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A recent report from Hangzhou Dianzi University in China discusses a new algorithm called Beetle Swarm with Constrained Levy Flight (BSL) for image matching in industrial production. The algorithm combines swarm intelligence with the constrained Levy flight and improves the convergence speed and accuracy of image matching. In practical application tests, BSL outperformed other commonly used meta-heuristic algorithms, showing stable performance and low time cost of convergence. The algorithm has been found to significantly improve the speed and accuracy of image matching in automated optical inspection (AOI) in production. [Extracted from the article]

A recent study from Tianjin University in China discusses the progress made in developing methods for predicting epileptic seizures based on EEG data. The researchers highlight the importance of early prediction and intervention in improving the quality of life for epilepsy patients. The study reviews commonly used databases, methods for pre-processing and analyzing EEG data, and limitations of current algorithms. The researchers also compare deep learning algorithms with traditional machine learning methods and suggest that these new technologies may lead to significant breakthroughs in seizure prediction. This research has been peer-reviewed and provides valuable insights for further study in this field. [Extracted from the article]

Researchers from Monash University Malaysia have proposed a new model called the rectified complex spherical fuzzy set (rCTSFS) for the detection of diabetic retinopathy. The model utilizes rectified complex t-spherical fuzzy Dombi-Choquet integral operators and a multi-criteria decision-making algorithm. The researchers found that their proposed algorithm was the only one that produced correct diagnostic results, outperforming 50 other algorithms tested. This research provides a potential advancement in the detection of diabetic retinopathy through the analysis of fundus images. [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 report from Changchun Normal University in Jilin, China discusses a study on the optimization of artemisinin, a medication used in malaria therapy. The study proposes an algorithm called the Artemisinin Optimization (AO) algorithm, which draws inspiration from the process of artemisinin therapy for malaria. The algorithm is tested on benchmark function sets and breast cancer pathology image segmentation applications, demonstrating its efficiency and potential in real-world optimization applications. The research concludes by providing access to the source codes for further exploration. [Extracted from the article]

A research report from Soochow University in Suzhou, China, introduces a new algorithm called Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MASDSC) for brain tumor clustering in medical imaging. The algorithm addresses the limitations of traditional unsupervised deep subspace clustering algorithms by incorporating a semi-supervised learning framework and a multi-scale feature extraction mechanism. It also employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters. The MAS-DSC algorithm achieves excellent clustering results on standard datasets and demonstrates robustness and enhanced clustering performance on a brain tumor dataset. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]

A recent study conducted by researchers at the South China University of Technology has developed a new algorithm called DCDLN (Densely Connected Convolutional Dynamic Learning Network) for diagnosing malaria. The algorithm uses artificial intelligence to analyze cells and achieved a diagnostic accuracy rate of 97.23% on a malaria cell dataset, outperforming existing methods. The researchers also tested the algorithm on skin cancer and garbage classification datasets, demonstrating its generalization capabilities. This research provides promising evidence for clinicians to accurately diagnose malaria and potentially other diseases. [Extracted from the article]

A report from Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia discusses the development of an improved Kepler Optimization Algorithm (I-KOA) for feature selection in liver disease classification. Liver diseases are a significant healthcare challenge globally, and accurate diagnosis is complex. The I-KOA algorithm combines Opposition-Based Learning and a Local Escaping Operator to achieve robust feature selection and high classification accuracy. The algorithm outperforms seven rival optimization algorithms, achieving an overall classification accuracy of 93.46% across multiple liver disease datasets. The researchers believe that I-KOA has the potential to revolutionize medical diagnosis and decision support systems. [Extracted from the article]

A recent report from the Saveetha School of Engineering in Chennai, India, discusses the development of a deep learning framework using a convolution neural network algorithm for breast cancer detection and classification. Breast cancer is a significant health concern globally, affecting both men and women. The proposed framework aims to improve the efficiency and accuracy of breast cancer diagnosis by automatically analyzing breast images. The research concludes that the deep learning framework, specifically the convolution neural network, is highly reliable compared to existing algorithms. This research has been peer-reviewed and provides valuable insights into the field of cancer detection. [Extracted from the article]

A recent report from the Indian Institute of Technology (IIT) Kharagpur discusses the need for an optimal multimodal multi-echelon vaccine distribution network design for low and medium-income countries (LMICs) with manufacturing infrastructure during healthcare emergencies. The researchers propose a cold chain network that integrates vaccine manufacturers and incorporates multiple transportation modes. They also introduce a novel algorithm to solve large-scale problems and validate it using data from India. The results show that the proposed model saves cumulative transportation time compared to the actual vaccine supply network in India during healthcare emergencies. [Extracted from the article]

