Software pipeline for predicting and analyzing the structure of the receptor–ligand by A.S. Kozlova, A.R. Mukhametgalieva, A.N. Fattakhova, N.I. Akberova

“…Possible interactions of the receptor–ligand complex are studied based on certain parameters: the energy of the affinity of the ligand for the receptor; the length and energy of the bond between the receptor and the ligand, both in the whole complex and between individual atoms. All characteristics can be automatically calculated by default under the specified optimal parameters. …”
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Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD: a framework for automatic diagnosis by Ying Mao, Ying Mao, Xuchen Qi, Xuchen Qi, Xuchen Qi, Lingyan He, Shan Wang, Zhaowei Wang, Fang Wang, Fang Wang

“…Then, four widely-employed machine learning algorithms (including random forest (RF), XGBoost, CatBoost, and LightGBM) were used for classification calculations, and the SHAP algorithm was then used to assess the importance of the contributing features to interpret the model’s decision process.ResultsThe results showed that the highest classification accuracy of 99.58% for pediatric ADHD detection was obtained with the CatBoost model based on the optimal feature subset of 206 features (PSD/FuzEn/MI = 53/5/148). …”
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Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia by Kriengsak Yothapakdee, Yosawaj Pugtao, Sarawoot Charoenkhun, Tanunchai Boonnuk, Kreangsak Tamee

“…The neural network algorithm achieved an accuracy rate of 87.00% across different image sizes. …”
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Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling by Lukas Schynol, Marius Pesavento

“…To optimize the deep network weights for detection performance, we employ a homotopy optimization approach based on an efficient approximation of the area under the receiver operating characteristic curve. …”
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Fault reconfiguration control strategy of islanded marine ranching power supply system based on deep reinforcement learning by Yichun Wang, Bo Zhang, Rongjie Wang, Jiang Desong, Yabo Cui, Zhouyang Sun, Hao Liu

“…Finally, through analysis of fault reconstruction cases in different operating conditions of the marine ranching power system, it is demonstrated that this proposed algorithm can provide optimal reconstruction strategies within as short as 70 ms while achieving objectives such as maximum load recovery, minimum switching times, and minimum network losses. …”
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Development of Adaptive Testing Method Based on Neurotechnologies by E. V. Chumakova, D. G. Korneev, M. S. Gasparian

“…SGD, Adam, NAdam and RMSprop implemented in Keras were compared as optimizers to achieve faster convergence. Adam showed the best results in terms of accuracy, while the MSE loss function (mean square error) was used together with the optimizer. …”
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IMPROVING EFFICIENCY OF RADIO MAINTENANCE TO ENSURE SAFETY by V. E. Emelyanov, I. A. Polotnyanschicov

“…The Algorithm of solving the task of optimal control is given.…”
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MPDP -medoids: Multiple partition differential privacy preserving -medoids clustering for data publishing in the Internet of Medical Things by Zekun Zhang, Tongtong Wu, Xiaoting Sun, Jiguo Yu

“…Based on the traditional k -medoids clustering, multiple partition differential privacy k -medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user’s privacy information. …”
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Design of artwork resource management system based on block classification coding and bit plane rearrangement by Xiaomeng Xia

“…By employing refined block classification coding (RS-BCC) and optimized bit plane rearrangement (BPR) techniques, this algorithm significantly enhances the watermark embedding capacity and robustness while ensuring image quality. …”
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Low latency Montgomery multiplier for cryptographic applications by khalid javeed, Muhammad Huzaifa, Safiullah Khan, Atif Raza Jafri

“…The proposed Montgomery multiplier is based on school-book multiplier, Karatsuba-Ofman algorithm and fast adders techniques. The Karatsuba-Ofman algorithm and school-book multiplier recommends cutting down the operands into smaller chunks while adders facilitate fast addition for large size operands. …”
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Sum Rate Maximization for Active RIS MISO Systems Based on DRL by Zhipeng Xi, Jianbo Ji

