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    Researcher at PSL Research University Releases New Study Findings on Robotics (R obot Docking and Charging Techniques in Real Time Deep Learning Model)

    29-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on robotics are disc ussed in a new report. According to news reporting from Paris, France, by NewsRx journalists, research stated, "This article describes various approaches that u tilize computer vision and Lidar technology." Our news editors obtained a quote from the research from PSL Research University : "These approaches include, but not limited to, vision-based algorithms such as the Faster RCNN model and AprilTag; and single shot detectors (SSD). In carryin g out docking and recharging operations, the aforementioned approaches have show n varying degrees of success and accuracy. In order to make it easier for mobile robot systems to perform autonomous docking and recharging (ADaR) in industrial settings, this study presents a new method that employs vision and Lidar techno logy. In this study, we propose the YOLOv7 deep learning model to find charging stations. To further simplify docking with the specified wireless charging stati on, a Lidar-based approach is used to precisely modify the robot's position. An account of the assessment standards and training procedure used for the adjusted YOLOv7 model is provided in the results and discussion section."
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    National Research Council (CNR) Researchers Update Understanding of Artificial I ntelligence (GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat)

    30-31页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news originating from Firenze, Ital y, by NewsRx editors, the research stated, "Capitalizing on the widespread adopt ion of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in th e agricultural domain. This paper introduces GranoScan, a freely available mobil e app accessible on major online platforms, specifically designed for the real-t ime detection and identification of over 80 threats affecting wheat in the Medit erranean region." The news journalists obtained a quote from the research from National Research C ouncil (CNR): "Developed through a co-design methodology involving direct collab oration with Italian farmers, this participatory approach resulted in an app fea turing: (i) a graphical interface optimized for diverse in-field lighting condit ions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward o perational guide, and (v) the ability to specify an area of interest in the phot o for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an e nsembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem diseas e tasks. For weeds in the post-germination phase, the precision values range bet ween 80% and 100%, while 100% is reache d in all the classes for preflowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances , with a mean accuracy of 77% and 95% for leaf disea ses and for spike, stem and root diseases, respectively."
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    Ho Chi Minh City University of Technology Reports Findings in Machine Learning ( Machine learning-enhanced gesture recognition through impedance signal analysis)

    31-32页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Ho Chi Minh City, Viet nam, by NewsRx correspondents, research stated, "Gesture recognition is a crucia l aspect in the advancement of virtual reality, healthcare, and human-computer i nteraction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Sig nal Spectrum Analysis (ISSA) with machine learning to improve gesture recognitio n precision." Our news journalists obtained a quote from the research from the Ho Chi Minh Cit y University of Technology, "A diverse dataset that included participants from v arious demographic backgrounds (five individuals) who were each executing a rang e of predefined gestures. The predefined gestures were designed to encompass a b road spectrum of hand movements, including intricate and subtle variations, to c hallenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Baye s (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86% ; GBM, 86%; NB, 84%; LR, 89%; RF, 87% ; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition."
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    Reports on Machine Learning from Polytechnic University Milan Provide New Insigh ts (Tybox: an Automatic Design and Code Generation Toolbox for Tinyml Incrementa l On-device Learning)

    32-33页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from Milan, Italy, by NewsRx corre spondents, research stated, “Incremental on-device learning is one of the most r elevant and interesting challenges in the field of Tiny Machine Learning (TinyML ). Indeed,differently from traditional TinyML solutions where the training is t ypically carried out on the Cloud and inference only occurs on the tiny devices (e.g., embedded systems or Internet-of-Things units), incremental on-device Tiny ML allows both the inference and the training of TinyML models directly on tiny devices.”Financial support for this research came from PNRR-PE-AI FAIR project - NextGene ration EU program.
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    Second Affiliated Hospital of Soochow University Reports Findings in Machine Lea rning (A new machine learning model to predict the prognosis of cardiogenic brai n infarction)

    33-34页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Suzhou, People 's Republic of China, by NewsRx journalists, research stated, "Cardiogenic cereb ral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis or stenosis of the coronary arteries in the patient's heart, which leads to neurological deficits. To predi ct the pathogenic factors of cardiogenic cerebral infarction, this paper propose s a machine learning based analytical prediction model. 494 patients with CCI wh o were hospitalized for the first time were consecutively included in the study between January 2017 and December 2021, and followed up every three months for o ne year after hospital discharge."
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    Khon Kaen University Reports Findings in Bioinformatics (Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma us ing bioinformatics and machine learning)

