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    Findings from Xiamen University Update Understanding of Artificial Intelligence (All-in-one Multifunctional and Deformation-insensitive Carbon Nanotube Nerve Patches Enabling On-demand Interactions)

    47-48页
    查看更多>>摘要：Current study results on Artificial Intelligence have been published. According to news reporting originating from Xiamen, People’s Republic of China, by NewsRx correspondents, research stated, “Artificial intelligence of things (AIoT) aims to establish smart informative interactions between humans and devices. However, common pixelated sensing arrays in AIoT applications present problems, such as hard and brittle devices, intricate wiring, complex structures, signal transmission crosstalk, and low precision.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Fujian Province, State Key Lab of Advanced Metals and Materials, Collaborative Innovation Platform Project of Fu-Xia-Quan National Independent Innovation Demonstration Zone, Fundamental Research Funds for the Central Universities, Ministry of Education, Singapore, under its MOE ARF Tier 2. Our news editors obtained a quote from the research from Xiamen University, “Herein, we show an innovative solution called all-in-one intelligent semitransparent interactive nerve patch (AISI nerve patch), which is realized by an electrical double-layer parallel separation structure with homogeneous soft conductive materials. Multifunctionality of sensing, recognition, and transmission is integrated into the AISI nerve patch, while its total thickness can be limited to the microscale level. The AISI nerve patch possesses favorable semitransparency (transmittance of similar to 80%), which enables the interactive area not only to be accurately identified but also not to affect aesthetics. High flexibility and bending-insensitivity allow the AISI nerve patch to attach to any curved surface, making things intelligent and interactive without sacrificing interactive efficiency and stability. A rapid response time and unpixellated recognition result in the AIS interactive patch realizing near-undull and ultrahigh-precision interactive recognition.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Study Data from School of Computing Science and Engineering Update Knowledge of Machine Learning (M-multisvm: an Efficient Feature Selection Assisted Network Intrusion Detection System Using Machine Learning)

    48-49页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news originating from Vijayawada, India, by NewsRx correspondents, research stated, “The intrusions are increasing daily, so there is a huge amount of privacy violations, financial loss, illegal transferring of information, etc. Various forms of intrusion occur in networks, such as menacing networks, computer resources and network information.” Our news journalists obtained a quote from the research from the School of Computing Science and Engineering, “Each type of intrusion focuses on specified tasks, whereas the hackers may focus on stealing confidential data, industrial secrets and personal information, which is then leaked to others for illegal gains. Due to the false detection of attacks in the security and changing environmental fields, limitations like data lagging on actual attacks and sustaining financial harms occur. To resolve this, automatic abnormality detection systems are required to secure the required computing ability and to analyze the attacks. Hence, an efficient automated intrusion detection system using machine learning methodology is proposed in this research paper. Initially, the data are gathered from CSE-CIC-IDS 2018 and UNSW-NB15 datasets. The acquired data are pre-processed using Null value handling and Min-Max normalization. Null value handling is used to remove missing values and irrelevant parameters. Min-Max normalization adjusted the unnormalized data in the pre-processing stage. After pre-processing, the class imbalance problem is reduced by using the Advanced Synthetic Minority Oversampling Technique (ASmoT). ASmoT aims to balance the class and reduce imbalance class problems and overfitting issues. The next phase is feature extraction, which is performed by Modified Singular Value Decomposition (M-SvD). M-SvD extracts essential features such as basic features, content features and traffic features from the input. The extracted features are optimized by the Opposition-based Northern Goshawk Optimization algorithm (ONgO). These optimal features are able to produce optimal output. After feature selection, the different types of attacks are classified by a hybrid machine learning model called Mud Ring assisted multilayer support vector machine (M-MultiSVM) and finally, the hyperparameters are tuned by the Mud Ring optimization algorithm. Thus, the proposed M-MultiSVM model can efficiently detect intrusion in the network.”
      原文链接:
    • NETL
    • NSTL

