity of HDP in high-risk pregnant women. The study found that the combination of these markers improved the prediction of severe preeclampsia and its complications compared to using the markers or ultrasound parameters alone. Implementing these algorithms could enhance the accuracy of HDP screening and help manage maternal and fetal complications. [Extracted from the article]

Researchers at Fuzhou University in the People's Republic of China have developed an improved optimal foraging algorithm (OFA) for global optimization. The OFA is a swarm-based algorithm inspired by animal behavioral ecology theory. The researchers introduced a new algorithm called QOS-OFA, which incorporates quasi-opposition-based learning, an efficient cosine-based scale factor, and a search strategy with social behavior. The QOS-OFA was compared to seven other meta-heuristic algorithms and performed better on most test problems. This research has been peer-reviewed and published in the journal Computing. [Extracted from the article]

A recent report from Brunel University London explores the biases present in Human Resource (HR) algorithms and their impact on HR systems and decisions. The study highlights how biases can become entrenched in HR algorithms, which are often designed without consultation with HR specialists and assumed to operate with scientific objectivity. The researchers propose a methodology for algorithmic hygiene to address bias in HR algorithms. This research has been peer-reviewed and is published in the Human Resource Management Journal. [Extracted from the article]

A study conducted by researchers at Sun Yat-sen University in Guangdong, China, has developed a novel classification model to predict the recurrence of early-stage nasopharyngeal carcinoma after radiotherapy. The model combines unsupervised diagnostic feature learning with Adaboost ensemble learning, resulting in an accuracy of 80.33%, sensitivity of 79.55%, specificity of 80.43%, and GMean of 79.99%. The researchers believe that this model can be a valuable tool for medical professionals in predicting the likelihood of recurrence in the cervical lymph nodes following radiotherapy for early-stage nasopharyngeal carcinoma. [Extracted from the article]

Researchers at Chalmers University of Technology in Gothenburg, Sweden have developed a framework for using reinforcement learning in de novo drug design. The researchers studied various reinforcement learning algorithms and replay buffers to generate novel molecules predicted to be active against the dopamine receptor DRD2. The findings suggest that using both top-scoring and low-scoring molecules improves performance stability, and replaying molecules can enhance structural diversity and the number of active molecules generated. The research provides an open-source framework for investigating reinforcement learning methods in drug design. [Extracted from the article]

This article discusses a study that compares seven different algorithms for tracing fibre-like structures in biomedical images. The analysis of these structures can help in understanding processes like angiogenesis and the effects of certain treatments. The study found that no single algorithm outperformed the others in all metrics, but the Trace Ridges algorithm ranked first in terms of total distance error. The algorithm is fully automatic and combines various image-processing techniques to accurately delineate the fibre-like structures. The article also provides information on where to access the code for the Trace Ridges algorithm. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

Researchers from the College of Animal Sciences and Technology in Tongliao, China have developed a novel machine learning method for the early detection of brain tumors. The method combines the advantages of Gated Recurrent Unit (GRU) networks and the Enhanced Hybrid Dwarf Mongoose Optimization (EHDMO) algorithm to analyze MRI images and accurately detect brain tumors. The proposed method achieved high sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, outperforming other recent methods. This research provides a promising approach to automate and improve brain tumor detection using advanced technology. [Extracted from the article]

A study conducted by researchers at Nanjing University of Information Science and Technology (NUIST) in China proposes a feature selection algorithm called L1-CBFS for risk assessment of atherosclerotic cardiovascular disease. The algorithm was combined with four classifiers to evaluate classification performance, and the L1-CBFS-LightGBM model showed the best performance with an accuracy of 91.34%. The study used real medical visit data and pulse wave velocities measured by an ultrafast pulse wave velocity device. This research has been peer-reviewed and published in Biomedical Signal Processing and Control. [Extracted from the article]

A study conducted by researchers at Guangxi University for Nationalities in Nanning, China, has developed a new algorithm called the elite opposition-based bare bones mayfly algorithm (EOBBMA) to optimize wireless sensor networks (WSNs) coverage. WSNs are composed of sensor nodes that monitor environmental conditions and collect data. The EOBBMA algorithm aims to maximize coverage area, minimize redundant area, and minimize moving distance. Experimental results showed that the coverage rate of the sensor deployment scheme obtained by EOBBMA was over 85%, with the highest coverage reaching 99.6%. The research concluded that EOBBMA outperformed other algorithms in terms of WSNs coverage problem. [Extracted from the article]

A study conducted by researchers at Air Force Medical University in China explores the treatment algorithms for relapsing multiple sclerosis (RMS) based on the available disease-modifying therapies (DMTs) in China. The study highlights the increasing number of DMTs being prescribed in clinical practice and the need for standardized treatment algorithms based on disease activity and DMT availability. The research provides insights for establishing standardized RMS treatment algorithms in China. The study was supported by the National Natural Science Foundation of China and the Shanghai Municipal Health Commission. [Extracted from the article]

A recent study conducted at the University of Tikrit explores the impact of poisoning attacks on deep learning-based Network Intrusion Detection Systems (NIDSs). The researchers found that these attacks, which aim to distort the functionality of ML systems, can compromise the integrity of smart technology-based systems and render them susceptible to unauthorized access. The study specifically investigates the resilience of deep learning models against untethered white-box attacks by injecting altered instances into training datasets. The results indicate that the poisoning attacks significantly impact the model's performance and are difficult to detect. The findings highlight the vulnerability of DL-based NIDSs and emphasize the need for defense techniques to secure these systems against sophisticated threats. [Extracted from the article]

A report from the University of Technology in Johor, Malaysia discusses the shift from standard healthcare to personalized medicine, which is based on genetics and other biological information. The researchers highlight the use of machine learning and data mining in classifying patient cohorts and subtyping diabetes. They found that diabetes can be differentiated into subtypes based on risk complications, genetics, clinical features, and treatment selection. The study emphasizes the importance of understanding the specific objectives and methods of diabetes subtyping using clustering algorithms to improve diabetes management. This research has been peer-reviewed and published in the Artificial Intelligence Review journal. [Extracted from the article]

A recent study conducted by researchers at Manipal Academy of Higher Education in India has developed a new approach to enhance precision in medical imaging. The study focuses on the detection of fiducial markers in neuroimaging, which are crucial reference points for accurate spatial alignment during image registration. The researchers propose a model that addresses challenges in marker placement and variability during MRI scanning, ensuring reliable localization for subsequent analysis. The model utilizes a 3D Convolutional Neural Network (CNN) and achieved exceptional accuracy and sensitivity in detecting fiducial points. The study concludes that this algorithm has the potential to improve accuracy and efficiency in neuroimaging studies. [Extracted from the article]