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    Reports from King Fahd University of Petroleum and Minerals Advance Knowledge in Machine Learning (Enhanced First-break Picking Using Hybrid Convolutional Neural Network and Recurrent Neural Networks)

    21-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Fresh dataon Machine Learning are presented in a new report. According to news reporting originating in Dhahran,Saudi Arabia, by NewsRx journalists, research stated, “First-break (FB) picking plays an important rolein many applications of seismic study. Different machine-learning-based methods have been proposed tosolve FB picking problem.”Financial support for this research came from College of Petroleum Engineering and Geosciences StartupGrant.
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    Recent Studies from Polytechnic University Torino Add New Data to Engineering (Experimental Demonstration of Partially Disaggregated Optical Network Control Using the Physical Layer Digital Twin)

    22-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Research findingson Engineering are discussed in a new report. According to news reporting from Turin, Italy, by NewsRxjournalists, research stated, “Optical communications and networking are fast becoming the solution tosupport ever-increasing data traffic across all segments of the network, expanding from core/metro networksto 5G/6G front-hauling. Therefore, optical networks need to evolve towards an efficient exploitation of theinfrastructure by overcoming the closed and aggregated paradigm, to enable apparatus sharing togetherwith the slicing and separation of the optical data plane from the optical control.”Funders for this research include EU Horizon Europe research and innovation program, ALLEGROProject, NextGenerationEU partnership on “Telecommunications of the Future.”
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    New Findings from Shenzhen University in the Area of Networks Described (Graph Neural Networks for Distributed Power Allocation In Wireless Networks: Aggregation Over-the-air)

    23-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Investigatorspublish new report on Networks. According to news reporting out of Guangdong, People’s Republic ofChina, by NewsRx editors, research stated, “Distributed power allocation is important for interferencelimitedwireless networks with dense transceiver pairs. In this paper, we aim to design low signalingoverhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalableto the number of wireless links.”
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    New Networks Study Findings Have Been Reported by Investigators at Heilongjiang University (Global H-synchronization of Stochastic Delayed High-order Inertial Neural Networks Subject To Markovian Jump Parameters)

    24-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Investigatorspublish new report on Networks. According to news reporting originating in Harbin, People’s Republic ofChina, by NewsRx journalists, research stated, “As a first exploration, this paper proposed the second-orderresponse system (SORS) method to study the global h-synchronization (SGhS) for high-order stochasticdelayed inertial neural networks subject to Markovian jumping parameters. Different from previous studies,this paper avoids reducing the order of the original drive system via substitution of variables, and directlygives the corresponding SORS.”
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    Study Data from University of Namur Update Knowledge of Engineering (Towards Better Transition Modeling In Recurrent Neural Networks: the Case of Sign Language Tokenization)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Current studyresults on Engineering have been published. According to news reporting from Namur, Belgium, by NewsRxjournalists, research stated, “Recurrent neural networks (RNNs) are a popular family of models widely usedwhen facing sequential data such as videos. However, RNNs make assumptions about state transitionsthat could be damageable.”Financial supporters for this research include Walloon region, Fonds de la Recherche Scientifique -FNRS, Funds InBev-Baillet Latour, F.R.S.-FNRS EOS VeriLearn.
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    Findings from Heilongjiang University in the Area of Networks Reported (Global Robust Exponential Synchronization of Interval Bam Neural Networks With Multiple Time-varying Delays)

    26-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Investigatorsdiscuss new findings in Networks. According to news reporting from Harbin, People’s Republic of China,by NewsRx journalists, research stated, “In this paper, we studied the problem of global robust exponentialsynchronization of interval bidirectional associative memory (BAM) neural networks with multiple timevaryingdelays. A direct method based on system solutions is proposed to give sufficient conditions for theglobal robust exponential synchronization of interval BAM neural networks under consideration.”Funders for this research include Natural Science Foundation of Heilongjiang Province, Natural ScienceFoundation of Heilongjiang Province, China Postdoctoral Science Foundation, Heilongjiang ProvincialPostdoctoral Science Foundation, Fundamental Research Funds in Heilongjiang Provincial Universities ofChina.
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    New Artificial Neural Networks Study Findings Have Been Reported from Port Said University (Computational Fluid Dynamics and Artificial Neural Networks for Modelling Lined Irrigation Canals With Low-density Polyethylene and Cement Concrete …)