    34-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biotechnology-Bioinf ormatics is the subject of a report. According to news reporting out of Khon Kae n, Thailand, by NewsRx editors, research stated, "Renal cell carcinoma (RCC), ac counting for 90% of all kidney cancer, is categorized into clear c ell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the cu rrent NCCN Guidelines. Thus, the classification will be associated with therapeu tic implications." Financial support for this research came from Khon Kaen University. Our news journalists obtained a quote from the research from Khon Kaen Universit y, "This study aims to identify novel biomarkers to differentiate ccRCC from non -ccRCC using bioinformatics and machine learning. The gene expression profiles o f ccRCC and non-ccRCC subtypes (including papillary RCC (pRCC) and chromophobe R CC (chRCC)), were obtained from TCGA. Differential expression genes (DEGs) were identified, and specific DEGs for ccRCC and non-ccRCC were explored using a Venn diagram. Gene Ontology and pathway enrichment analysis were performed using DAV ID. The top ten expressed genes in ccRCC were then selected for machine learning analysis. Feature selection was operated to identify a minimum highly effective gene set for constructing a predictive model. The expression of best-performing gene set was validated on tissue samples from RCC patients using immunohistoche mistry techniques. Subsequently, machine learning models for diagnosing RCC were developed using H-scores. There were 910, 415, and 835 genes significantly spec ific for DEGs in ccRCC, pRCC, and chRCC, respectively. Specific DEGs in ccRCC en riched in PD-1 signaling, immune system, and cytokine signaling in the immune sy stem, whereas TCA cycle and respiratory, signaling by insulin receptor, and meta bolism were enriched in chRCC. Feature selection based on Decision Tree Classifi er revealed that the model with two genes, including NDUFA4L2 and DAT, had an ac curacy of 98.89%. Supervised classification models based on H-score of NDUFA4L2, and DAT revealed that Decision Tree models showed the best perform ance with 82 % accuracy and 0.9 AUC. NDUFA4L2 expression was associ ated with lymphovascular invasion, pathologic stage and pT stage in ccRCC."
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    Study Findings from Shenyang University of Technology Advance Knowledge in Robot ics (Research and Implementation of Pneumatic Amphibious Soft Bionic Robot)

    35-36页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on robotics have been published . According to news reporting from Shenyang, People's Republic of China, by News Rx journalists, research stated, "To meet the requirements of amphibious explora tion, ocean exploration, and military reconnaissance tasks, a pneumatic amphibio us soft bionic robot was developed by taking advantage of the structural charact eristics, motion forms, and propulsion mechanisms of the sea lion fore-flippers, inchworms, Carangidae tails, and dolphin tails." Funders for this research include National Natural Science Foundation of China. The news journalists obtained a quote from the research from Shenyang University of Technology: "Using silicone rubber as the main material of the robot, combin ed with the driving mechanism of the pneumatic soft bionic actuator, and based o n the theory of mechanism design, a systematic structural design of the pneumati c amphibious soft bionic robot was carried out from the aspects of flippers, tai l, head-neck, and trunk. Then, a numerical simulation algorithm was used to anal yze the main executing mechanisms and their coordinated motion performance of th e soft bionic robot and to verify the rationality and feasibility of the robot s tructure design and motion forms. With the use of rapid prototyping technology t o complete the construction of the robot prototype body, based on the motion amp litude, frequency, and phase of the bionic prototype, the main execution mechani sms of the robot were controlled through a pneumatic system to carry out experim ental testing."
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    Findings from Edith Cowan University Provides New Data on Robotics (Self-efficac y Changes and Gender Effects On Self-efficacy In a Large-scale Robotic Telescope Focused Curriculum)

    36-36页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news reporting out of Joondalup, Australia, by NewsRx editors , research stated, "In this paper, we present the results of an investigation in to the effects of engaging with robotic telescopes during an Astronomy 101 (Astr o101) course in the United States and Canada on the self-efficacy of students." Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from Edith Cowan Univers ity, "Using an astronomy self-efficacy survey that measures both astronomy perso nal self-efficacy and instrumental selfefficacy, the authors probed their covar iance with the respondents' experience of an Astro101 course that uses robotic t elescopes to collect astronomical data. Strong effects on both selfefficacy scal es were seen over the period of a semester utilizing a scalable educational desi gn using robotic telescopes."
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    Data on Robotics Detailed by Researchers at Shanghai University (Analyzing Defor mation Factors In Six-segment Dielectric Elastomer Actuator Grippers: a Finite E lement Method-based Numerical Simulation)

    37-38页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Robotics. A ccording to news originating from Shanghai, People's Republic of China, by NewsR x correspondents, research stated, "Dielectric elastomer actuators (DEAs) are in creasingly recognized as pivotal components in flexible robotic actuators due to their substantial displacement, high energy density, rapid response times, and adaptable design characteristics. Nonetheless, the widespread adoption of DEAs i s impeded by challenges such as elevated manufacturing costs and inherent nonlin ear characteristics, which obscure a comprehensive understanding of the interpla y between dielectric elastomer (DE) related material properties and voltage stim uli governing DEA operation." Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC).
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    Studies from Hamad Bin Khalifa University Reveal New Findings on Machine Transla tion (Cross-linguistic authorship attribution and gender profiling. Machine tran slation as a method for bridging the language gap)

    38-38页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on machine translation h ave been presented. According to news reporting from Doha, Qatar, by NewsRx jour nalists, research stated, "This study explores the feasibility of cross-linguist ic authorship attribution and the author's gender identification using Machine T ranslation (MT)." Our news journalists obtained a quote from the research from Hamad Bin Khalifa U niversity: "Computational stylistics experiments were conducted on a Greek blog corpus translated into English using Google's Neural MT. A Random Forest algorit hm was employed for authorship and gender profiling, using different feature gro ups [Author's Multilevel N-gram Profiles, quantitative lingui stics (QL), and cross-lingual word embeddings (CLWE)] in both original and translated texts."
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