“…Additionally, it is observed that proper parameter settings significantly enhance the performance of the proposed algorithm. Finally, the algorithm can allocate suitable power to the active RIS while maintaining a constant total power, thereby optimizing the system performance.…”
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Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. …”
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UAV-Assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis by Ruslan Zhagypar, Nour Kouzayha, Hesham ElSawy, Hayssam Dahrouj, Tareq Y. Al-Naffouri

“…Additionally, the algorithm facilitates optimization of system parameters such as UAV count, altitude, battery capacity, etc. …”
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Area-Time-Efficient Secure Comb Scalar Multiplication Architecture Based on Recoding by Zhantao Zhang, Weijiang Wang, Jingqi Zhang, Xiang He, Mingzhi Ma, Shiwei Ren, Hua Dang

“…The interleaved modular multiplication algorithm and modified binary inverse algorithm are used to achieve short clock cycle delay and high frequency while taking into account the need for a low area. …”
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Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data by Lixiran Yu, Hongfei Tao, Qiao Li, Hong Xie, Yan Xu, Aihemaiti Mahemujiang, Youwei Jiang

“…The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones.…”
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Using Graph Neural Networks in Reinforcement Learning With Application to Monte Carlo Simulations in Power System Reliability Analysis by Oystein Rognes Solheim, Boye Annfelt Hoverstad, Magnus Korpas

“…Recent efforts from the authors indicate that optimal power flow solvers could potentially be replaced with the policies of deep reinforcement learning agents, to obtain significant speedups of Monte Carlo simulations while retaining close to optimal accuracies. …”
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Control strategy of robotic manipulator based on multi-task reinforcement learning by Tao Wang, Ziming Ruan, Yuyan Wang, Chong Chen

“…To tackle this issue, instead of uniform parameter sharing, we propose an adjudicate reconfiguration network model, which we integrate into the Soft Actor-Critic (SAC) algorithm to address the optimization problems brought about by parameter sharing in multi-task reinforcement learning algorithms. …”
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A Hybrid Deep Learning Model for UAV Path Planning in Dynamic Environments by Junchi Zhang, Yanning Xian, Xun Zhu, Hongtao Deng

“…While its variants can converge to the optimal solution, they suffer from more memory and computation consumption. …”
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Interference-Aware AAV-TBS Coordinated NOMA: Joint User Scheduling, Power Allocation and Trajectory Design by Haiyong Zeng, Rui Zhang, Xu Zhu, Yufei Jiang, Zhongxiang Wei, Fu-Chun Zheng

“…With the proposed scheme, the interference links between TBS and AAV-served users are enabled to carry useful information, therefore, an enhanced degree of freedom is achieved, leading to a much higher sum-rate over the non-coordinated AAV-assisted NOMA systems where the interference of AAV-served users from TBS is extensively suppressed. Moreover, joint optimization of user scheduling, power allocation and AAV three-dimensional (3D) trajectory design is conducted to maximize the sum-rate of edge users while maintaining a high quality of service at cell-center users, with the consideration of imperfect channel estimation: a) A user scheduling principle dedicated for AAV-TBS coordinated NOMA systems is presented, based on which a two-step user scheduling and power allocation (USPA) algorithm is proposed, with the derivation of optimal power allocation solution; b) A joint USPA algorithm is proposed with closed-form results; c) Considering the line of sight (LoS) and non-LoS factors in the air to ground channel, the 3D trajectory of AAV is designed based on successive convex approximation. …”
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Design of FPGA-Based Accelerator for Convolutional Neural Network under Heterogeneous Computing Framework with OpenCL by Li Luo, Yakun Wu, Fei Qiao, Yi Yang, Qi Wei, Xiaobo Zhou, Yongkai Fan, Shuzheng Xu, Xinjun Liu, Huazhong Yang

“…Among these, FPGA can accelerate the computation by mapping the algorithm to the parallel hardware instead of CPU, which cannot fully exploit the parallelism. …”
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