A study conducted by researchers at Beijing Jiaotong University in China has developed a new algorithm called EnergySaving Maximum Entropy Deep Reinforcement Learning (ES-MEDRL) to optimize the energy consumption of urban rail transit systems. The algorithm incorporates Lagrange multipliers and maximum policy entropy to minimize train traction energy consumption while balancing ride comfort, punctuality, and safety. Numerical experiments conducted on the Beijing Metro's Yizhuang Line showed a minimum 20% increase in energy efficiency compared to manual driving. The algorithm has the potential to guide energy-efficient train operations at the planning level. [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]

Bones of mice with osteoporosis that received the treatment were similar to those of the control group, and the treatment showed no evidence of toxicity. Keywords: Algorithms; American Chemical Society; Antiinfectives; Antimalarial Agents; Antiprotozoal Agents; Asia; Bone Research; Chemistry; China; Drugs and Therapies; Genetics; Health and Medicine; Metabolic Bone Diseases and Conditions; Mosquito-Borne Diseases; Musculoskeletal Diseases and Conditions; Osteoporosis EN Algorithms American Chemical Society Antiinfectives Antimalarial Agents Antiprotozoal Agents Asia Bone Research Chemistry China Drugs and Therapies Genetics Health and Medicine Metabolic Bone Diseases and Conditions Mosquito-Borne Diseases Musculoskeletal Diseases and Conditions Osteoporosis 357 357 1 10/30/23 20231103 NES 231103 2023 NOV 3 (NewsRx) -- By a News Reporter-Staff News Editor at Zika & Mosquito Week -- Correction (): The paper's title has been corrected to "Deep Learning-Predicted Dihydroartemisinin Rescues Osteoporosis by Maintaining Mesenchymal Stem Cell Stemness through Activating Histone 3 Lys 9 Acetylation Artificial intelligence has exploded in popularity and is being harnessed by some scientists to predict which molecules could treat illnesses, or to quickly screen existing medicines for new applications. [Extracted from the article]

To this end, this paper proposes an agent-based algorithm for the protection for user privacy health topics based on identity replacement." Keywords: Zhejiang; People's Republic of China; Asia; Engineering; Algorithms; Cybersecurity; Health and Medicine; Public Health EN Zhejiang People's Republic of China Asia Engineering Algorithms Cybersecurity Health and Medicine Public Health 8478 8478 1 10/30/23 20231103 NES 231103 2023 NOV 3 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New research on Engineering is the subject of a report. [Extracted from the article]

Jammu Kashmir, India, Asia, Algorithms, Cancer, Cervical Cancer, Diagnostics and Screening, Health and Medicine, Oncology, Women's Health Keywords: Jammu Kashmir; India; Asia; Algorithms; Cancer; Cervical Cancer; Diagnostics and Screening; Health and Medicine; Oncology; Women's Health EN Jammu Kashmir India Asia Algorithms Cancer Cervical Cancer Diagnostics and Screening Health and Medicine Oncology Women's Health 1014 1014 1 10/03/23 20231006 NES 231006 2023 OCT 3 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- A new study on Oncology - Cervical Cancer is now available. [Extracted from the article]

A new report from Shaanxi Normal University in Xi'an, China, presents research on an effective metaheuristic algorithm called the Inherited Optimization Algorithm (IOA). Inspired by the cognitive tendencies and adaptive feedback behavior of people, the IOA algorithm aims to solve complex optimization problems in engineering applications. The algorithm's performance was evaluated on various benchmarks and compared to other metaheuristics, demonstrating its computational efficiency and search efficiency. The research was funded by the National Natural Science Foundation of China and the Fundamental Research Funds for the Central Universities. [Extracted from the article]

A recent study conducted at COMSATS University in Islamabad, Pakistan, has focused on the importance of selecting the right medical device manufacturer in healthcare systems. The researchers developed a decision-making algorithm based on fuzzy theory to assist in choosing the most suitable worldwide medical device manufacturer. They utilized complex q-rung orthopair fuzzy Yager aggregation operators and tested their efficacy in multi-criteria decision-making. The results demonstrated the adaptability and efficiency of the proposed algorithm and aggregation operators. The research has been peer-reviewed and confirmed to produce precise and accurate outcomes. [Extracted from the article]

A study conducted by researchers at China University of Mining and Technology explores the challenges of transporting medical waste generated by medical institutions (MIs) to disposal centers (DCs). The researchers propose a bi-level optimization model that aims to minimize costs and transportation risks associated with medical waste transportation. They use a simulated annealing algorithm and an improved genetic algorithm to address the optimal location problem and the capacity-constrained vehicle routing problem, respectively. The study emphasizes the importance of travel time reliability and loading reliability in designing an efficient medical waste recycling network. The researchers found that their modified genetic algorithm exhibited superior performance compared to other methods. [Extracted from the article]