    27-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Researchersdetail new data in Artificial Neural Networks. According to news reporting originating from Port Said,Egypt, by NewsRx correspondents, research stated, “This study numerically investigated the lining effecton the discharges and seepage losses of five reaches which belong to the El-Sont Canal, Asyut, Egypt,using FLOW-3D and Slide2 models, respectively. Two lining materials were considered, cement concrete(CC) and CC with low-density polyethylene (LDPE) film.”
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    New Information Technology Findings Has Been Reported by Investigators at Islamic Azad University (Qfs-rpl: Mobility and Energy Aware Multi Path Routing Protocol for the Internet of Mobile Things Data Transfer Infrastructures)

    28-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Fresh dataon Information Technology are presented in a new report. According to news reporting originating inOrumiyeh, Iran, by NewsRx journalists, research stated, “The Internet of Things (IoT) is a network ofvarious interconnected objects capable of collecting and exchanging data without human interaction. Theseobjects have limited processing power, storage space, memory, bandwidth and energy.”The news reporters obtained a quote from the research from Islamic Azad University, “Therefore, dueto these limitations, data transmission and routing are challenging issues where data collection and analysismethods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the bestalternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designedfor non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support,leading to unfair distribution of traffic in the network that may decrease network efficiency. This studyproposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideationfrom the Fisheye State Routing protocol. We have developed an algorithm for ease of data transferin the IoT, which provides better performance than existing protocols, especially when dealing with amobile network. To evaluate the performance of the proposed method, the Contiki operating systemand Cooja simulator have been used in scenarios with mobile and stationary nodes and random networktopologies. The results have been compared with RPL and mRPL. We have developed an algorithm forease of data transfer in the IoT, which provides better performance than existing protocols, especiallywhen dealing with a mobile network. The simulation outputs revealed that our scheme performs moreefficiently in load balancing, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E)latency, network throughput, convergence speed, control packet overhead and Remaining Useful Lifetimein designed scenarios compared to other methods.”
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    Investigators at University of Guilan Report Findings in Artificial Neural Networks (Investigating the Effect of Fundamental Properties of Materials On the Mechanisms of Thermal Cracking of Asphalt Mixtures)

    29-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Data detailedon Artificial Neural Networks have been presented. According to news reporting out of Rasht, Iran, byNewsRx editors, research stated, “In most of tests and models to evaluate the thermal cracking of asphaltmixture, the consideration of different elements within the asphalt mixture has been neglected to providea corrective solution. Accordingly, in this research, the evaluation of the mechanisms of thermal crackinginvolved assessing the impact of surface free energy (SFE) parameters and other fundamental propertiesof bitumen and aggregate.”
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    Dicle University Reports Findings in Pain and Central Nervous System (Deep Insights into MCI Diagnosis: A Comparative Deep Learning Analysis of EEG Time Series)

    30-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - New research onPain and Central Nervous System is the subject of a report. According to news reporting from Diyarbakir,Turkey, by NewsRx journalists, research stated, “Individuals in the early stages of Alzheimer’s Disease(AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phasebetween normal cognitive function and AD.”The news correspondents obtained a quote from the research from Dicle University, “Electroencephalography(EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration.To enhance the precision of dementia diagnosis, automatic and intelligent methods are requiredfor the analysis and processing of EEG signals. This paper aims to address the challenges associated withMCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuseson processing the information embedded within the sequence of raw EEG time series data. EEG recordingsare collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30mineyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and appliedto deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch areperformed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNetarchitectures are utilized for 1D time series. ResNet demonstrates superior effectiveness in detecting MCIwhen compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNetfor noisy segments. ResNet has yielded a 3% higher accuracy rate compared to CNN. None of thearchitectures in the literature have achieved 100% accuracy except proposed EEGNet and DeepConvnet.”
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