RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM by HUANG Pei

“…Deep learning can make the computing model that contains a number of processing layers to learn the data that contains many levels of abstract representation.This kind of learning way in the most advanced speech recognition,visual object recognition,object detection and many other areas,such as biology,genetics and medicine brought significant improvement.Deep learning can find the complex structure of large data,and the convolution neural network as one of the important models of the depth study in the processing of voice,image,video and text,and other aspects of a new breakthrough.It is the use of BP algorithm to guide the machine how to get the error before the layer to adjust the parameters of this layer,so that these parameters are more conducive to the calculation of the model.In view of the shortcomings of traditional BP algorithm,a fast BP algorithm is proposed,which has the disadvantages of slow convergence speed and often falls into local minimum points.The improved convolutional neural network is used to validate the data set MNIST,English character recognition and medical image.The simulation results show the effectiveness of the proposed algorithm.…”
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Assessing Impact of Seasonal Lighting Variation on Visual Positioning of Drones by Che-Cheng Chang, Bo-Yu Liu, Bo-Ren Chen, Po-Ting Wu

“…The global positioning system (GPS) is the most common method for drone positioning, but the GPS is not always precise or available. …”
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AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation by Jie Li, Changchun Yang

“…However, most existing models fail to distinguish the importance of different nodes, which limits their performance. …”
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Model for non-periodic time series prediction by CAO Jianwen, WEI Xingbao, YANG Yi, LI Caihong, ZHAO Wenqing

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KERATOCONUS DETECTION USING DEEP LEARNING by Younis Abbosh, Shatha Ali, Dia Ali, Iman Jasim

“…The eye is considered as one of the most complicated organs of the human body, with various ailments that can impact it. …”
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Deep learning model for early acute lymphoblastic leukemia detection using microscopic images by Vatsala Anand, Prabhnoor Bachhal, Deepika Koundal, Arvind Dhaka

“…The design of the deep optimized CNN model consisted of five convolutional blocks with thirteen convolutional layers and five max pool layers. …”
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DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

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Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms by Julius Griškevičius, Kristina Daunoravičienė, Liudvikas Petrauskas, Andrius Apšega, Vidmantas Alekna

“…The raw signals from accelerometers and gyroscopes were converted into time–frequency spectrograms. A convolutional neural network (CNN) trained solely on raw IMU-derived spectrograms achieved 71.4 % subject-wise accuracy in distinguishing frailty levels. …”
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AI-Based Architecture and Distributed Processing for the Detection and Mitigation of Spoofing Attacks in IoT Networks by William Villegas, Iván Ortiz-Garcés, Jaime Govea

“…The exponential increase in IoT devices within industrial networks has heightened their exposure to cyberattacks, with spoofing attacks posing one of the most critical threats. These attacks exploit communication vulnerabilities, enabling malicious entities to manipulate network traffic and impersonate legitimate devices, compromising system integrity and security. …”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…Additionally, a Temporal Convolutional Network is integrated within the Transformer’s encoder, employing causal convolutions and dilation to adapt to the sequential nature of data with an expanded receptive field. …”
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Identifying Bias in Deep Neural Networks Using Image Transforms by Sai Teja Erukude, Akhil Joshi, Lior Shamir

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Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images by Onur Sevli

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Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest by Gabriel R. Pardini, Paulo M. Tasinaffo, Elcio H. Shiguemori, Tahisa N. Kuck, Marcos R. O. A. Maximo, William R. Gyotoku

“…The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. …”
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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review by Wei Chen, Monisha Dhawan, Jonathan Liu, Damie Ing, Kruti Mehta, Daniel Tran, Daniel Lawrence, Max Ganhewa, Nicola Cirillo

“…Results Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). …”
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EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification by Zeshan Aslam Khan, Muhammad Waqar, Khalid Mehmood Cheema, Ali Abu Bakar Mahmood, Quratul Ain, Naveed Ishtiaq Chaudhary, Abdullah Alshehri, Sultan S. Alshamrani, Muhammad Asif Zahoor Raja

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PD Recognition for Typical Cardboard Insulation Defect with CNN by ZHAO Jing-he, XIU Da-yuan, WANG Jin-long, CHI Ming-he

“…The convolutional neural network， which is constructed and optimized， obtains the correct rate of about 96.5% and 89.9% respectively in the training set and the test set, showing that convolutional neural networks are suitable for local discharge recognition based on PRPD images.…”
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Improving YOLOv11 for marine water quality monitoring and pollution source identification by Fang Wang

“…Additionally, Multi-scale Feature Fusion (MFF) combines Convolutional Neural Networks (CNN) and Transformer-based feature extraction to enhance robustness in complex environments. …”
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METHOD FOR EVALUATING EXCAVATION SHORING DESIGN CONCEPTS by Yu. G. Zheglova, B. P. Titarenko

“…Aim.When conducting engineering and geological surveys at the conceptual stage of the feasibility study, it is necessary to create an automated system that allows the designer to take all possible factors into account and choose the most optimal design solution for the shoring of excavations.Method. …”
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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

“…Finally, we utilize a depthwise separable convolution block to extract features from power load data, which efficiently captures the pattern of change in load. …”
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Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques by Farid Amirouche, Aashik Mathew Prosper, Majd Mzeihem

“…Abstract Background In emergency departments, residents and physicians interpret X-rays to identify fractures, with distal radius fractures being the most common in children. Skilled radiologists typically ensure accurate readings in well-resourced hospitals, but rural areas often lack this expertise, leading to lower diagnostic accuracy and potential delays in treatment. …”
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