Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms by R. Kabilan, V. Chandran, J. Yogapriya, Alagar Karthick, Priyesh P. Gandhi, V. Mohanavel, Robbi Rahim, S. Manoharan

“…The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. …”
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Raw Camera Data Object Detectors: An Optimisation for Automotive Video Processing and Transmission by Pak Hung Chan, Chuheng Wei, Anthony Huggett, Valentina Donzella

“…Whilst Deep Neural Networks (DNNs) have been developing swiftly, most of the research has been focused on videos based on RGB frames. …”
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Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model by Vishan Kumar Gupta, Avdhesh Gupta, Dinesh Kumar, Anjali Sardana

“…On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine, decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. …”
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Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model by Rajesh Doss, Jayabrabu Ramakrishnan, S. Kavitha, S. Ramkumar, G. Charlyn Pushpa Latha, Kiran Ramaswamy

“…The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. …”
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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…Our approach begins with a Time Delay Neural Network to pre-train a speaker-related feature extractor using a large-scale speaker recognition dataset while simultaneously pre-training a speaker’s emotion-related feature extractor with a speech emotion dataset. …”
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Dueling Network Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design by Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Hiroyuki Hochigai, Takayuki Kuroda, Ikuko Eguchi Yairi

“…This paper presents an improved deep reinforcement learning-based (DRL) approach for end-to-end models using a Graph Neural Network(GNN). The proposed method aims to improve end-to-end deep Q learning with a GNN by decomposing the GNN-based Q-network structure into two sub-streams to separately estimate the global state value and the state-dependent action advantage instead. …”
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Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice by Naoki Furutani, Yuki C. Saito, Yasutaka Niwa, Yu Katsuyama, Yuta Nariya, Mitsuru Kikuchi, Tetsuya Takahashi, Takeshi Sakurai

“…Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spectrum and EMG amplitude analysis. …”
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WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram, Muhammad Awais Azam

“…Then, we use three deep learning techniques, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), to classify the attacks. …”
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Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs by Hassen Louati, Ali Louati, Khalid Mansour, Elham Kariri

“…First, NAS is employed to automatically discover the optimal convolutional neural network (CNN) architecture tailored to the ChestX-Ray14 dataset, reducing the need for extensive manual tuning. …”
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Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics by Achmad Widodo, Djoeli Satrijo, Toni Prahasto, Gang-Min Lim, Byeong-Keun Choi

“…This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. …”
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A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…The ant colony optimization algorithm is then used to train a recurrent neural network. Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). …”
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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…For instance, Lasso regression, a PR method with regularization, achieves results that are 33% better and 2.1 times faster than the best artificial neural network-based solution. Our results challenge common assumptions in the literature about the complexity and feasibility of higher-order PR solutions, suggesting that they can be a compelling alternative for various challenges across domains. …”
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Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis by Jia Li, Pei Wu, Feilong Kang, Lina Zhang, Chuanzhong Xuan

“…The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…The objective is to develop an innovative deep learning (DL) model that integrates a convolutional neural network (CNN) with a gated recurrent unit (GRU) to enhance forecasting precision for day-ahead applications. …”
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Hierarchical quantitative prediction of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertainty risk by Yinming Liu, Wengang Wang, Xiangyue Meng, Yuchen Zhang, Zhuyu Chen

“…According to the calculated risk value, a double-layer photovoltaic power generation cost planning model is constructed, the upper and lower objective functions of the model are determined, and the constraint conditions are designed; Obtain a cost planning objective function solution base on a matrix task prioritization method, and generating a prioritization table; Prediction of photovoltaic power generation depreciation expense based on long-short memory neural network for each solution in the sorting table. …”
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Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media by Jiao-huang Luo, Ai-hua Yang

“…To solve the problem, we regard ADR detection as a question-answer problem and introduces an innovative neural network framework with multiple GRU layers designed for extracting ADR-related information from tweets. …”
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Social Risk Early Warning of Environmental Damage of Large-Scale Construction Projects in China Based on Network Governance and LSTM Model by Junmin Fang, Dechun Huang, Jingrong Xu

“…Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. …”
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Speech Enhancement Using Joint DNN-NMF Model Learned with Multi-Objective Frequency Differential Spectrum Loss Function by Matin Pashaian, Sanaz Seyedin

“…We propose a multi-objective joint model of non-negative matrix factorization (NMF) and deep neural network (DNN) with a new loss function for speech enhancement. …”
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Pixel-Level Recognition of Pavement Distresses Based on U-Net by Deru Li, Zhongdong Duan, Xiaoyang Hu, Dongchang Zhang

“…Secondly, the U-net model, one of the most advanced deep neural networks for image segmentation, is combined with the ResNet neural network as the basic classification network to recognize distressed areas in the images. …”
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Review on operation control of cold thermal energy storage in cooling systems by Huan Wang, Baoshan Xie, Chuanchang Li

“…Two types of cold load predictions, parametric regression and artificial neural network method, are introduced. Three aspects of economic costs are summarized in terms of initial equipment investment cost, operational cost, and life-cycle cost are summarized. …”
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