    Findings from Lamar University Has Provided New Data on Machine Learning (Development of Machine Learning-based Models for Describing Processes In a Continuous Solar-driven Biomass Gasifier)

    49-50页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news originating from Beaumont, Texas, by NewsRx correspondents, research stated, “The synergy of two renewable and efficient sources in producing clean fuels, i.e., solar energy and biomass, can result in high efficiency. In this regard, developing syngas pro-duction systems based on solar biomass gasification has attracted much attention.” Financial support for this research came from Deputyship for Research & Innovation, Ministry of Education Saudi Arabia. Our news journalists obtained a quote from the research from Lamar University, “How-ever, experimental setups on solar-driven gasifier processes are costly and time-intensive. In such a situation, an accurate and low-cost alternative is to develop data-driven machine learning (ML) models to predict the processes involved in solar-driven biomass gasifiers. In the present study, several ML models, including random forest (RF), RANdom SAmple energy conversion efficiency for formulas, respectively, are 0.998, 0.998, 0.999, 0.999, 0.999, 0.996, and 0.998 by the elastic net.”
      原文链接:
    • NETL
    • NSTL

    Central South University of Forestry and Technology Reports Findings in Retinopathy of Prematurity (Image analysis-based machine learning for the diagnosis of retinopathy of prematurity: A metaanalysis and systematic review)

    50-51页
    查看更多>>摘要：New research on Eye Diseases and Conditions Retinopathy of Prematurity is the subject of a report. According to news reporting out of Hunan, People’s Republic of China, by NewsRx editors, research stated, “To evaluate the performance of machine learning (ML) in the diagnosis of retinopathy of prematurity (ROP) and to assess whether it can be an effective automated diagnostic tool for clinical applications. Early detection of retinopathy of prematurity (ROP) is crucial for preventing tractional retinal detachment and blindness in preterm infants, which has significant clinical relevance.” Our news journalists obtained a quote from the research from the Central South University of Forestry and Technology, “Web of Science, PubMed, Embase, IEEE, and Cochrane Library were searched for published studies on image-based ML for diagnosis of ROP or classification of clinical subtypes from inception to October 1, 2022. QUADAS-AI was used to conduct the risk of bias (Rob) research on the included original studies. The bivariate mixed effects model was used for quantitative analysis of the data, and the Deek’s test was used for calculating publication bias. Quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Twenty-two studies were included in the systematic review; four studies were at high or unclear Rob. In the area of indicator test items, only two studies were at high or unclear Rob because they did not establish predefined thresholds. In the area of reference standards, three studies had a high or unclear risk of bias. Regarding applicability, only one study was considered to have high or unclear applicability in terms of patient selection. The machine learning methods involved used deep learning in 86% of the studies. The sensitivity and specificity of image-based ML for the diagnosis of ROP 93% (95% CI:0.90-0.94), 95% (95% CI:0.94-0.97), AUC was 0.98(95% CI:0.97-0.99) and the sensitivity, specificity was 93% (95% CI:0.89-0.96), 93% (95% CI:0.89-0.95), AUC was 0.97(95% CI:0.96-0.98) for the classification of clinical subtypes of ROP. The classification results were highly similar to those of clinical experts (Spearman’s R=0.879). Machine learning algorithms is no less accurate than the human expert and hold considerable potential as automated diagnostic tools for ROP.”
      原文链接:
    • NETL
    • NSTL

    New Findings on Machine Learning from National University of Defense Technology Summarized (Dual-parametric Simultaneous Demodulation of Fiber Optic Seawater Temperature and Pressure Sensors Based On Machine Learning Methods)