A recent study conducted in Dongguan, People's Republic of China, focused on the application of sequential databases in various fields such as market basket analysis, medical prediction, and sign language recognition. The study highlighted the limitations of previous research that assumed each event occurred instantaneously and proposed the integration of duration with temporal patterns in interval-based sequential databases. The researchers developed two algorithms, FTDPMiner-EP and FTDPMiner-TM, to mine frequent temporal duration-based patterns (TDPs). The experiments conducted on real and synthetic databases demonstrated the good performance of the algorithms. This research has been peer-reviewed and provides valuable insights into the field of mathematics. [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 study conducted by researchers at Jamia Hamdard in New Delhi, India, explores the use of machine learning algorithms for diabetes detection. The study focuses on supervised machine learning techniques and tests the efficiency of various classification algorithms using a publicly available dataset. The Random Forest algorithm was found to outperform the other algorithms, achieving an accuracy of 79.52%. The research concludes that further iterations of the algorithm resulted in correct predictions for a significant number of test data. For more information, readers can refer to the journal article "The Diabacare Cloud: Predicting Diabetes Using Machine Learning" published in Acta Scientiarum. Technology. [Extracted from the article]

A recent report from Shanghai Jiao Tong University in China discusses the use of soft robots in cardiac surgery. Soft robots are known for their ability to interact safely with organs and have lower risks of damage compared to rigid robots. The researchers propose an adaptive iterative learning controller to track the irregular motion of the beating heart during surgery. The algorithm has been proven to improve control precision and stability compared to traditional controllers. The research provides valuable insights into the potential of soft robots in surgical applications. [Extracted from the article]

A report from Yangtze University in Jingzhou, China discusses the use of deep learning algorithms in food safety and authenticity detection. The researchers highlight the advantages of deep learning algorithms over traditional machine learning algorithms and chemometric algorithms. They specifically focus on convolutional neural networks (CNN), fully convolutional networks (FCN), and generative adversarial networks (GAN). The report also explores the potential for further development in this field, including the use of data augmentation and multimodal neural networks. The research concludes that deep learning algorithms have made significant progress in food safety and authenticity detection, but there is still room for improvement. [Extracted from the article]

A recent study conducted at Guangxi University in Nanning, China, has developed a new quantum algorithm called BG-VQE that accurately simulates chemical molecule properties. The algorithm reduces the complexity of theoretical simulation for drug molecule synthesis, making it easier to find precise solutions. The researchers used bridge-inspired quantum circuits and a gradient filter module to design the algorithm, which demonstrated its effectiveness and superiority in calculating ground state energies for various molecules. The research was supported by the National Natural Science Foundation of China and won the championship in the Quantum Biochemical Engineering track of the 2nd CCF Origin Pilot Cup Quantum Computing Challenge. [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]

A recent study conducted at Xi'an University of Science and Technology in Xi'an, People's Republic of China, focused on the accurate recognition of micro-expressions in demanding scenarios such as clinical psychotherapy and criminal interrogation. The study proposed a novel architecture based on a multi-scale 3D residual convolutional neural network, which effectively captured weak and fleeting facial features and improved recognition performance. The algorithm's performance was evaluated using public datasets, and it achieved recognition accuracies of 74.6%, 84.77%, and 91.35% on these datasets. The study concluded that the proposed algorithm could serve as an important reference for researchers working on high-precision micro-expression recognition. [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 the Central University of Haryana in India has proposed a privacy-preserving collaborative model for the classification of brain tumors using magnetic resonance imaging (MRI) images. The study utilized deep learning and transfer learning algorithms to analyze an open-source dataset of MRI images, which included four types of tumors and no tumor. The researchers found that federated deep learning models, involving multiple clients, outperformed conventional pretrained models in terms of accuracy. The proposed framework offers a solution for early diagnosis of brain tumors while ensuring data privacy for edge devices with limited resources. [Extracted from the article]

According to the news editors, the research concluded: "The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image." To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm.". [Extracted from the article]

The article presents a study conducted at APJ Abdul Kalam Technological University focusing on a new approach for liver tumor segmentation and classification. Topics discussed include the application of a deep learning model with a hybrid optimization technique, the use of advanced image processing methods, and the improvements in accuracy, specificity, sensitivity, and precision achieved with the proposed model.