    51-52页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “The pressure and temperature of seawater are two important parameters. At present, people mainly rely on various types of temperature and depth measurement instruments to monitor the temperature and pressure of seawater.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Interdisciplinary Scientific Research Foundation of Guangxi University, National Natural Science Foundation of Guangxi Province, Guangxi Major Projects of Science and Technology, Guangxi Key Projects of Science and Technology. Our news editors obtained a quote from the research from the National University of Defense Technology, “Optical temperature-depth (TD) sensors have advantages such as antielectromagnetic interference, corrosion resistance, and multiplexing capability. By using polydimethylsiloxane with a high thermo-optical coefficient and a high elastic-optical coefficient to encapsulate optical microfiber coupler combined sagnac loop (OMCSL) structure with large abrupt field characteristic, a simultaneous temperature and pressure fiber optic sensor with high stability and high sensitivity could be achieved. However, when using the conventional sensitivity matrix method (SMM) to demodulate the sensor, the demodulation results were unstable and encountered large error. One of the main reasons for the errors in the demodulation of the sensor using SMM is that the sensitivity matrix is an ill-conditioned matrix under certain conditions, and SMM in this state would greatly amplify the errors in the demodulation results. The other reason is that the feature wavelengths of the sensor would show a nonlinear relationship with temperature when sensing in some environments. To reduce the demodulation error, in this article, we researched and used various machine learning methods (MLMs) to demodulate the sensor. The experimental results showed that the demodulation error of the sensor could be greatly reduced by using the MLM compared to the traditional SMM.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Findings in Artificial Intelligence Described from Madhav Institute of Science & Technology (Intelligent Fault Diagnosis for Aitbased Smart Farming Applications)

    52-53页
    查看更多>>摘要：Research findings on Artificial Intelligence are discussed in a new report. According to news originating from Gwalior, India, by NewsRx correspondents, research stated, “In today’s era, the artificial intelligence of things (AIT) has revolutionized every sphere of human life. Artificial intelligence (AI) approaches can effectively manage the heterogeneity of IoT devices.” Our news journalists obtained a quote from the research from the Madhav Institute of Science & Technology, “The applications of AIT are seen in various verticals, including smart homes/offices, smart factories, Industrial IoT, precision agriculture, etc. In particular, AIT has been a huge asset to the farming sector. Smart farm monitoring requires a huge number of sensors deployed in the monitoring field that measure various physical parameters such as humidity, soil moisture, temperature, etc. However, the main challenge is to handle the vulnerability of sensor node failure due to any natural calamity. This leads to the malfunctioning of sensor modules, which degrades the performance of the network. Also, sensor nodes are battery-limited. It is difficult to recharge and replace the sensors frequently. This article proposes an AI-based hyperparameter-tuned least square support vector machine (HT-LS-SVM) for fault diagnosis that detects faults with high accuracy. Also, it proposes mobile sink that applies reinforcement learning (RL)-based algorithm to perform obstacle-aware path planning. This algorithm adds self-learning capability to the mobile sink, which makes the system autonomous and improves network lifetime significantly.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Researchers at University of Salerno Publish New Study Findings on Machine Learning (Advancements and novel approaches in modified AutoDock Vina algorithms for enhanced molecular docking)

    53-53页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting out of Fisciano, Italy, by NewsRx editors, research stated, “Molecular docking plays a crucial role in modern drug discovery by facilitating the prediction of interactions between small molecules and biomolecular targets.” Financial supporters for this research include Ministero Dell’universita E Della Ricerca; Ministero Dell’istruzione Dell’universita E Della Ricerca. The news journalists obtained a quote from the research from University of Salerno: “AutoDock Vina (Vina) has earned its reputation as a leading software thanks to its effective energy-based scoring system and user-friendly interface. However, the growing demands of computational biology have prompted investigations into improvements for Vina, leading to a range of algorithmic enhancements. This systematic review explores the recent developments achieved by Vina for molecular docking. These modifications include hybrid parallelization methods utilizing high-performance computing and innovative scoring functions integrated with machine learning.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Robotics and Automation Findings from Ira A. Fulton School of Engineering Reported (Design and Validation of Soft Sliding Structure With Adjustable Stiffness for Ankle Sprain Prevention)

    54-54页
    查看更多>>摘要：A new study on Robotics - Robotics and Automation is now available. According to news reporting originating from Tempe, Arizona, by NewsRx correspondents, research stated, “This study presents the design and validation of a soft sliding stiffness structure with a soft-rigid layer sliding mechanism. It aims to mitigate ankle sprains and address the progression of chronic ankle instability by providing stiffness support.” Financial support for this research came from National Research Foundation of Korea. Our news editors obtained a quote from the research from the Ira A. Fulton School of Engineering, “The soft-rigid layer sliding mechanism of the structure is designed to achieve a wide range of stiffness while maintaining a compact form factor. The structure incorporates rigid retainer pieces within each layer, which allows for sliding within a hollow cuboid structure and enables modulation of stiffness. An analytical model is presented to investigate the variations in stiffness resulting from the different sliding states. The stiffness characteristics of the structure were validated through both bench tests and human subject tests. The gradual sliding of the structure’s layer resulted in an increase in stiffness, aligning with the analytical model’s predictions. At the most rigid stage (0% alignment), the stiffness exhibited a significant increase of 111.1% compared to the most flexible stage (100% alignment). Additionally, the human subject testing demonstrated a stiffness increase of up to 93.8%.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Data on Arthroplasty Reported by P. S. Ashok Kumar and Colleagues (Does robotic-assisted unicompartmental knee arthroplasty restore native joint line more accurately than with conventional instruments?)

    55-55页
    查看更多>>摘要：New research on Surgery Arthroplasty is the subject of a report. According to news reporting from Tamil Nadu, India, by NewsRx journalists, research stated, “The study’s primary aim is the restoration of native joint line in patients having robotic-assisted unicondylar knee arthroplasty and conventional unicondylar knee arthroplasty. Literature in the past has demonstrated that reducing the joint line can result in greater failure rates.” The news correspondents obtained a quote from the research, “This is a prospective cohort investigation of patients who had medial UKA between March 2017 and March 2022.All patient’s pre-operative and post-operative radiological joint line assessments were examined by two observers by Weber’s methods. Robotic-assisted UKA performed with hand-held image-free robots was compared to conventional UKA groups. The distal position of the femoral component was higher in Group B utilizing conventional tools than in Group A employing robotic-assisted UKA. This positional difference was statistically significant. The mean difference among the pre-operative and post-operative joint lines in Group A was 1.6 ± 0.49 (range 0.8 mm-2.4 mm), while it was 2.47 ± 0.51 (range 1.6 mm-3.9 mm) (p 0.005) in Group B. In Group A, a greater percentage of the subjects (64%) attained a femoral component position within two millimeters from the joint line, whereas just 18% in Group B did. When compared with the conventional UKA technique, the meticulous attention to detail and planning for ligament rebalancing when using the robotic-assisted UKA technique not solely enhance surgical precision for implant placing but additionally provides excellent native joint line restoration and balancing.”
      原文链接:
    • NETL
    • NSTL

    Study Results from University of Jeddah in the Area of Machine Learning Published (Machine learning approach to optimal task scheduling in cloud communication)

    56-56页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting originating from Jeddah, Saudi Arabia, by NewsRx correspondents, research stated, “Cloud communication is a combination of distributed computing and parallel computing.” Our news journalists obtained a quote from the research from University of Jeddah: “One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial completeness (NP) of cloud systems. To solve this problem, various approximation techniques based on swarm intelligence have been developed. This study proposes a dual machine learning strategy using kmeans to optimize performance and aid in selecting cloud scheduling technologies. The first technique is called Efficient Kmeans (Ekmeans) and the second technique is called Kmeans HEFT (KmeanH), where HEFT stands for Heterogeneous Earliest End Time. Our main contribution is to reduce processing time and increase speed and efficiency for a given set of tasks.” According to the news reporters, the research concluded: “We evaluate the impact of both algorithms on different virtual machines (ranging from 2 to 32) and task sizes (ranging from 50 to 1000).”
      原文链接:
    • NETL
    • NSTL
    • Elsevier