A study conducted by Chongqing Medical University in China has developed a new algorithm for detecting hip dysplasia. The algorithm, called Cba-yolov5s, is based on the YOLOv5s model and incorporates angle consistency and bi-level routing attention. By expanding the local neighborhood size and filtering out irrelevant regions, the algorithm improves the accuracy of hip joint keypoint detection and positioning. The algorithm was tested on a clinical dataset and outperformed existing algorithms in terms of error reduction. The researchers believe that this algorithm could provide a possibility for automatic diagnosis of hip dysplasia. [Extracted from the article]

A recent study conducted by researchers at Wenzhou University in China focuses on improving the accuracy of image segmentation for breast cancer diagnosis. The study proposes a refined whale optimization algorithm that combines a quantum phase interference mechanism and an enhanced solution quality strategy. The algorithm is compared to other state-of-the-art algorithms and is tested on benchmark datasets to validate its performance. The results demonstrate the effectiveness of the segmentation method in both low and high threshold levels, making it a valuable technique for practical applications. The study has been peer-reviewed and provides potential avenues for further research in breast cancer diagnosis. [Extracted from the article]

Researchers at Dalian Ocean University in Dalian, China have developed a new machine learning-based artificial intelligence method for predicting air pollution levels. The method, called the Hybrid Integration (HIG) algorithm, combines data pre-processing, time series decomposition, signal decomposition, a prediction module, a matching strategy, and a hybrid integrated optimization algorithm. The researchers found that the HIG-RNN and HIG-LSTM models outperformed traditional methods in terms of accuracy. This new method could be a valuable tool for managing air pollution and informing policy decisions. [Extracted from the article]

Researchers at Wenzhou University in China have developed an advanced optimization algorithm called Biogeography-based Learning Rime Optimization Algorithm (BLRIME) to improve the image segmentation of lupus nephritis (LN). LN is the most common symptom of systemic lupus erythematosus, and the demand for efficient image segmentation techniques has grown with its increasing prevalence. The BLRIME algorithm integrates multi-threshold image segmentation with metaheuristic algorithms to enhance the efficiency of LN image segmentation. Experimental studies have shown that BLRIME demonstrates superior global search capabilities and produces remarkable outcomes in the segmentation of LN images. [Extracted from the article]

Researchers from Hangzhou Medical College in China have conducted a study exploring the use of deep learning technology in identifying and locating spinal cord injuries (SCI) based on MRI imaging. The study utilized the Faster R-CNN algorithm combined with convolutional neural network (CNN) models to recognize and locate SCI in MRI images. The trained network achieved a model mean Average Precision (mAP) of 88.6% and an image test speed of 0.22s per image. This research provides valuable insights into the potential application of deep learning technology in the analysis of spinal cord injuries. [Extracted from the article]

A study conducted in Beijing, China, focused on brain cancer, specifically gliomas, which have the highest mortality rate among brain tumors. The researchers proposed a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net) to improve the classification of glioma risk. The network framework consists of three parallel layers: global feature extraction, local feature extraction, and multi-scale feature fusion. The results of the study showed that HMAC-Net performed the best in both qualitative and quantitative analysis. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]

A recent study conducted at Mohamed Sathak Engineering College in Tamil Nadu, India, focused on the detection and classification of brain tumors using magnetic resonance imaging (MRI). The researchers proposed an algorithm called Adaptive Convolutional Autoencoder-based Snow Avalanches (ACAE-SA) to automate the detection and categorization of brain tumors from MRI images. The algorithm was found to be effective and outperformed existing techniques. This research has been peer-reviewed and published in the journal Signal Image and Video Processing. For more information, readers can contact the researchers at Mohamed Sathak Engineering College. [Extracted from the article]

Researchers from Southeast University in Nanjing, China have developed a constrained maximum power point tracking (MPPT) strategy for wave energy converters (WECs) using magnetic lead screws (MLSs). MLSs have been found to be efficient and powerful in sustainable wave energy conversion. The study analyzed the mechanism of MLS and established a dynamic model for MLS-based WECs. An adaptive constrained genetic algorithm (ACGA) was used to achieve MPPT under constraints, and simulations and experiments were conducted to verify the effectiveness of the strategy. The proposed ACGA improved the energy harvest rate by 3.95% to 6% compared to the standard constrained genetic algorithm (SCGA). [Extracted from the article]

A recent study from Guangzhou University in China has proposed a new algorithm for telemedicine applications that aims to protect the privacy of electronic patient information. The algorithm combines reversible data hiding (RDH) with secure multi-party computation to embed secret data into image blocks. The experimental results show that the proposed algorithm has a higher embedding rate compared to other algorithms and exhibits robustness against existing attacks. This research has been peer-reviewed and provides valuable insights into privacy protection in telemedicine. [Extracted from the article]

Researchers from Nanjing University of Science and Technology in China have developed algorithms to attack federated learning (FL), a type of distributed machine learning. The algorithms, known as covert model poisoning (CMP), are designed to manipulate the system outputs by crafting uploaded model parameters. The researchers found that the CMP algorithms were effective and outperformed existing attack mechanisms. The study was supported by the National Natural Science Foundation of China. [Extracted from the article]